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https://github.com/bagmk/Quantum_Machine_Learning_Express
|
bagmk
|
import numpy as np
from qiskit.quantum_info import state_fidelity, Statevector
def getStatevector(circuit):
return Statevector(circuit).data
import warnings
warnings.filterwarnings('ignore')
def P_haar(N, F):
if F == 1:
return 0
else:
return (N - 1) * ((1 - F) ** (N - 2))
def KL(P, Q):
epsilon = 1e-8
kl_divergence = 0.0
for p, q in zip(P, Q):
kl_divergence += p * np.log( (p + epsilon) / (q + epsilon) )
return abs(kl_divergence)
def expressibility(qubits, sampler, *, bins=100, epoch=3000, layer=1, encode=False, return_detail=False):
unit = 1 / bins
limits = []
probabilities = np.array([0] * bins)
for i in range(1, bins + 1):
limits.append(unit * i)
for i in range(epoch):
circuit_1 = sampler(layer=layer, qubits=qubits)
circuit_2 = sampler(layer=layer, qubits=qubits)
f = state_fidelity(
getStatevector(circuit_1),
getStatevector(circuit_2)
)
for j in range(bins):
if f <= limits[j]:
probabilities[j] += 1
break
pHaar_vqc = [ P_haar(2 ** qubits, f - (unit/2)) / bins for f in limits]
probabilities = [ p / epoch for p in probabilities ]
if return_detail:
return pHaar_vqc, probabilities
else:
return KL(probabilities, pHaar_vqc)
|
https://github.com/bagmk/Quantum_Machine_Learning_Express
|
bagmk
|
from matplotlib import pyplot
from qiskit import *
from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit
from qiskit import IBMQ, Aer, execute,assemble,QuantumCircuit, aqua
from qiskit.visualization import plot_histogram, plot_bloch_vector, plot_bloch_multivector
from qiskit.quantum_info import Statevector
from qiskit.extensions import *
provider = IBMQ.load_account()
from qiskit.quantum_info import random_unitary
from qiskit.providers.aer import QasmSimulator, StatevectorSimulator, UnitarySimulator
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import math
from math import pi, sqrt
from scipy.special import rel_entr
from random import seed
from random import random
import cmath
#Possible Bin
bins_list=[];
for i in range(76):
bins_list.append((i)/75)
#Center of the Bean
bins_x=[]
for i in range(75):
bins_x.append(bins_list[1]+bins_list[i])
def P_harr(l,u,N):
return (1-l)**(N-1)-(1-u)**(N-1)
#Harr historgram
P_harr_hist=[]
for i in range(75):
P_harr_hist.append(P_harr(bins_list[i],bins_list[i+1],2))
#Imaginary
j=(-1)**(1/2)
backend = Aer.get_backend('qasm_simulator')
for x in range(1,20,1):
print()
arr = []
for nsh in range(1,20,1):
arr.append([])
for lp in range(1,10,1):
nshot=int(round(1000*(nsh)**1.5,0))
nparam=1000*lp
fidelity=[]
for x in range(nparam):
qr = QuantumRegister(1)
cr = ClassicalRegister(1)
qc = QuantumCircuit(qr, cr)
u13=UnitaryGate(random_unitary(2))
qc.append(u13, [qr[0]] )
u13=UnitaryGate(random_unitary(2))
qc.append(u13, [qr[0]] )
qc.measure(qr[0],cr[0])
job = execute(qc, backend, shots=nshot)
result = job.result()
count =result.get_counts()
if '0' in count and '1' in count:
ratio=count['0']/nshot
elif '0' in count and '1' not in count:
ratio=count['0']/nshot
else:
ratio=0
fidelity.append(ratio)
#Kullback Leibler divergence
weights = np.ones_like(fidelity)/float(len(fidelity))
P_U_hist=np.histogram(fidelity, bins=bins_list, weights=weights, range=[0, 1])[0];
#Kullback Leibler divergence
print(nshot,'shots per simulation',nparam,'distribution size')
arr[nsh-1].append(sum(rel_entr(P_U_hist, P_harr_hist)))
import sys
import numpy
qasm=arr
numpy.set_printoptions(threshold=sys.maxsize)
np.savetxt("KLDivg_qasm.txt",qasm,fmt='%.2f')
backend = Aer.get_backend('statevector_simulator')
for x in range(1,20,1):
print()
arr = []
for nsh in range(1,20,1):
arr.append([])
for lp in range(1,10,1):
nshot=int(round(1000*(nsh)**1.5,0))
nparam=1000*lp
fidelity=[]
for x in range(nparam):
qr = QuantumRegister(1)
cr = ClassicalRegister(1)
qc = QuantumCircuit(qr, cr)
u13=UnitaryGate(random_unitary(2))
qc.append(u13, [qr[0]] )
u13=UnitaryGate(random_unitary(2))
qc.append(u13, [qr[0]] )
qc.measure(qr[0],cr[0])
job = execute(qc, backend, shots=nshot)
result = job.result()
count =result.get_counts()
if '0' in count and '1' in count:
ratio=count['0']/nshot
elif '0' in count and '1' not in count:
ratio=count['0']/nshot
else:
ratio=0
fidelity.append(ratio)
#Kullback Leibler divergence
weights = np.ones_like(fidelity)/float(len(fidelity))
P_U_hist=np.histogram(fidelity, bins=bins_list, weights=weights, range=[0, 1])[0];
#Kullback Leibler divergence
print(nshot,'shots per simulation',nparam,'distribution size')
arr[nsh-1].append(sum(rel_entr(P_U_hist, P_harr_hist)))
import sys
import numpy
statevector=arr
numpy.set_printoptions(threshold=sys.maxsize)
np.savetxt("KLDivg_statevector.txt",statevector,fmt='%.2f')
##Plot
import pandas as pd
import matplotlib.pyplot as plt
#loading dataset
x = [i+1 for i in range(19)]
y = [i[0] for i in arr]
plt.plot(x, y, 'o', color='red', label='L=1');
x = [i+1 for i in range(19)]
y = [i[1] for i in arr]
plt.plot(x, y, 'o', color='blue', label='L=2');
x = [i+1 for i in range(19)]
y = [i[2] for i in arr]
plt.plot(x, y, 'o', color='black', label='L=3');
x = [i+1 for i in range(19)]
y = [i[3] for i in arr]
plt.plot(x, y, 'o', color='green', label='L=4');
x = [i+1 for i in range(19)]
y = [i[3] for i in arr]
plt.plot(x, y, 'o', color='purple', label='L=5');
plt.legend(loc='upper right')
plt.yscale('log',base=10)
plt.xlabel('Circuit ID')
plt.ylabel('Expressibility')
# Create names on the x axis
plt.xticks([i+1 for i in range(19)])
plt.show()
|
https://github.com/bagmk/Quantum_Machine_Learning_Express
|
bagmk
|
from matplotlib import pyplot
from qiskit import *
from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit
from qiskit import IBMQ, Aer, execute,assemble,QuantumCircuit, aqua
from qiskit.visualization import plot_histogram, plot_bloch_vector, plot_bloch_multivector
from qiskit.quantum_info import Statevector
from qiskit.extensions import *
provider = IBMQ.load_account()
from qiskit.quantum_info import random_unitary
from qiskit.providers.aer import QasmSimulator, StatevectorSimulator, UnitarySimulator
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import math
from math import pi, sqrt
from scipy.special import rel_entr
from random import seed
from random import random
import cmath
#Possible Bin
bins_list=[];
for i in range(76):
bins_list.append((i)/75)
#Center of the Bean
bins_x=[]
for i in range(75):
bins_x.append(bins_list[1]+bins_list[i])
def P_harr(l,u,N):
return (1-l)**(N-1)-(1-u)**(N-1)
#Harr historgram
P_harr_hist=[]
for i in range(75):
P_harr_hist.append(P_harr(bins_list[i],bins_list[i+1],2))
#Imaginary
j=(-1)**(1/2)
backend = Aer.get_backend('qasm_simulator')
for x in range(1,20,1):
print()
arr = []
for nsh in range(1,20,1):
arr.append([])
for lp in range(1,10,1):
nshot=int(round(1000*(nsh)**1.5,0))
nparam=1000*lp
fidelity=[]
for x in range(nparam):
qr = QuantumRegister(1)
cr = ClassicalRegister(1)
qc = QuantumCircuit(qr, cr)
u13=UnitaryGate(random_unitary(2))
qc.append(u13, [qr[0]] )
u13=UnitaryGate(random_unitary(2))
qc.append(u13, [qr[0]] )
qc.measure(qr[0],cr[0])
job = execute(qc, backend, shots=nshot)
result = job.result()
count =result.get_counts()
if '0' in count and '1' in count:
ratio=count['0']/nshot
elif '0' in count and '1' not in count:
ratio=count['0']/nshot
else:
ratio=0
fidelity.append(ratio)
#Kullback Leibler divergence
weights = np.ones_like(fidelity)/float(len(fidelity))
P_U_hist=np.histogram(fidelity, bins=bins_list, weights=weights, range=[0, 1])[0];
#Kullback Leibler divergence
print(nshot,'shots per simulation',nparam,'distribution size')
arr[nsh-1].append(sum(rel_entr(P_U_hist, P_harr_hist)))
import sys
import numpy
numpy.set_printoptions(threshold=sys.maxsize)
np.savetxt("KLDivg_qasm.txt",qasm)
qasm
backend = QasmSimulator(method='density_matrix')
for x in range(1,20,1):
print()
arr = []
for nsh in range(1,20,1):
arr.append([])
for lp in range(1,10,1):
nshot=int(round(1000*(nsh)**1.5,0))
nparam=1000*lp
fidelity=[]
for x in range(nparam):
qr = QuantumRegister(1)
cr = ClassicalRegister(1)
qc = QuantumCircuit(qr, cr)
u13=UnitaryGate(random_unitary(2))
qc.append(u13, [qr[0]] )
u13=UnitaryGate(random_unitary(2))
qc.append(u13, [qr[0]] )
qc.measure(qr[0],cr[0])
job = execute(qc, backend, shots=nshot)
result = job.result()
count =result.get_counts()
if '0' in count and '1' in count:
ratio=count['0']/nshot
elif '0' in count and '1' not in count:
ratio=count['0']/nshot
else:
ratio=0
fidelity.append(ratio)
#Kullback Leibler divergence
weights = np.ones_like(fidelity)/float(len(fidelity))
P_U_hist=np.histogram(fidelity, bins=bins_list, weights=weights, range=[0, 1])[0];
#Kullback Leibler divergence
arr[nsh-1].append(sum(rel_entr(P_U_hist, P_harr_hist)))
print(nshot,'shots per simulation',nparam,'distribution size KL=',sum(rel_entr(P_U_hist, P_harr_hist)))
import sys
import numpy
density_mat=arr
numpy.set_printoptions(threshold=sys.maxsize)
np.savetxt("KLDivg_density_mat.txt",density_mat)
density_mat
backend = QasmSimulator(method='statevector')
for x in range(1,20,1):
print()
arr = []
for nsh in range(1,20,1):
arr.append([])
for lp in range(1,10,1):
nshot=int(round(1000*(nsh)**1.5,0))
nparam=1000*lp
fidelity=[]
for x in range(nparam):
qr = QuantumRegister(1)
cr = ClassicalRegister(1)
qc = QuantumCircuit(qr, cr)
u13=UnitaryGate(random_unitary(2))
qc.append(u13, [qr[0]] )
u13=UnitaryGate(random_unitary(2))
qc.append(u13, [qr[0]] )
qc.measure(qr[0],cr[0])
job = execute(qc, backend, shots=nshot)
result = job.result()
count =result.get_counts()
if '0' in count and '1' in count:
ratio=count['0']/nshot
elif '0' in count and '1' not in count:
ratio=count['0']/nshot
else:
ratio=0
fidelity.append(ratio)
#Kullback Leibler divergence
weights = np.ones_like(fidelity)/float(len(fidelity))
P_U_hist=np.histogram(fidelity, bins=bins_list, weights=weights, range=[0, 1])[0];
#Kullback Leibler divergence
arr[nsh-1].append(sum(rel_entr(P_U_hist, P_harr_hist)))
print(nshot,'shots per simulation',nparam,'distribution size KL=',sum(rel_entr(P_U_hist, P_harr_hist)))
import sys
import numpy
statevector1=arr
numpy.set_printoptions(threshold=sys.maxsize)
np.savetxt("KLDivg_statevector.txt",statevector1)
statevector1
backend = QasmSimulator(method='matrix_product_state')
for x in range(1,20,1):
print()
arr = []
for nsh in range(1,20,1):
arr.append([])
for lp in range(1,10,1):
nshot=int(round(1000*(nsh)**1.5,0))
nparam=1000*lp
fidelity=[]
for x in range(nparam):
qr = QuantumRegister(1)
cr = ClassicalRegister(1)
qc = QuantumCircuit(qr, cr)
u13=UnitaryGate(random_unitary(2))
qc.append(u13, [qr[0]] )
u13=UnitaryGate(random_unitary(2))
qc.append(u13, [qr[0]] )
qc.measure(qr[0],cr[0])
job = execute(qc, backend, shots=nshot)
result = job.result()
count =result.get_counts()
if '0' in count and '1' in count:
ratio=count['0']/nshot
elif '0' in count and '1' not in count:
ratio=count['0']/nshot
else:
ratio=0
fidelity.append(ratio)
#Kullback Leibler divergence
weights = np.ones_like(fidelity)/float(len(fidelity))
P_U_hist=np.histogram(fidelity, bins=bins_list, weights=weights, range=[0, 1])[0];
#Kullback Leibler divergence
arr[nsh-1].append(sum(rel_entr(P_U_hist, P_harr_hist)))
print(nshot,'shots per simulation',nparam,'distribution size KL=',sum(rel_entr(P_U_hist, P_harr_hist)))
import sys
import numpy
matrix_product1=arr
numpy.set_printoptions(threshold=sys.maxsize)
np.savetxt("KLDivg_matrix_product1.txt",matrix_product1)
matrix_product1
def plotdata(i,data):
klll=np.transpose(data)
x=[];
y=[];
for nsh in range(0,9,1):
x.append(int(round(1000*(nsh)**1.5,0)))
y.append(klll[nsh][i])
return [x,y]
#loading dataset
fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(20, 5))
datalist=[density_mat,statevector1,matrix_product1,qasm]
datalistn=['density_mat','statevector','matrix_product','qasm']
indx=0;
for indx in range(4):
data=datalist[indx];
axes[indx].plot(plotdata(0,data)[0],plotdata(0,data)[1], color='red', label='npram=1000');
axes[indx].plot(plotdata(1,data)[0],plotdata(1,data)[1], color='blue', label='npram=2000');
axes[indx].plot(plotdata(2,data)[0],plotdata(2,data)[1], color='black', label='npram=3000');
axes[indx].plot(plotdata(3,data)[0],plotdata(3,data)[1], color='green', label='npram=4000');
axes[indx].plot(plotdata(4,data)[0],plotdata(4,data)[1], color='purple', label='npram=5000');
axes[indx].plot(plotdata(5,data)[0],plotdata(5,data)[1], color='gray', label='npram=6000');
axes[indx].plot(plotdata(6,data)[0],plotdata(6,data)[1], color='black', label='npram=7000');
axes[indx].plot(plotdata(7,data)[0],plotdata(7,data)[1], color='yellow', label='npram=8000');
axes[indx].plot(plotdata(8,data)[0],plotdata(8,data)[1], color='pink', label='npram=9000');
axes[indx].set_ylim([0.003, 0.05])
axes[indx].legend(loc='upper right')
axes[indx].set_title(datalistn[indx])
axes[indx].set_yscale('log',base=10)
axes[indx].set_ylabel('Expressibility')
from matplotlib import pyplot as plt
fig.savefig('ExpressibilitybySimulator.png')
fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(20, 5))
indx=0
for indx in range(4):
data=density_mat;
axes[indx].plot(plotdata(indx,data)[0],plotdata(indx,data)[1], color='red', label='density_mat');
data=statevector1;
axes[indx].plot(plotdata(indx,data)[0],plotdata(indx,data)[1], color='blue', label='statevector');
data=matrix_product1;
axes[indx].plot(plotdata(indx,data)[0],plotdata(indx,data)[1], color='green', label='matrix_product1');
data=qasm;
axes[indx].plot(plotdata(indx,data)[0],plotdata(indx,data)[1], color='pink', label='qasm');
axes[indx].set_title('nparam='+str((indx+1)*1000))
axes[indx].set_yscale('log',base=10)
axes[indx].set_ylim([0.003, 0.05])
axes[indx].set_ylabel('Expressibility')
axes[indx].legend(['density_mat','statevector','matrix_product1','qasm'])
axes[indx].set_xlabel('iteration')
fig.savefig('ExpressibilitybySimulatornparam1.png')
fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(20, 5))
indx=0
for indx1 in range(4,8,1):
indx=indx1-4
data=density_mat;
axes[indx].plot(plotdata(indx1,data)[0],plotdata(indx1,data)[1], color='red', label='density_mat');
data=statevector1;
axes[indx].plot(plotdata(indx1,data)[0],plotdata(indx1,data)[1], color='blue', label='statevector');
data=matrix_product1;
axes[indx].plot(plotdata(indx1,data)[0],plotdata(indx1,data)[1], color='green', label='matrix_product1');
data=qasm;
axes[indx].plot(plotdata(indx1,data)[0],plotdata(indx1,data)[1], color='pink', label='qasm');
axes[indx].set_title('nparam='+str((indx1+1)*1000))
axes[indx].set_yscale('log',base=10)
axes[indx].set_ylabel('Expressibility')
axes[indx].set_ylim([0.003, 0.05])
axes[indx].legend(['density_mat','statevector','matrix_product1','qasm'])
axes[indx].set_xlabel('iteration')
fig.savefig('ExpressibilitybySimulatornparam2.png')
|
https://github.com/bagmk/Quantum_Machine_Learning_Express
|
bagmk
|
import numpy as np
from sklearn.utils import shuffle
import matplotlib.pyplot as plt
data1Path = r'../../dataset/data0.txt'
data1Label = r'../../dataset/data0label.txt'
dataCoords = np.loadtxt(data1Path)
dataLabels = np.loadtxt(data1Label)
# Make a data structure which is easier to work with
# for shuffling.
# Also, notice we change the data labels from {0, 1} to {-1, +1}
data = list(zip(dataCoords, 2*dataLabels-1))
shuffled_data = shuffle(data)
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 0)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 0)])[1::2], ls='', marker='o')
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 1)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 1)])[1::2], ls='', marker='o')
from IPython.display import Image
import sys
sys.path.append('../../Pyfiles')
from circuits import *
from qiskit.circuit import Parameter
#circuit 1
qr = QuantumRegister(4)
qc = QuantumCircuit(qr)
theta=[]
for i in range(62):
theta.append((Parameter('θ'+str(i))))
qc=circuit1(qc,qr,theta,1,0)
#load the simulation functcions from the quantumcircuit.py file
from quantumcircuit import *
#location,label,param,[circuit#,layer]
loss_qubitF([0.5,0.5],-1,[0,0,0,-5,0,0,0,0],[0,1])
#load the SPSA optimizer from the optimizer.py file
from optimizer import *
c = 0.5
a = 0.5
# Do the updates
lossList = []
coeffsList = []
paramsList = []
accuracyList = []
np.random.seed(2)
currentParams = pi*np.random.uniform(size=8)
for j in range(10):
cj = c/(j+1)**(1/2)
aj = a/(j+1)
# Grab a subset of the data for minibatching
#np.random.seed(j)
np.random.seed(2)
#data_ixs = np.random.choice(len(shuffled_data), size=len(shuffled_data))
data_ixs = np.random.choice(len(data), size=100)
# Evaluate the loss over that subset
# We include a regularization term at the end
L = lambda x: np.sum([loss_qubitF(data[j][0],data[j][1],x,[0,1]) for j in data_ixs])/len(data_ixs)
lossList.append(L(currentParams))
coeffsList.append((cj, aj))
paramsList.append(currentParams)
accuracyList.append(np.sum([predict_qubitF(data[j][0],currentParams,[0,1]) ==data[j][1] for j in data_ixs])/len(data_ixs))
print(j,"th iteration L=",lossList[-1],"Accuracy =",accuracyList[-1])
currentParams = SPSA_update(L, currentParams, aj, cj)
fig = plt.figure(figsize=(15, 10))
ax = fig.add_subplot(2, 2, 1)
ax.plot(lossList)
ax.set_title('Loss function\nStart {0} Finish {1}'.format(np.round(lossList[0], 3), np.round(lossList[-1], 3)))
ax.set_yscale('log')
ax = fig.add_subplot(2, 2, 2)
ax.plot(accuracyList)
ax.set_title('Classification accuracy \nStart {0} Finish {1}'.format(np.round(accuracyList[0], 3), np.round(accuracyList[-1], 3)))
ax.set_yscale('log')
ax = fig.add_subplot(2, 2, 3)
ax.plot([c[0] for c in coeffsList], label='a')
ax.plot([c[1] for c in coeffsList], label='c')
ax.legend(loc=0)
ax.set_title('Update coefficients')
ax = fig.add_subplot(2, 2, 4)
for j in range(4):
ax.plot([X[j] for X in paramsList], label='x{0}'.format(j))
ax.legend(loc=0)
ax.set_title('Parameter values')
ax.legend(loc=0)
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 0)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 0)])[1::2], ls='', marker='o')
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 1)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 1)])[1::2], ls='', marker='o')
X = np.linspace(0, 1, num=10)
Z = np.zeros((len(X), len(X)))
# Contour map
for j in range(len(X)):
for k in range(len(X)):
# Fill Z with the labels (numerical values)
# the inner loop goes over the columns of Z,
# which corresponds to sweeping x-values
# Therefore, the role of j,k is flipped in the signature
Z[j, k] = predict_qubitF( np.array([X[k], X[j]]),currentParams,[0,1])
ax.contourf(X, X, Z, cmap='bwr', levels=30)
#circuit 2
qr = QuantumRegister(4)
qc = QuantumCircuit(qr)
theta=[]
for i in range(62):
theta.append((Parameter('θ'+str(i))))
qc=circuit3(qc,qr,theta,1,0)
data1Path = r'../../dataset/data1a.txt'
data1Label = r'../../dataset/data1alabel.txt'
dataCoords = np.loadtxt(data1Path)
dataLabels = np.loadtxt(data1Label)
# Make a data structure which is easier to work with
# for shuffling.
# Also, notice we change the data labels from {0, 1} to {-1, +1}
data = list(zip(dataCoords, 2*dataLabels-1))
shuffled_data = shuffle(data)
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 0)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 0)])[1::2], ls='', marker='o')
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 1)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 1)])[1::2], ls='', marker='o')
c = 1
a = 1
# Do the updates
lossList = []
coeffsList = []
paramsList = []
accuracyList = []
np.random.seed(2)
currentParams = pi*np.random.uniform(size=12)
for j in range(10):
cj = c/(j+1)**(1/2)
aj = a/(j+1)
# Grab a subset of the data for minibatching
#np.random.seed(j)
np.random.seed(3)
#data_ixs = np.random.choice(len(shuffled_data), size=len(shuffled_data))
data_ixs = np.random.choice(len(data), size=100)
# Evaluate the loss over that subset
# We include a regularization term at the end
L = lambda x: np.sum([loss_qubitF(data[j][0],data[j][1],x,[2,1]) for j in data_ixs])/len(data_ixs)
lossList.append(L(currentParams))
coeffsList.append((cj, aj))
paramsList.append(currentParams)
accuracyList.append(np.sum([predict_qubitF(data[j][0],currentParams,[2,1]) ==data[j][1] for j in data_ixs])/len(data_ixs))
print(j,"th iteration L=",lossList[-1],"Accuracy =",accuracyList[-1])
currentParams = SPSA_update(L, currentParams, aj, cj)
fig = plt.figure(figsize=(15, 10))
ax = fig.add_subplot(2, 2, 1)
ax.plot(lossList)
ax.set_title('Loss function\nStart {0} Finish {1}'.format(np.round(lossList[0], 3), np.round(lossList[-1], 3)))
ax.set_yscale('log')
ax = fig.add_subplot(2, 2, 2)
ax.plot(accuracyList)
ax.set_title('Classification accuracy \nStart {0} Finish {1}'.format(np.round(accuracyList[0], 3), np.round(accuracyList[-1], 3)))
ax.set_yscale('log')
ax = fig.add_subplot(2, 2, 3)
ax.plot([c[0] for c in coeffsList], label='a')
ax.plot([c[1] for c in coeffsList], label='c')
ax.legend(loc=0)
ax.set_title('Update coefficients')
ax = fig.add_subplot(2, 2, 4)
for j in range(4):
ax.plot([X[j] for X in paramsList], label='x{0}'.format(j))
ax.legend(loc=0)
ax.set_title('Parameter values')
ax.legend(loc=0)
currentParams
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 0)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 0)])[1::2], ls='', marker='o')
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 1)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 1)])[1::2], ls='', marker='o')
X = np.linspace(0, 1, num=10)
Z = np.zeros((len(X), len(X)))
# Contour map
for j in range(len(X)):
for k in range(len(X)):
# Fill Z with the labels (numerical values)
# the inner loop goes over the columns of Z,
# which corresponds to sweeping x-values
# Therefore, the role of j,k is flipped in the signature
Z[j, k] = predict_qubitF( np.array([X[k], X[j]]),currentParams,[1,1])
ax.contourf(X, X, Z, cmap='bwr', levels=30)
#circuit 1
qr = QuantumRegister(4)
qc = QuantumCircuit(qr)
theta=[]
for i in range(62):
theta.append((Parameter('θ'+str(i))))
qc=circuit19(qc,qr,theta,2,0)
data1Path = r'../../dataset/data2c.txt'
data1Label = r'../../dataset/data2clabel.txt'
dataCoords = np.loadtxt(data1Path)
dataLabels = np.loadtxt(data1Label)
# Make a data structure which is easier to work with
# for shuffling.
# Also, notice we change the data labels from {0, 1} to {-1, +1}
data = list(zip(dataCoords, 2*dataLabels-1))
shuffled_data = shuffle(data)
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 0)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 0)])[1::2], ls='', marker='o')
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 1)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 1)])[1::2], ls='', marker='o')
c = 1
a = 1
# Do the updates
lossList = []
coeffsList = []
paramsList = []
accuracyList = []
np.random.seed(2)
currentParams = 2*np.random.uniform(size=40)
for j in range(30):
cj = c/(j+1)**(1/4)
aj = a/(j+1)
# Grab a subset of the data for minibatching
#np.random.seed(j)
np.random.seed(3)
#data_ixs = np.random.choice(len(shuffled_data), size=len(shuffled_data))
data_ixs = np.random.choice(len(data), size=100)
# Evaluate the loss over that subset
# We include a regularization term at the end
L = lambda x: np.sum([loss_qubitF(data[j][0],data[j][1],x,[18,2]) for j in data_ixs])/len(data_ixs)
lossList.append(L(currentParams))
coeffsList.append((cj, aj))
paramsList.append(currentParams)
accuracyList.append(np.sum([predict_qubitF(data[j][0],currentParams,[18,2]) ==data[j][1] for j in data_ixs])/len(data_ixs))
print(j,"th iteration L=",lossList[-1],"Accuracy =",accuracyList[-1])
currentParams = SPSA_update(L, currentParams, aj, cj)
fig = plt.figure(figsize=(15, 10))
ax = fig.add_subplot(2, 2, 1)
ax.plot(lossList)
ax.set_title('Loss function\nStart {0} Finish {1}'.format(np.round(lossList[0], 3), np.round(lossList[-1], 3)))
ax.set_yscale('log')
ax = fig.add_subplot(2, 2, 2)
ax.plot(accuracyList)
ax.set_title('Classification accuracy \nStart {0} Finish {1}'.format(np.round(accuracyList[0], 3), np.round(accuracyList[-1], 3)))
ax.set_yscale('log')
ax = fig.add_subplot(2, 2, 3)
ax.plot([c[0] for c in coeffsList], label='a')
ax.plot([c[1] for c in coeffsList], label='c')
ax.legend(loc=0)
ax.set_title('Update coefficients')
ax = fig.add_subplot(2, 2, 4)
for j in range(4):
ax.plot([X[j] for X in paramsList], label='x{0}'.format(j))
ax.legend(loc=0)
ax.set_title('Parameter values')
ax.legend(loc=0)
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 0)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 0)])[1::2], ls='', marker='o')
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 1)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 1)])[1::2], ls='', marker='o')
X = np.linspace(0, 1, num=10)
Z = np.zeros((len(X), len(X)))
# Contour map
for j in range(len(X)):
for k in range(len(X)):
# Fill Z with the labels (numerical values)
# the inner loop goes over the columns of Z,
# which corresponds to sweeping x-values
# Therefore, the role of j,k is flipped in the signature
Z[j, k] = predict_qubitF( np.array([X[k], X[j]]),currentParams,[18,2])
ax.contourf(X, X, Z, cmap='bwr', levels=30)
currentParams
|
https://github.com/bagmk/Quantum_Machine_Learning_Express
|
bagmk
|
The purpose of this notebook is to show how to use Qiskit's `Parameter` object to create and manipulated PQCs.
import sys
sys.path.append('../../Pyfiles')
import qiskit
qiskit.__qiskit_version__
from qiskit import QuantumCircuit, Aer
from qiskit.circuit import Parameter
import numpy as np
from sklearn.utils import shuffle
import matplotlib.pyplot as plt
%matplotlib inline
from optimizer import *
dataID = '2a'
dataPath = r'../../dataset/data{0}.txt'.format(dataID)
dataLabel = r'../../dataset/data{0}label.txt'.format(dataID)
dataCoords = np.loadtxt(dataPath)
dataLabels = np.loadtxt(dataLabel)
# Make a data structure which is easier to work with
# for shuffling.
# Also, notice we change the data labels from {0, 1} to {-1, +1}
data = list(zip(dataCoords, 2*dataLabels-1))
shuffled_data = np.array(shuffle(data), dtype='object')
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 0)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 0)])[1::2], ls='', marker='o')
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 1)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 1)])[1::2], ls='', marker='o')
shuffled_data
x0 = Parameter('x0')
x1 = Parameter('x1')
t0 = Parameter('t0')
t1 = Parameter('t1')
t2 = Parameter('t2')
t3 = Parameter('t3')
qc = QuantumCircuit(2)
qc.rx(x0, 0)
qc.rx(x1, 1)
qc.ry(np.pi/4, range(qc.width()))
qc.rz(np.pi/4, range(qc.width()))
qc.rx(t0, 0)
qc.rz(t1, 0)
qc.rx(t2, 1)
qc.rz(t3, 1)
qc.draw()
def calc_probs_from_results(counts, converter_dict):
r'''Given a Qiskit `counts` object (i.e., a dictionary containing
bitstrings and outcome probabilities), make an assignment to the
probabilities of a +/- 1 label.'''
probsPlus = 0
probsMinus = 0
for k in converter_dict.keys():
if converter_dict[k] == 1:
probsPlus += counts[k]
else:
probsMinus += counts[k]
return probsPlus, probsMinus
def label_assignment(probsPlus, probsMinus):
r'''Given the probabilities of +/- 1, declare the
label which should be assigned'''
if probsPlus > probsMinus:
label = 1
elif probsMinus > probsPlus:
label = -1
else:
# An edge case: if the probs are equal, just return a random label
label= 2*np.random.binomial(1, .5) -1
return label
def prediction_loss(label, prediction):
return np.abs(label - prediction)
def loss(params, paramNames, dataCircuits, converter_dict):
r'''Function which computes the loss.
Inputs:
params: The particular parameter values
paramNames: The name of the parameters
dataCircuits: List of partially-parameterized circuits
converter_dict: A dictionary representing how to map from bitstrings to labels
Outputs:
The loss
'''
# Bind the variables for the classifier part of the circuit
fully_bound_qcs = [d.bind_parameters(dict(zip(paramNames, params)))for d in dataCircuits]
# Run all the circuits
job = be.run(fully_bound_qcs)
# Get the counts
counts = job.result().get_counts()
# Now, calculate the probabilities of the +/- 1 outcome
probsList = [calc_probs_from_results(C, converter_dict) for C in list(counts)]
# Do the label assignment
predictions_list = [label_assignment(P[0], P[1]) for P in probsList]
# Calculate the loss on each data point
prediction_loss_list = [prediction_loss(data[j][1], predictions_list[j]) for j in range(len(predictions_list))]
# Sum it up and return
return np.sum(prediction_loss_list)
# Converter dictionary for 2-qubit circuit.
two_q_converter_dict = {'00': 1, '01': -1, '10': -1, '11': 1}
# We'll use the statevector simulator, to get the
# exact outcome probabilities.
be = Aer.get_backend('statevector_simulator')
#load the SPSA optimizer from the optimizer.py file
from optimizer import *
c = 1
a = c/5
currentParams = np.random.uniform(-np.pi, np.pi, size=4)
lossList = []
cList = []
aList = []
paramsList = []
for j in range(10):
print(j)
cj = c/(j+1)**(1/3)
aj = a/(j+1)
aList.append(aj)
cList.append(cj)
np.random.seed(j)
data_ixs = np.random.choice(len(shuffled_data), size=750)
data_set = shuffled_data[data_ixs]
# Generate a list of circuits whose parameters are partially bound,
# based on the data set.
dataCircuits = [qc.bind_parameters({x0:X[0][0], x1:X[0][1]}) for X in data_set]
# Use a lambda function to make a call to the loss function
# where the only thing which changes is the parameter values
# for the classification part of the circuit.
L = lambda x: loss(x, [t0, t1, t2, t3], dataCircuits, two_q_converter_dict)
lossList.append(L(currentParams))
# Update the parameters
currentParams = SPSA_update(L, currentParams, aj, cj)
paramsList.append(currentParams)
lossList.append(L(currentParams))
fig = plt.figure(figsize=(15, 10))
ax = fig.add_subplot(2, 2, 1)
ax.plot(lossList)
ax = fig.add_subplot(2, 2, 2)
ax.plot(aList, label='a')
ax.plot(cList, label='c')
ax.legend(loc=0)
ax.set_yscale('log')
ax = fig.add_subplot(2, 2, 4)
for j in range(len(paramsList[0])):
ax.plot([p[j] for p in paramsList], label='t{0}'.format(j))
ax.legend(loc=0)
|
https://github.com/bagmk/Quantum_Machine_Learning_Express
|
bagmk
|
import numpy as np
import matplotlib.pyplot as plt
from sklearn.utils import shuffle
%matplotlib inline
def SPSA_gradient(loss, currentParams, gradientCoefficient):
r'''Computes an estimator of the gradient using the procedure
described in the SPSA algorithm.
Inputs:
loss: The loss function
currentParams: The current value for the parameters
gradientCoefficient: The coefficient c_n, which controls how much the current parameters are
perturbed when computing the gradient
Returns:
gradient: The SPSA-based gradient of the loss function at currentParams'''
numParams = len(currentParams)
# Generate a random perturbation using the Rademacher distribution
randomPerturbation = 2*np.random.binomial(1, .5, size=numParams) - 1
gradient = (loss(currentParams + gradientCoefficient*randomPerturbation) - loss(currentParams - gradientCoefficient*randomPerturbation))\
/(gradientCoefficient*randomPerturbation)
return gradient
def SPSA_update(loss, currentParams, updateCoefficient, gradientCoefficient):
r'''Performs a parameter update according to the SPSA approach.
NOTE: This function isn't aware of the notion of iterations, or anything of that sort.
Inputs:
loss: The loss function
currentParams: The current value for the parameters
updateCoefficient: The coefficient a_n, which controls how the current parameters are updated
when including the gradient
gradientCoefficient: The coefficient c_n, which controls how much the current parameters are
perturbed when computing the gradient
Returns:
The updated parameter values'''
grad = SPSA_gradient(loss, currentParams, gradientCoefficient)
return currentParams - updateCoefficient*grad
def loss(x):
r'''Compute the loss'''
return np.linalg.norm(x - np.ones_like(x))
c = 2
a = 1
# Make some lists to keep track of things
lossList = []
coeffsList = []
paramsList = []
# Set starting values for the parameters
np.random.seed(0)
currentParams = np.random.uniform(size=4)
# Do the updates
for j in range(50):
cj = c/(j+1)**.5
aj = a/(j+1)
lossList.append(loss(currentParams))
coeffsList.append((cj, aj))
paramsList.append(currentParams)
currentParams = SPSA_update(loss, currentParams, aj, cj)
fig = plt.figure(figsize=(15, 5))
ax = fig.add_subplot(1, 3, 1)
ax.plot(lossList)
ax.set_title('Value of the Loss')
ax = fig.add_subplot(1, 3, 2)
ax.plot([X[0] for X in coeffsList], label='c')
ax.plot([X[1] for X in coeffsList], label='a')
ax.set_title('Update Coefficients')
ax.legend(loc=0)
ax = fig.add_subplot(1, 3, 3)
for j in range(4):
ax.plot([X[j] for X in paramsList], label='x{0}'.format(j))
ax.legend(loc=0)
ax.set_title('Parameter values')
data1Path = r'../../dataset/data1a.txt'
data1Label = r'../../dataset/data1alabel.txt'
dataCoords = np.loadtxt(data1Path)
dataLabels = np.loadtxt(data1Label)
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 0)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 0)])[1::2], ls='', marker='o')
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 1)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 1)])[1::2], ls='', marker='o')
def value(x, dataPoint):
r'''Returns the value assigned to a datapoint by the SVM'''
return np.dot(x[:2], dataPoint) - x[2]
def loss(x, dataPoint, dataLabel):
r'''The loss function we'll use is the hinge loss'''
return max(0, 1-dataLabel*value(x, dataPoint))
# Make a data structure which is easier to work with
# for shuffling.
# Also, notice we change the data labels from {0, 1} to {-1, +1}
data = list(zip(dataCoords, 2*dataLabels-1))
shuffled_data = shuffle(data)
c = 5
a = 1
# Make some lists to keep track of things
lossList = []
coeffsList = []
paramsList =[]
accuracyList = []
# Set starting values for the parameters
np.random.seed(2)
currentParams = np.random.uniform(size=3)
# Do the updates
for j in range(10):
cj = c/(j+1)**.5
aj = a/(j+1)
# Grab a subset of the data for minibatching
np.random.seed(j)
data_ixs = np.random.choice(len(shuffled_data), size=len(shuffled_data))
# Evaluate the loss over that subset
# We include a regularization term at the end
L = lambda x: np.sum([loss(x, shuffled_data[j][0], shuffled_data[j][1]) for j in data_ixs])/len(data_ixs) + .05*np.linalg.norm(x[:2])**2
lossList.append(L(currentParams))
coeffsList.append((cj, aj))
paramsList.append(currentParams)
accuracyList.append(np.sum([np.sign(value(currentParams,d[0])) == d[1] for d in data])/len(data))
currentParams = SPSA_update(L, currentParams, aj, cj)
fig = plt.figure(figsize=(15, 10))
ax = fig.add_subplot(2, 2, 1)
ax.plot(lossList)
ax.set_title('Loss function\nStart {0} Finish {1}'.format(np.round(lossList[0], 3), np.round(lossList[-1], 3)))
ax.set_yscale('log')
ax = fig.add_subplot(2, 2, 2)
ax.plot(accuracyList)
ax.set_title('Classification accuracy \nStart {0} Finish {1}'.format(np.round(accuracyList[0], 3), np.round(accuracyList[-1], 3)))
ax.set_yscale('log')
ax = fig.add_subplot(2, 2, 3)
ax.plot([c[0] for c in coeffsList], label='a')
ax.plot([c[1] for c in coeffsList], label='c')
ax.legend(loc=0)
ax.set_title('Update coefficients')
ax = fig.add_subplot(2, 2, 4)
ax.plot([c[0] for c in paramsList], label='x')
ax.plot([c[1] for c in paramsList], label='y')
ax.plot([c[2] for c in paramsList], label='b')
ax.set_yscale('log')
ax.set_title('Model coefficients')
ax.legend(loc=0)
# A quick evaluation of the accuracy
np.sum([np.sign(value(currentParams,d[0])) == d[1] for d in data])/len(data)
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 0)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 0)])[1::2], ls='', marker='o')
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 1)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 1)])[1::2], ls='', marker='o')
X = np.linspace(0, 1, num=100)
Z = np.zeros((len(X), len(X)))
# Contour map
for j in range(len(X)):
for k in range(len(X)):
# Fill Z with the labels (numerical values)
# the inner loop goes over the columns of Z,
# which corresponds to sweeping x-values
# Therefore, the role of j,k is flipped in the signature
Z[j, k] = value(currentParams, np.array([X[k], X[j]]))
ax.contourf(X, X, Z, cmap='bwr', levels=30)
|
https://github.com/bagmk/Quantum_Machine_Learning_Express
|
bagmk
|
import sys
sys.path.append('../../Pyfiles')
import numpy as np
from sklearn.utils import shuffle
import matplotlib.pyplot as plt
data1Path = r'../../dataset/data0test.txt'
data1Label = r'../../dataset/data0testlabel.txt'
dataCoords = np.loadtxt(data1Path)
dataLabels = np.loadtxt(data1Label)
# Make a data structure which is easier to work with
# for shuffling.
# Also, notice we change the data labels from {0, 1} to {-1, +1}
data = list(zip(dataCoords, 2*dataLabels-1))
shuffled_data = shuffle(data)
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 0)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 0)])[1::2], ls='', marker='o')
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 1)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 1)])[1::2], ls='', marker='o')
from IPython.display import Image
from circuits import *
from qiskit.circuit import Parameter
#circuit 1
qr = QuantumRegister(4)
qc = QuantumCircuit(qr)
theta=[]
for i in range(62):
theta.append((Parameter('θ'+str(i))))
qc=circuit1(qc,qr,theta,1,0)
qc.draw('mpl')
#load the simulation functcions from the quantumcircuit.py file
from quantumcircuit import *
#location,label,param,[circuit#,layer]
loss_qubitF([0.5,0.5],-1,[0,0,0,-5,0,0,0,0],[0,1])
#load the SPSA optimizer from the optimizer.py file
from optimizer import *
c = 0.5
a = 0.5
# Do the updates
lossList = []
coeffsList = []
paramsList = []
accuracyList = []
np.random.seed(2)
currentParams = pi*np.random.uniform(size=8)
for j in range(10):
cj = c/(j+1)**(1/2)
aj = a/(j+1)
# Grab a subset of the data for minibatching
#np.random.seed(j)
np.random.seed(2)
#data_ixs = np.random.choice(len(shuffled_data), size=len(shuffled_data))
data_ixs = np.random.choice(len(data), size=100)
# Evaluate the loss over that subset
# We include a regularization term at the end
L = lambda x: np.sum([loss_qubitF(data[j][0],data[j][1],x,[0,1]) for j in data_ixs])/len(data_ixs)
lossList.append(L(currentParams))
coeffsList.append((cj, aj))
paramsList.append(currentParams)
accuracyList.append(np.sum([predict_qubitF(data[j][0],currentParams,[0,1]) ==data[j][1] for j in data_ixs])/len(data_ixs))
print(j,"th iteration L=",lossList[-1],"Accuracy =",accuracyList[-1])
currentParams = SPSA_update(L, currentParams, aj, cj)
fig = plt.figure(figsize=(15, 10))
ax = fig.add_subplot(2, 2, 1)
ax.plot(lossList)
ax.set_title('Loss function\nStart {0} Finish {1}'.format(np.round(lossList[0], 3), np.round(lossList[-1], 3)))
ax.set_yscale('log')
ax = fig.add_subplot(2, 2, 2)
ax.plot(accuracyList)
ax.set_title('Classification accuracy \nStart {0} Finish {1}'.format(np.round(accuracyList[0], 3), np.round(accuracyList[-1], 3)))
ax.set_yscale('log')
ax = fig.add_subplot(2, 2, 3)
ax.plot([c[0] for c in coeffsList], label='a')
ax.plot([c[1] for c in coeffsList], label='c')
ax.legend(loc=0)
ax.set_title('Update coefficients')
ax = fig.add_subplot(2, 2, 4)
for j in range(4):
ax.plot([X[j] for X in paramsList], label='x{0}'.format(j))
ax.legend(loc=0)
ax.set_title('Parameter values')
ax.legend(loc=0)
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 0)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 0)])[1::2], ls='', marker='o')
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 1)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 1)])[1::2], ls='', marker='o')
X = np.linspace(0, 1, num=10)
Z = np.zeros((len(X), len(X)))
# Contour map
for j in range(len(X)):
for k in range(len(X)):
# Fill Z with the labels (numerical values)
# the inner loop goes over the columns of Z,
# which corresponds to sweeping x-values
# Therefore, the role of j,k is flipped in the signature
Z[j, k] = predict_qubitF( np.array([X[k], X[j]]),currentParams,[0,1])
ax.contourf(X, X, Z, cmap='bwr', levels=30)
#circuit 2
qr = QuantumRegister(4)
qc = QuantumCircuit(qr)
theta=[]
for i in range(62):
theta.append((Parameter('θ'+str(i))))
qc=circuit3(qc,qr,theta,1,0)
qc.draw('mpl')
data1Path = r'../../dataset/data1a.txt'
data1Label = r'../../dataset/data1alabel.txt'
dataCoords = np.loadtxt(data1Path)
dataLabels = np.loadtxt(data1Label)
# Make a data structure which is easier to work with
# for shuffling.
# Also, notice we change the data labels from {0, 1} to {-1, +1}
data = list(zip(dataCoords, 2*dataLabels-1))
shuffled_data = shuffle(data)
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 0)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 0)])[1::2], ls='', marker='o')
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 1)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 1)])[1::2], ls='', marker='o')
c = 1
a = 1
# Do the updates
lossList = []
coeffsList = []
paramsList = []
accuracyList = []
np.random.seed(2)
currentParams = pi*np.random.uniform(size=12)
for j in range(10):
cj = c/(j+1)**(1/2)
aj = a/(j+1)
# Grab a subset of the data for minibatching
#np.random.seed(j)
np.random.seed(3)
#data_ixs = np.random.choice(len(shuffled_data), size=len(shuffled_data))
data_ixs = np.random.choice(len(data), size=100)
# Evaluate the loss over that subset
# We include a regularization term at the end
L = lambda x: np.sum([loss_qubitF(data[j][0],data[j][1],x,[2,1]) for j in data_ixs])/len(data_ixs)
lossList.append(L(currentParams))
coeffsList.append((cj, aj))
paramsList.append(currentParams)
accuracyList.append(np.sum([predict_qubitF(data[j][0],currentParams,[2,1]) ==data[j][1] for j in data_ixs])/len(data_ixs))
print(j,"th iteration L=",lossList[-1],"Accuracy =",accuracyList[-1])
currentParams = SPSA_update(L, currentParams, aj, cj)
fig = plt.figure(figsize=(15, 10))
ax = fig.add_subplot(2, 2, 1)
ax.plot(lossList)
ax.set_title('Loss function\nStart {0} Finish {1}'.format(np.round(lossList[0], 3), np.round(lossList[-1], 3)))
ax.set_yscale('log')
ax = fig.add_subplot(2, 2, 2)
ax.plot(accuracyList)
ax.set_title('Classification accuracy \nStart {0} Finish {1}'.format(np.round(accuracyList[0], 3), np.round(accuracyList[-1], 3)))
ax.set_yscale('log')
ax = fig.add_subplot(2, 2, 3)
ax.plot([c[0] for c in coeffsList], label='a')
ax.plot([c[1] for c in coeffsList], label='c')
ax.legend(loc=0)
ax.set_title('Update coefficients')
ax = fig.add_subplot(2, 2, 4)
for j in range(4):
ax.plot([X[j] for X in paramsList], label='x{0}'.format(j))
ax.legend(loc=0)
ax.set_title('Parameter values')
ax.legend(loc=0)
currentParams
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 0)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 0)])[1::2], ls='', marker='o')
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 1)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 1)])[1::2], ls='', marker='o')
X = np.linspace(0, 1, num=10)
Z = np.zeros((len(X), len(X)))
# Contour map
for j in range(len(X)):
for k in range(len(X)):
# Fill Z with the labels (numerical values)
# the inner loop goes over the columns of Z,
# which corresponds to sweeping x-values
# Therefore, the role of j,k is flipped in the signature
Z[j, k] = predict_qubitF( np.array([X[k], X[j]]),currentParams,[1,1])
ax.contourf(X, X, Z, cmap='bwr', levels=30)
#circuit 1
qr = QuantumRegister(4)
qc = QuantumCircuit(qr)
theta=[]
for i in range(62):
theta.append((Parameter('θ'+str(i))))
qc=circuit19(qc,qr,theta,2,0)
qc.draw('mpl')
data1Path = r'../../dataset/data2c.txt'
data1Label = r'../../dataset/data2clabel.txt'
dataCoords = np.loadtxt(data1Path)
dataLabels = np.loadtxt(data1Label)
# Make a data structure which is easier to work with
# for shuffling.
# Also, notice we change the data labels from {0, 1} to {-1, +1}
data = list(zip(dataCoords, 2*dataLabels-1))
shuffled_data = shuffle(data)
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 0)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 0)])[1::2], ls='', marker='o')
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 1)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 1)])[1::2], ls='', marker='o')
c = 1
a = 1
# Do the updates
lossList = []
coeffsList = []
paramsList = []
accuracyList = []
np.random.seed(2)
currentParams = 2*np.random.uniform(size=40)
for j in range(30):
cj = c/(j+1)**(1/4)
aj = a/(j+1)
# Grab a subset of the data for minibatching
#np.random.seed(j)
np.random.seed(3)
#data_ixs = np.random.choice(len(shuffled_data), size=len(shuffled_data))
data_ixs = np.random.choice(len(data), size=100)
# Evaluate the loss over that subset
# We include a regularization term at the end
L = lambda x: np.sum([loss_qubitF(data[j][0],data[j][1],x,[18,2]) for j in data_ixs])/len(data_ixs)
lossList.append(L(currentParams))
coeffsList.append((cj, aj))
paramsList.append(currentParams)
accuracyList.append(np.sum([predict_qubitF(data[j][0],currentParams,[18,2]) ==data[j][1] for j in data_ixs])/len(data_ixs))
print(j,"th iteration L=",lossList[-1],"Accuracy =",accuracyList[-1])
currentParams = SPSA_update(L, currentParams, aj, cj)
fig = plt.figure(figsize=(15, 10))
ax = fig.add_subplot(2, 2, 1)
ax.plot(lossList)
ax.set_title('Loss function\nStart {0} Finish {1}'.format(np.round(lossList[0], 3), np.round(lossList[-1], 3)))
ax.set_yscale('log')
ax = fig.add_subplot(2, 2, 2)
ax.plot(accuracyList)
ax.set_title('Classification accuracy \nStart {0} Finish {1}'.format(np.round(accuracyList[0], 3), np.round(accuracyList[-1], 3)))
ax.set_yscale('log')
ax = fig.add_subplot(2, 2, 3)
ax.plot([c[0] for c in coeffsList], label='a')
ax.plot([c[1] for c in coeffsList], label='c')
ax.legend(loc=0)
ax.set_title('Update coefficients')
ax = fig.add_subplot(2, 2, 4)
for j in range(4):
ax.plot([X[j] for X in paramsList], label='x{0}'.format(j))
ax.legend(loc=0)
ax.set_title('Parameter values')
ax.legend(loc=0)
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 0)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 0)])[1::2], ls='', marker='o')
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 1)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 1)])[1::2], ls='', marker='o')
X = np.linspace(0, 1, num=10)
Z = np.zeros((len(X), len(X)))
# Contour map
for j in range(len(X)):
for k in range(len(X)):
# Fill Z with the labels (numerical values)
# the inner loop goes over the columns of Z,
# which corresponds to sweeping x-values
# Therefore, the role of j,k is flipped in the signature
Z[j, k] = predict_qubitF( np.array([X[k], X[j]]),currentParams,[18,2])
ax.contourf(X, X, Z, cmap='bwr', levels=30)
currentParams
|
https://github.com/bagmk/Quantum_Machine_Learning_Express
|
bagmk
|
# The code below is a hack in case Travis' kernel fails.
#import os
#os.environ['KMP_DUPLICATE_LIB_OK'] ='True'
import sys
sys.path.append('../../Pyfiles')
# Pull in the helper files.
from experiments import *
# Set up the experiment
circuitID = 1
epochs = 20
import numpy as np
import sys
np.set_printoptions(threshold=sys.maxsize)
lr_list=[round(((i+1)/10)**2,2) for i in range(20)]
ds_list=['0','1a','2a','3c']
for dsID in ds_list:
# Run the experiment
print('--------dataset',dsID,'is initialized------')
lr_acc=[]
for lr in lr_list:
# Load in the data
data = load_data(dsID)
# Generate the splittings
train_X, train_y, validate_X, validate_y, test_X, test_y = generate_train_validate_test_data(data)
# Make the feature map
feature_map= make_embedding_circuit()
# Make the classifier
ansatz = make_classifer_circuit(circuitID)
# Do the training
model = train_model(feature_map, ansatz, epochs, lr, train_X, train_y)
# Check the validation accuracy.
val_accuracy = check_accuracy(model, validate_X, validate_y)
lr_acc.append([lr,val_accuracy])
np.savetxt(r"Learning_Rate_Data\circuit{0}_data{1}.txt".format(circuitID,dsID),lr_acc,fmt='%.2f')
# Set up the experiment
circuitID = 1
epochs = 20
ds_list=['0','1a','2a','3c']
#selected learning rates
lr_opt=[3.24,2.89,0.36,3.24]
model_list=[]
for i in range(4):
data = load_data(ds_list[i])
# Generate the splittings
train_X, train_y, validate_X, validate_y, test_X, test_y = generate_train_validate_test_data(data)
# Make the feature map
feature_map= make_embedding_circuit()
# Make the classifier
ansatz = make_classifer_circuit(circuitID)
# Do the training
model = train_model(feature_map, ansatz, epochs, lr_opt[i], train_X, train_y)
model_list.append(model)
# Check the validation accuracy.
val_accuracy = check_accuracy(model, test_X, test_y)
import matplotlib.pyplot as plt
for lr in range(4):
data = load_data(ds_list[lr])
# Generate the splittings
train_X, train_y, validate_X, validate_y, test_X, test_y = generate_train_validate_test_data(data)
fig = plt.figure(figsize=(15, 10))
ax = fig.add_subplot(4, 2, 2*lr+1)
y_predict = []
for x in test_X:
output = model_list[lr](Tensor(x))
y_predict += [np.argmax(output.detach().numpy())]
print(check_accuracy(model_list[lr], test_X, test_y))
# plot results
# red == wrongly classified
for x, y_target, y_ in zip(test_X, test_y, y_predict):
if y_target == 1:
ax.plot(x[0], x[1], 'bo')
else:
ax.plot(x[0], x[1], 'go')
if y_target != y_:
ax.scatter(x[0], x[1], s=200, facecolors='none', edgecolors='r', linewidths=2)
ax = fig.add_subplot(4, 2, 2*lr+2)
for x, y_target, y_ in zip(test_X, test_y, y_predict):
if y_target == 1:
ax.plot(x[0], x[1], 'bo')
else:
ax.plot(x[0], x[1], 'go')
X1 = np.linspace(0, 1, num=10)
Z1 = np.zeros((len(X1), len(X1)))
# Contour map
for j in range(len(X1)):
for k in range(len(X1)):
# Fill Z with the labels (numerical values)
# the inner loop goes over the columns of Z,
# which corresponds to sweeping x-values
# Therefore, the role of j,k is flipped in the signature
Z1[j, k] = np.argmax(model_list[lr](Tensor([X1[k],X1[j],X1[k],X1[j]])).detach().numpy())
ax.contourf(X1, X1, Z1, cmap='bwr', levels=30)
|
https://github.com/bagmk/Quantum_Machine_Learning_Express
|
bagmk
|
# The code below is a hack in case Travis' kernel fails.
#import os
#os.environ['KMP_DUPLICATE_LIB_OK'] ='True'
# Pull in the helper files.
import sys
sys.path.append('../../Pyfiles')
# Pull in the helper files.
from experiments import *
# Set up the experiment
circuitID = 14
epochs = 20
import numpy as np
import sys
np.set_printoptions(threshold=sys.maxsize)
lr_list=[round(((i+1)/10)**2,2) for i in range(20)]
ds_list=['0','1a','2a','3c']
for dsID in ds_list:
# Run the experiment
print('--------dataset',dsID,'is initialized------')
lr_acc=[]
for lr in lr_list:
# Load in the data
data = load_data(dsID)
# Generate the splittings
train_X, train_y, validate_X, validate_y, test_X, test_y = generate_train_validate_test_data(data)
# Make the feature map
feature_map= make_embedding_circuit()
# Make the classifier
ansatz = make_classifer_circuit(circuitID)
# Do the training
model = train_model(feature_map, ansatz, epochs, lr, train_X, train_y)
# Check the validation accuracy.
val_accuracy = check_accuracy(model, validate_X, validate_y)
lr_acc.append([lr,val_accuracy])
np.savetxt(r"Learning_Rate_Data\circuit{0}_data{1}.txt".format(circuitID,dsID),lr_acc,fmt='%.2f')
# Set up the experiment
circuitID = 14
epochs = 20
ds_list=['0','1a','2a','3c']
#selected learning rates
lr_opt=[0.01,3.61,0.04,0.25]
model_list=[]
for i in range(4):
data = load_data(ds_list[i])
# Generate the splittings
train_X, train_y, validate_X, validate_y, test_X, test_y = generate_train_validate_test_data(data)
# Make the feature map
feature_map= make_embedding_circuit()
# Make the classifier
ansatz = make_classifer_circuit(circuitID)
# Do the training
model = train_model(feature_map, ansatz, epochs, lr_opt[i], train_X, train_y)
model_list.append(model)
# Check the validation accuracy.
val_accuracy = check_accuracy(model, test_X, test_y)
import matplotlib.pyplot as plt
for lr in range(4):
data = load_data(ds_list[lr])
# Generate the splittings
train_X, train_y, validate_X, validate_y, test_X, test_y = generate_train_validate_test_data(data)
fig = plt.figure(figsize=(15, 10))
ax = fig.add_subplot(4, 2, 2*lr+1)
y_predict = []
for x in test_X:
output = model_list[lr](Tensor(x))
y_predict += [np.argmax(output.detach().numpy())]
print(check_accuracy(model_list[lr], test_X, test_y))
# plot results
# red == wrongly classified
for x, y_target, y_ in zip(test_X, test_y, y_predict):
if y_target == 1:
ax.plot(x[0], x[1], 'bo')
else:
ax.plot(x[0], x[1], 'go')
if y_target != y_:
ax.scatter(x[0], x[1], s=200, facecolors='none', edgecolors='r', linewidths=2)
ax = fig.add_subplot(4, 2, 2*lr+2)
for x, y_target, y_ in zip(test_X, test_y, y_predict):
if y_target == 1:
ax.plot(x[0], x[1], 'bo')
else:
ax.plot(x[0], x[1], 'go')
X1 = np.linspace(0, 1, num=10)
Z1 = np.zeros((len(X1), len(X1)))
# Contour map
for j in range(len(X1)):
for k in range(len(X1)):
# Fill Z with the labels (numerical values)
# the inner loop goes over the columns of Z,
# which corresponds to sweeping x-values
# Therefore, the role of j,k is flipped in the signature
Z1[j, k] = np.argmax(model_list[lr](Tensor([X1[k],X1[j],X1[k],X1[j]])).detach().numpy())
ax.contourf(X1, X1, Z1, cmap='bwr', levels=30)
|
https://github.com/bagmk/Quantum_Machine_Learning_Express
|
bagmk
|
# The code below is a hack in case Travis' kernel fails.
#import os
#os.environ['KMP_DUPLICATE_LIB_OK'] ='True'
# Pull in the helper files.
import sys
sys.path.append('../../Pyfiles')
# Pull in the helper files.
from experiments import *
# Set up the experiment
circuitID = 6
epochs = 20
import numpy as np
import sys
np.set_printoptions(threshold=sys.maxsize)
lr_list=[round(((i+1)/10)**2,2) for i in range(20)]
ds_list=['0','1a','2a','3c']
for dsID in ds_list:
# Run the experiment
print('--------dataset',dsID,'is initialized------')
lr_acc=[]
for lr in lr_list:
# Load in the data
data = load_data(dsID)
# Generate the splittings
train_X, train_y, validate_X, validate_y, test_X, test_y = generate_train_validate_test_data(data)
# Make the feature map
feature_map= make_embedding_circuit()
# Make the classifier
ansatz = make_classifer_circuit(circuitID)
# Do the training
model = train_model(feature_map, ansatz, epochs, lr, train_X, train_y)
# Check the validation accuracy.
val_accuracy = check_accuracy(model, validate_X, validate_y)
lr_acc.append([lr,val_accuracy])
np.savetxt(r"Learning_Rate_Data\circuit{0}_data{1}.txt".format(circuitID,dsID),lr_acc,fmt='%.2f')
import numpy as np
import sys
np.set_printoptions(threshold=sys.maxsize)
lr_list=[round(((i+1)/10)**2,2) for i in range(20)]
dsID='3c'
print('--------dataset',dsID,'is initialized------')
lr_acc=[]
for lr in lr_list:
# Load in the data
data = load_data(dsID)
# Generate the splittings
train_X, train_y, validate_X, validate_y, test_X, test_y = generate_train_validate_test_data(data)
# Make the feature map
feature_map= make_embedding_circuit()
# Make the classifier
ansatz = make_classifer_circuit(circuitID)
# Do the training
model = train_model(feature_map, ansatz, epochs, lr, train_X, train_y)
# Check the validation accuracy.
val_accuracy = check_accuracy(model, validate_X, validate_y)
lr_acc.append([lr,val_accuracy])
np.savetxt(r"Learning_Rate_Data\circuit{0}_data{1}.txt".format(circuitID,dsID),lr_acc,fmt='%.2f')
# Set up the experiment
circuitID = 6
epochs = 20
ds_list=['0','1a','2a','3c']
#selected learning rates
lr_opt=[0.25,0.25,0.25,0.81]
model_list=[]
for i in range(4):
data = load_data(ds_list[i])
# Generate the splittings
train_X, train_y, validate_X, validate_y, test_X, test_y = generate_train_validate_test_data(data)
# Make the feature map
feature_map= make_embedding_circuit()
# Make the classifier
ansatz = make_classifer_circuit(circuitID)
# Do the training
model = train_model(feature_map, ansatz, epochs, lr_opt[i], train_X, train_y)
model_list.append(model)
import matplotlib.pyplot as plt
for lr in range(4):
\
data = load_data(ds_list[lr])
# Generate the splittings
train_X, train_y, validate_X, validate_y, test_X, test_y = generate_train_validate_test_data(data)
fig = plt.figure(figsize=(15, 10))
ax = fig.add_subplot(4, 2, 2*lr+1)
y_predict = []
for x in test_X:
output = model_list[lr](Tensor(x))
y_predict += [np.argmax(output.detach().numpy())]
print(check_accuracy(model_list[lr], test_X, test_y))
# plot results
# red == wrongly classified
for x, y_target, y_ in zip(test_X, test_y, y_predict):
if y_target == 1:
ax.plot(x[0], x[1], 'bo')
else:
ax.plot(x[0], x[1], 'go')
if y_target != y_:
ax.scatter(x[0], x[1], s=200, facecolors='none', edgecolors='r', linewidths=2)
ax = fig.add_subplot(4, 2, 2*lr+2)
for x, y_target, y_ in zip(test_X, test_y, y_predict):
if y_target == 1:
ax.plot(x[0], x[1], 'bo')
else:
ax.plot(x[0], x[1], 'go')
X1 = np.linspace(0, 1, num=10)
Z1 = np.zeros((len(X1), len(X1)))
# Contour map
for j in range(len(X1)):
for k in range(len(X1)):
# Fill Z with the labels (numerical values)
# the inner loop goes over the columns of Z,
# which corresponds to sweeping x-values
# Therefore, the role of j,k is flipped in the signature
Z1[j, k] = np.argmax(model_list[lr](Tensor([X1[k],X1[j],X1[k],X1[j]])).detach().numpy())
ax.contourf(X1, X1, Z1, cmap='bwr', levels=30)
|
https://github.com/bagmk/Quantum_Machine_Learning_Express
|
bagmk
|
# The code below is a hack in case Travis' kernel fails.
#import os
#os.environ['KMP_DUPLICATE_LIB_OK'] ='True'
# Pull in the helper files.
import sys
sys.path.append('../../Pyfiles')
# Pull in the helper files.
from experiments import *
# Set up the experiment
circuitID = 9
epochs = 20
import numpy as np
import sys
np.set_printoptions(threshold=sys.maxsize)
lr_list=[round(((i+1)/10)**2,2) for i in range(20)]
ds_list=['0','1a','2a','3c']
for dsID in ds_list:
# Run the experiment
print('--------dataset',dsID,'is initialized------')
lr_acc=[]
for lr in lr_list:
# Load in the data
data = load_data(dsID)
# Generate the splittings
train_X, train_y, validate_X, validate_y, test_X, test_y = generate_train_validate_test_data(data)
# Make the feature map
feature_map= make_embedding_circuit()
# Make the classifier
ansatz = make_classifer_circuit(circuitID)
# Do the training
model = train_model(feature_map, ansatz, epochs, lr, train_X, train_y)
# Check the validation accuracy.
val_accuracy = check_accuracy(model, validate_X, validate_y)
lr_acc.append([lr,val_accuracy])
np.savetxt(r"Learning_Rate_Data\circuit{0}_data{1}.txt".format(circuitID,dsID),lr_acc,fmt='%.2f')
import numpy
import sys
numpy.set_printoptions(threshold=sys.maxsize)
lr_list=[round(((i+1)/10)**2,2) for i in range(20)]
# Run the experiment
lr_acc=[]
for lr in lr_list:
# Load in the data
data = load_data('0')
# Generate the splittings
train_X, train_y, validate_X, validate_y, test_X, test_y = generate_train_validate_test_data(data)
# Make the feature map
feature_map= make_embedding_circuit()
# Make the classifier
ansatz = make_classifer_circuit(circuitID)
# Do the training
model = train_model(feature_map, ansatz, epochs, lr, train_X, train_y)
# Check the validation accuracy.
val_accuracy = check_accuracy(model, validate_X, validate_y)
lr_acc.append([lr,val_accuracy])
np.savetxt(r"Learning_Rate_Data\circuit{0}_data{1}.txt".format(circuitID,dsID),lr_acc,fmt='%.2f')
# Set up the experiment
circuitID = 9
epochs = 20
ds_list=['0','1a','2a','3c']
#selected learning rates
lr_opt=[2.89,0.16,0.09,2.25]
model_list=[]
for i in range(4):
data = load_data(ds_list[i])
# Generate the splittings
train_X, train_y, validate_X, validate_y, test_X, test_y = generate_train_validate_test_data(data)
# Make the feature map
feature_map= make_embedding_circuit()
# Make the classifier
ansatz = make_classifer_circuit(circuitID)
# Do the training
model = train_model(feature_map, ansatz, epochs, lr_opt[i], train_X, train_y)
model_list.append(model)
# Check the validation accuracy.
val_accuracy = check_accuracy(model, test_X, test_y)
import matplotlib.pyplot as plt
for lr in range(4):
data = load_data(ds_list[lr])
# Generate the splittings
train_X, train_y, validate_X, validate_y, test_X, test_y = generate_train_validate_test_data(data)
fig = plt.figure(figsize=(15, 10))
ax = fig.add_subplot(4, 2, 2*lr+1)
y_predict = []
for x in test_X:
output = model_list[lr](Tensor(x))
y_predict += [np.argmax(output.detach().numpy())]
print(check_accuracy(model_list[lr], test_X, test_y))
# plot results
# red == wrongly classified
for x, y_target, y_ in zip(test_X, test_y, y_predict):
if y_target == 1:
ax.plot(x[0], x[1], 'bo')
else:
ax.plot(x[0], x[1], 'go')
if y_target != y_:
ax.scatter(x[0], x[1], s=200, facecolors='none', edgecolors='r', linewidths=2)
ax = fig.add_subplot(4, 2, 2*lr+2)
for x, y_target, y_ in zip(test_X, test_y, y_predict):
if y_target == 1:
ax.plot(x[0], x[1], 'bo')
else:
ax.plot(x[0], x[1], 'go')
X1 = np.linspace(0, 1, num=10)
Z1 = np.zeros((len(X1), len(X1)))
# Contour map
for j in range(len(X1)):
for k in range(len(X1)):
# Fill Z with the labels (numerical values)
# the inner loop goes over the columns of Z,
# which corresponds to sweeping x-values
# Therefore, the role of j,k is flipped in the signature
Z1[j, k] = np.argmax(model_list[lr](Tensor([X1[k],X1[j],X1[k],X1[j]])).detach().numpy())
ax.contourf(X1, X1, Z1, cmap='bwr', levels=30)
|
https://github.com/bagmk/Quantum_Machine_Learning_Express
|
bagmk
|
import qiskit
from qiskit import Aer, QuantumCircuit
from qiskit.utils import QuantumInstance
from qiskit.opflow import AerPauliExpectation
from qiskit.circuit import Parameter
from qiskit_machine_learning.neural_networks import CircuitQNN
from qiskit_machine_learning.connectors import TorchConnector
qi = QuantumInstance(Aer.get_backend('statevector_simulator'))
qiskit.__qiskit_version__
import numpy as np
from numpy import pi
import matplotlib.pyplot as plt
from sklearn.utils import shuffle
from pandas.core.common import flatten
import torch
from torch import Tensor
from torch.nn import Linear, CrossEntropyLoss, MSELoss
from torch.optim import LBFGS, SGD,Adam
import torch.optim as optim
torch.cuda.is_available()
# import the example dataset from the folder
# data = array([x1,y1],[x2,y2],[x3,y3],.....,[x1500,y1500])
# label = array([0,0,0,....,1,1,1])
data0Path = r'data.txt'
data0Label = r'datalabel.txt'
dataCoords = np.loadtxt(data0Path)
dataLabels = np.loadtxt(data0Label)
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 0)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 0)])[1::2], ls='', marker='o')
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 1)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 1)])[1::2], ls='', marker='o')
# Change the data structure for shuffling
# We are taking 100 data to train the model
np.random.seed(2)
data1 = list(zip(dataCoords, dataLabels))
data_ixs = np.random.choice(len(data1), size=100)
# Transform the data to work with the pytorch optimizer.
# data coordinate X is [x1,y1,x1,y1] to embed in four-qubit circuit.
X= [np.array(list(flatten([data1[j][0],data1[j][0]]))) for j in data_ixs]
y01 = [data1[j][1] for j in data_ixs]
X_ = Tensor(X)
y01_ = Tensor(y01).reshape(len(y01)).long()
n=4;
feature_map = QuantumCircuit(n, name='Embed')
feature_map.rx(Parameter('x[0]'),0)
feature_map.rx(Parameter('x[1]'),1)
feature_map.rx(Parameter('x[2]'),2)
feature_map.rx(Parameter('x[3]'),3)
for i in range(n):
feature_map.ry(pi/4,i)
for i in range(n):
feature_map.rz(pi/4,i)
param_y=[];
for i in range(8):
param_y.append((Parameter('θ'+str(i))))
ansatz = QuantumCircuit(n, name='PQC')
for i in range(n):
ansatz.ry(param_y[i],i)
for i in range(n):
ansatz.rz(param_y[i+4],i)
def binary(x):
return ('0'*(4-len('{:b}'.format(x) ))+'{:b}'.format(x))
def firsttwo(x):
return x[:2]
parity = lambda x: firsttwo(binary(x)).count('1') % 2
[parity(i) for i in range(16)]
qc = QuantumCircuit(n)
qc.append(feature_map, range(n))
qc.append(ansatz, range(n))
# Model for LBFGS
# Combining the circuit together with CircuitQNN
np.random.seed(3)
qnn2 = CircuitQNN(qc, input_params=feature_map.parameters, weight_params=ansatz.parameters,
interpret=parity, output_shape=2, quantum_instance=qi)
initial_weights = 0.1*(2*np.random.rand(qnn2.num_weights) - 1)
model2 = TorchConnector(qnn2, initial_weights)
# Model for Adam
np.random.seed(3)
qnn3 = CircuitQNN(qc, input_params=feature_map.parameters, weight_params=ansatz.parameters,
interpret=parity, output_shape=2, quantum_instance=qi)
initial_weights = 0.1*(2*np.random.rand(qnn3.num_weights) - 1)
model3 = TorchConnector(qnn3, initial_weights)
# define optimizer and loss function
optimizer = LBFGS(model2.parameters(),lr=0.01)
f_loss = CrossEntropyLoss()
# start training
model2.train() # set model to training mode
# define objective function
def closure():
optimizer.zero_grad() # initialize gradient
loss = 0.0 # initialize loss
for x, y_target in zip(X, y01): # evaluate batch loss
output = model2(Tensor(x)).reshape(1, 2) # forward pass
loss += f_loss(output, Tensor([y_target]).long())
loss.backward() # backward pass
print(loss.item()) # print loss
return loss
# run optimizer
optimizer.step(closure)
# evaluate model and compute accuracy
y_predict = []
for x in X:
output = model2(Tensor(x))
y_predict += [np.argmax(output.detach().numpy())]
print('Accuracy:', sum(y_predict == np.array(y01))/len(np.array(y01)))
# plot results
# red == wrongly classified
for x, y_target, y_ in zip(X, y01, y_predict):
if y_target == 1:
plt.plot(x[0], x[1], 'bo')
else:
plt.plot(x[0], x[1], 'go')
if y_target != y_:
plt.scatter(x[0], x[1], s=200, facecolors='none', edgecolors='r', linewidths=2)
plt.show()
for x, y_target, y_ in zip(X, y01, y_predict):
if y_target == 1:
plt.plot(x[0], x[1], 'bo')
else:
plt.plot(x[0], x[1], 'go')
X1 = np.linspace(0, 1, num=10)
Z1 = np.zeros((len(X1), len(X1)))
# Contour map
for j in range(len(X1)):
for k in range(len(X1)):
# Fill Z with the labels (numerical values)
# the inner loop goes over the columns of Z,
# which corresponds to sweeping x-values
# Therefore, the role of j,k is flipped in the signature
Z1[j, k] = np.argmax(model2(Tensor([X1[k],X1[j],X1[k],X1[j]])).detach().numpy())
plt.contourf(X1, X1, Z1, cmap='bwr', levels=30)
# Converged paramter for the PQC
for p in model2.parameters():
print(p.data)
optimizer = optim.Adam(model3.parameters(),lr=0.05)
f_loss = MSELoss(reduction='mean')
model3.train()
epochs = 10 # set number of epochs
for epoch in range(epochs):
optimizer.zero_grad()
loss = 0.0
for x, y_target in zip(X, y01):
output = model3(Tensor(x)).reshape(1, 2)
targets=Tensor([y_target]).long()
targets = targets.to(torch.float32)
loss += f_loss(output, targets)
loss.backward()
print(loss.item())
optimizer.step()
y_predict = []
for x in X:
output = model3(Tensor(x))
y_predict += [np.argmax(output.detach().numpy())]
print('Accuracy:', sum(y_predict == np.array(y01))/len(np.array(y01)))
for x, y_target, y_ in zip(X, y01, y_predict):
if y_target == 1:
plt.plot(x[0], x[1], 'bo')
else:
plt.plot(x[0], x[1], 'go')
if y_target != y_:
plt.scatter(x[0], x[1], s=200, facecolors='none', edgecolors='r', linewidths=2)
plt.show()
for x, y_target, y_ in zip(X, y01, y_predict):
if y_target == 1:
plt.plot(x[0], x[1], 'bo')
else:
plt.plot(x[0], x[1], 'go')
X1 = np.linspace(0, 1, num=10)
Z1 = np.zeros((len(X1), len(X1)))
for j in range(len(X1)):
for k in range(len(X1)):
Z1[j, k] = np.argmax(model3(Tensor([X1[k],X1[j],X1[k],X1[j]])).detach().numpy())
plt.contourf(X1, X1, Z1, cmap='bwr', levels=30)
for p in model3.parameters():
print(p.data)
|
https://github.com/bagmk/Quantum_Machine_Learning_Express
|
bagmk
|
import qiskit
from qiskit import Aer, QuantumCircuit
from qiskit.utils import QuantumInstance
from qiskit.opflow import AerPauliExpectation
from qiskit.circuit import Parameter
from qiskit_machine_learning.neural_networks import CircuitQNN
from qiskit_machine_learning.connectors import TorchConnector
qi = QuantumInstance(Aer.get_backend('statevector_simulator'))
qiskit.__qiskit_version__
import numpy as np
from numpy import pi
import matplotlib.pyplot as plt
from sklearn.utils import shuffle
from pandas.core.common import flatten
import torch
from torch import Tensor
from torch.nn import Linear, CrossEntropyLoss, MSELoss
from torch.optim import LBFGS, SGD,Adam
import torch.optim as optim
torch.cuda.is_available()
# import the example dataset from the folder
# data = array([x1,y1],[x2,y2],[x3,y3],.....,[x1500,y1500])
# label = array([0,0,0,....,1,1,1])
data0Path = r'data.txt'
data0Label = r'datalabel.txt'
dataCoords = np.loadtxt(data0Path)
dataLabels = np.loadtxt(data0Label)
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 0)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 0)])[1::2], ls='', marker='o')
ax.plot(np.ravel(dataCoords[np.where(dataLabels == 1)])[::2],
np.ravel(dataCoords[np.where(dataLabels == 1)])[1::2], ls='', marker='o')
# Change the data structure for shuffling
# We are taking 100 data to train the model
np.random.seed(2)
data1 = list(zip(dataCoords, dataLabels))
data_ixs = np.random.choice(len(data1), size=100)
# Transform the data to work with the pytorch optimizer.
# data coordinate X is [x1,y1,x1,y1] to embed in four-qubit circuit.
X= [np.array(list(flatten([data1[j][0],data1[j][0]]))) for j in data_ixs]
y01 = [data1[j][1] for j in data_ixs]
X_ = Tensor(X)
y01_ = Tensor(y01).reshape(len(y01)).long()
n=4;
feature_map = QuantumCircuit(n, name='Embed')
feature_map.rx(Parameter('x[0]'),0)
feature_map.rx(Parameter('x[1]'),1)
feature_map.rx(Parameter('x[2]'),2)
feature_map.rx(Parameter('x[3]'),3)
for i in range(n):
feature_map.ry(pi/4,i)
for i in range(n):
feature_map.rz(pi/4,i)
feature_map.draw('mpl')
param_y=[];
for i in range(8):
param_y.append((Parameter('θ'+str(i))))
ansatz = QuantumCircuit(n, name='PQC')
for i in range(n):
ansatz.ry(param_y[i],i)
for i in range(n):
ansatz.rz(param_y[i+4],i)
ansatz.draw('mpl')
def binary(x):
return ('0'*(4-len('{:b}'.format(x) ))+'{:b}'.format(x))
def firsttwo(x):
return x[:2]
parity = lambda x: firsttwo(binary(x)).count('1') % 2
[parity(i) for i in range(16)]
qc = QuantumCircuit(n)
qc.append(feature_map, range(n))
qc.append(ansatz, range(n))
qc.draw('mpl')
# Model for LBFGS
# Combining the circuit together with CircuitQNN
np.random.seed(3)
qnn2 = CircuitQNN(qc, input_params=feature_map.parameters, weight_params=ansatz.parameters,
interpret=parity, output_shape=2, quantum_instance=qi)
initial_weights = 0.1*(2*np.random.rand(qnn2.num_weights) - 1)
model2 = TorchConnector(qnn2, initial_weights)
# Model for Adam
np.random.seed(3)
qnn3 = CircuitQNN(qc, input_params=feature_map.parameters, weight_params=ansatz.parameters,
interpret=parity, output_shape=2, quantum_instance=qi)
initial_weights = 0.1*(2*np.random.rand(qnn3.num_weights) - 1)
model3 = TorchConnector(qnn3, initial_weights)
# define optimizer and loss function
optimizer = LBFGS(model2.parameters(),lr=0.01)
f_loss = CrossEntropyLoss()
# start training
model2.train() # set model to training mode
# define objective function
def closure():
optimizer.zero_grad() # initialize gradient
loss = 0.0 # initialize loss
for x, y_target in zip(X, y01): # evaluate batch loss
output = model2(Tensor(x)).reshape(1, 2) # forward pass
loss += f_loss(output, Tensor([y_target]).long())
loss.backward() # backward pass
print(loss.item()) # print loss
return loss
# run optimizer
optimizer.step(closure)
# evaluate model and compute accuracy
y_predict = []
for x in X:
output = model2(Tensor(x))
y_predict += [np.argmax(output.detach().numpy())]
print('Accuracy:', sum(y_predict == np.array(y01))/len(np.array(y01)))
# plot results
# red == wrongly classified
for x, y_target, y_ in zip(X, y01, y_predict):
if y_target == 1:
plt.plot(x[0], x[1], 'bo')
else:
plt.plot(x[0], x[1], 'go')
if y_target != y_:
plt.scatter(x[0], x[1], s=200, facecolors='none', edgecolors='r', linewidths=2)
plt.show()
for x, y_target, y_ in zip(X, y01, y_predict):
if y_target == 1:
plt.plot(x[0], x[1], 'bo')
else:
plt.plot(x[0], x[1], 'go')
X1 = np.linspace(0, 1, num=10)
Z1 = np.zeros((len(X1), len(X1)))
# Contour map
for j in range(len(X1)):
for k in range(len(X1)):
# Fill Z with the labels (numerical values)
# the inner loop goes over the columns of Z,
# which corresponds to sweeping x-values
# Therefore, the role of j,k is flipped in the signature
Z1[j, k] = np.argmax(model2(Tensor([X1[k],X1[j],X1[k],X1[j]])).detach().numpy())
plt.contourf(X1, X1, Z1, cmap='bwr', levels=30)
# Converged paramter for the PQC
for p in model2.parameters():
print(p.data)
optimizer = optim.Adam(model3.parameters(),lr=0.05)
f_loss = MSELoss(reduction='mean')
model3.train()
epochs = 10 # set number of epochs
for epoch in range(epochs):
optimizer.zero_grad()
loss = 0.0
for x, y_target in zip(X, y01):
output = model3(Tensor(x)).reshape(1, 2)
targets=Tensor([y_target]).long()
targets = targets.to(torch.float32)
loss += f_loss(output, targets)
loss.backward()
print(loss.item())
optimizer.step()
y_predict = []
for x in X:
output = model3(Tensor(x))
y_predict += [np.argmax(output.detach().numpy())]
print('Accuracy:', sum(y_predict == np.array(y01))/len(np.array(y01)))
for x, y_target, y_ in zip(X, y01, y_predict):
if y_target == 1:
plt.plot(x[0], x[1], 'bo')
else:
plt.plot(x[0], x[1], 'go')
if y_target != y_:
plt.scatter(x[0], x[1], s=200, facecolors='none', edgecolors='r', linewidths=2)
plt.show()
for x, y_target, y_ in zip(X, y01, y_predict):
if y_target == 1:
plt.plot(x[0], x[1], 'bo')
else:
plt.plot(x[0], x[1], 'go')
X1 = np.linspace(0, 1, num=10)
Z1 = np.zeros((len(X1), len(X1)))
for j in range(len(X1)):
for k in range(len(X1)):
Z1[j, k] = np.argmax(model3(Tensor([X1[k],X1[j],X1[k],X1[j]])).detach().numpy())
plt.contourf(X1, X1, Z1, cmap='bwr', levels=30)
for p in model3.parameters():
print(p.data)
|
https://github.com/bagmk/Quantum_Machine_Learning_Express
|
bagmk
|
# Practical Implementation of a Quantum String Matching Algorithm
# S. Faro, F.P. Marino, and A. Scardace
# QUASAR 2024
%%capture
!pip install ibm-cloud-sdk-core==3.18.2
!pip install qiskit==0.45.1
!pip install qiskit-aer==0.13.1
!pip install pylatexenc==2.10
from numpy import log2, sqrt
from numpy import pi
from qiskit.tools.visualization import plot_histogram
from qiskit.circuit.gate import Gate
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
from qiskit import Aer
from qiskit import transpile
def run(circuit: QuantumCircuit, shots: int) -> dict:
simulator = Aer.get_backend('aer_simulator')
compiled_circuit = transpile(circuit, simulator)
job = simulator.run(compiled_circuit, shots=shots)
result = job.result()
return result.get_counts(compiled_circuit)
def init_register(bin_str: str) -> QuantumCircuit:
data_qr = QuantumRegister(len(bin_str), 'data')
qc = QuantumCircuit(data_qr)
for i, bit in enumerate(bin_str):
if bit == '1':
qc.x(data_qr[i])
return qc
classical_value = '1001'
init_register(classical_value).draw(fold=255)
def rot(n: int, s: int) -> QuantumCircuit:
y_qr = QuantumRegister(n, 'y')
qc = QuantumCircuit(y_qr, name='ROT_' + str(s))
for i in range(1, (int(log2(n)) - int(log2(s)) + 2)):
for j in range(int(n / (s * (2**i)))):
for q in range(j * s * (2**i), s * (j*2 ** i+1)):
qc.swap(n - 1 - (q+s), n - 1 - (q+2 ** (i-1) * s + s))
return qc
num_qubits = 8
shift_value = 2
rot(num_qubits, shift_value).draw(fold=255)
def rot_gate(n: int, s: int) -> Gate:
rot_circuit = rot(n, s)
return rot_circuit.to_gate(label='ROT_' + str(s))
def parameterized_rot(n: int) -> QuantumCircuit:
j_qr = QuantumRegister(int(log2(n)), 'j')
y_qr = QuantumRegister(n, 'y')
qc = QuantumCircuit(j_qr, y_qr)
for i in range(int(log2(n))):
crot = rot_gate(n, 2**i).control(1)
qc = qc.compose(crot, [j_qr[i]] + y_qr[:])
return qc
num_qubits = 8
parameterized_rot(num_qubits).draw(fold=255)
text = '10110001'
text_length = len(text)
shift_qr = QuantumRegister(int(log2(text_length)), 'shift')
text_qr = QuantumRegister(text_length, 'text')
output_cr = ClassicalRegister(text_length, 'output_classic')
circ = QuantumCircuit(shift_qr, text_qr, output_cr)
circ.h(shift_qr)
circ.append(init_register(text), text_qr[:])
circ.append(parameterized_rot(text_length), shift_qr[:] + text_qr[:])
circ.measure(text_qr, output_cr)
counts = run(circ, 100)
plot_histogram(counts, title='Rotate ' + text + ' Leftward in Superposition')
def match(m: int) -> QuantumCircuit:
x_qr = QuantumRegister(m, 'x')
y_qr = QuantumRegister(m, 'y')
out_qr = QuantumRegister(1, 'out')
qc = QuantumCircuit(x_qr, y_qr, out_qr)
for i in range(m):
qc.cx(x_qr[i], y_qr[i])
qc.x(y_qr[i])
qc.mcx(y_qr[:], out_qr)
for i in reversed(range(m)):
qc.x(y_qr[i])
qc.cx(x_qr[i], y_qr[i])
return qc
pattern_length = 4
match(pattern_length).draw(fold=255)
text = '1011'
text_length = len(text)
pattern_qr = QuantumRegister(text_length, 'pattern')
text_qr = QuantumRegister(text_length, 'text')
output_qr = QuantumRegister(1, 'output')
output_cr = ClassicalRegister(text_length + 1, 'output_classic')
circ = QuantumCircuit(pattern_qr, text_qr, output_qr, output_cr)
circ.h(pattern_qr)
circ.append(init_register(text), text_qr[:])
circ.append(match(text_length), pattern_qr[:] + text_qr[:] + output_qr[:])
circ.measure(pattern_qr, output_cr[:-1])
circ.measure(output_qr, output_cr[-1])
counts = run(circ, 100)
plot_histogram(counts, title='Matching ' + text)
def match_gate(m: int) -> Gate:
match_circuit = match(m)
return match_circuit.to_gate(label='MATCH')
def esm(m: int, n: int) -> QuantumCircuit:
j_qr = QuantumRegister(int(log2(n)), 'j')
x_qr = QuantumRegister(m, 'x')
y_qr = QuantumRegister(n, 'y')
out = QuantumRegister(1, 'out')
qc = QuantumCircuit(
j_qr,
x_qr,
y_qr,
out
)
qc = qc.compose(parameterized_rot(n), j_qr[:] + y_qr[:])
qc = qc.compose(match_gate(m), x_qr[:] + y_qr[:m] + out[:])
qc = qc.compose(parameterized_rot(n).inverse(), j_qr[:] + y_qr[:])
return qc
pattern_length = 2
text_length = 4
esm(pattern_length, text_length).draw(fold=255)
def esm_oracle(m: int, n: int):
esm_circuit = esm(m, n)
return esm_circuit.to_gate(label='ESMO')
def diffuser(n: int) -> Gate:
qc = QuantumCircuit(n)
qc.h(range(n))
qc.x(range(n))
qc.h(n-1)
qc.mcx(list(range(n-1)), n-1)
qc.h(n-1)
qc.x(range(n))
qc.h(range(n))
return qc.to_gate(label='DIFF')
def grover(esmo: Gate, t: int, x: str, y: str) -> QuantumCircuit:
n = len(y)
m = len(x)
logn = int(log2(n))
num_iterations = int(pi/4 * sqrt(n/t))
j_qr = QuantumRegister(logn, 'j')
x_qr = QuantumRegister(m, 'x')
y_qr = QuantumRegister(n, 'y')
out_qr = QuantumRegister(2, 'out')
out_cr = ClassicalRegister(logn+1, 'c')
qc = QuantumCircuit(j_qr, x_qr, y_qr, out_qr, out_cr)
qc.h(j_qr)
qc.x(out_qr[0])
qc.h(out_qr[0])
qc = qc.compose(init_register(x), x_qr[:])
qc = qc.compose(init_register(y), y_qr[:])
for _ in range(num_iterations):
qc = qc.compose(esmo)
qc = qc.compose(diffuser(logn))
qc.measure(j_qr, out_cr[:-1])
qc = qc.compose(esmo, j_qr[:] + x_qr[:] + y_qr[:] + [out_qr[1]])
qc.measure(out_qr[1], out_cr[-1])
return qc
x = '11'
y = '10101100'
esmo = esm_oracle(len(x), len(y))
grover(esmo, 1, x, y).draw(fold=255)
x = '00'
y = '01010101'
esmo = esm_oracle(len(x), len(y))
counts = run(grover(esmo, 1, x, y), 100)
plot_histogram(counts, title=f'Search for {x} in {y} - 0 occurrence(s)')
x = '00'
y = '11010011'
esmo = esm_oracle(len(x), len(y))
counts = run(grover(esmo, 1, x, y), 100)
plot_histogram(counts, title=f'Search for {x} in {y} - 1 occurrence(s)')
x = '00'
y = '00111001'
esmo = esm_oracle(len(x), len(y))
counts = run(grover(esmo, 2, x, y), 100)
plot_histogram(counts, title=f'Search for {x} in {y} - 2 occurrence(s)')
def search(x: str, y: str) -> int:
m = len(x)
n = len(y)
esmo = esm_oracle(m, n)
for t in range(1, int(n/2) + 1):
print('Trying with t =', t)
results = run(grover(esmo, t, x, y), 1)
results = list(results.keys())[0]
outcome = int(results[0])
position = int(results[1:], 2)
if outcome: return position
else: print('Pattern not found in position', position)
return -1
x = input('Enter the value of x: ')
y = input('Enter the value of y: ')
if len(x) > len(y):
raise Exception('The length of x must be shorter than the length of y.')
if x.count('0') + x.count('1') < len(x):
raise Exception('The pattern must be a binary string.')
if y.count('0') + y.count('1') < len(y):
raise Exception('The text must be a binary string.')
print('')
position = search(x, y)
if position >= 0: print('Pattern occurrence found in position', str(position))
else: print('Pattern occurrence not found.')
|
https://github.com/bagmk/Quantum_Machine_Learning_Express
|
bagmk
|
from qiskit import *
from oracle_generation import generate_oracle
get_bin = lambda x, n: format(x, 'b').zfill(n)
def gen_circuits(min,max,size):
circuits = []
secrets = []
ORACLE_SIZE = size
for i in range(min,max+1):
cur_str = get_bin(i,ORACLE_SIZE-1)
(circuit, secret) = generate_oracle(ORACLE_SIZE,False,3,cur_str)
circuits.append(circuit)
secrets.append(secret)
return (circuits, secrets)
|
https://github.com/bagmk/Quantum_Machine_Learning_Express
|
bagmk
|
from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit
from math import pi
def circuit1(qc,theta,L,repeat):
#circuit 1
#theta is list of the parameters
#theta length is 8L
#L is the number of repeatation
# repeat will conjugate the first part and add next the the circuit for expressibility
# 0:No, 1: Repeat
count=0
for l in range(L):
for i in range(4):
qc.rx(theta[count],i)
count=count+1
for i in range(4):
qc.rz(theta[count],i)
count=count+1
if repeat!=0:
for l in range(L):
for i in range(4):
qc.rz(theta[count],i)
count=count+1
for i in range(4):
qc.rx(theta[count],i)
count=count+1
return qc
def circuit2(qc,theta,L,repeat):
#circuit 2
#theta is list of the parameters
#theta length is 8L
#L is the number of repeatation
# repeat will conjugate the first part and add next the the circuit for expressibility
# 0:No, 1: Repeat
count=0
for l in range(L):
for i in range(4):
qc.rx(theta[count],i)
count=count+1
for i in range(4):
qc.rz(theta[count],i)
count=count+1
qc.cx(3,2)
qc.cx(2,1)
qc.cx(1,0)
if repeat!=0:
for l in range(L):
qc.cx(1,0)
qc.cx(2,1)
qc.cx(3,2)
for i in range(4):
qc.rz(theta[count],i)
count=count+1
for i in range(4):
qc.rx(theta[count],i)
count=count+1
return qc
def circuit3(qc,theta,L,repeat):
#circuit 3
#theta is list of the parameters
#theta length is (11)L
#L is the number of repeatation
# repeat will conjugate the first part and add next the the circuit for expressibility
# 0:No, 1: Repeat
count=0
for l in range(L):
for i in range(4):
qc.rx(theta[count],i)
count=count+1
for i in range(4):
qc.rz(theta[count],i)
count=count+1
qc.crz(theta[count],3,2)
count=count+1
qc.crz(theta[count],2,1)
count=count+1
qc.crz(theta[count],1,0)
count=count+1
if repeat!=0:
for l in range(L):
qc.crz(theta[count],1,0)
count=count+1
qc.crz(theta[count],2,1)
count=count+1
qc.crz(theta[count],3,2)
count=count+1
for i in range(4):
qc.rz(theta[count],i)
count=count+1
for i in range(4):
qc.rx(theta[count],i)
count=count+1
return qc
def circuit4(qc,theta,L,repeat):
#circuit 4
#theta is list of the parameters
#theta length is (11)L
#L is the number of repeatation
# repeat will conjugate the first part and add next the the circuit for expressibility
# 0:No, 1: Repeat
count=0
for l in range(L):
for i in range(4):
qc.rx(theta[count],i)
count=count+1
for i in range(4):
qc.rz(theta[count],i)
count=count+1
qc.crx(theta[count],3,2)
count=count+1
qc.crx(theta[count],2,1)
count=count+1
qc.crx(theta[count],1,0)
count=count+1
if repeat!=0:
for l in range(L):
qc.crx(theta[count],1,0)
count=count+1
qc.crx(theta[count],2,1)
count=count+1
qc.crx(theta[count],3,2)
count=count+1
for i in range(4):
qc.rz(theta[count],i)
count=count+1
for i in range(4):
qc.rx(theta[count],i)
count=count+1
return qc
def circuit5(qc,theta,L,repeat):
#circuit 5
#theta is list of the parameters
#theta length is (28)L
#L is the number of repeatation
# repeat will conjugate the first part and add next the the circuit for expressibility
# 0:No, 1: Repeat
count=0
for l in range(L):
for i in range(4):
qc.rx(theta[count],i)
count=count+1
for i in range(4):
qc.rz(theta[count],i)
count=count+1
for j in range(4):
for i in range(4):
if i!=j:
qc.crz(theta[count],3-j,3-i)
count=count+1
for i in range(4):
qc.rx(theta[count],i)
count=count+1
for i in range(4):
qc.rz(theta[count],i)
count=count+1
if repeat!=0:
for l in range(L):
for i in range(4):
qc.rz(theta[count],i)
count=count+1
for i in range(4):
qc.rx(theta[count],i)
count=count+1
for j in range(4):
for i in range(4):
if i!=j:
qc.crz(theta[count],j,i)
count=count+1
for i in range(4):
qc.rz(theta[count],i)
count=count+1
for i in range(4):
qc.rx(theta[count],i)
count=count+1
return qc
def circuit6(qc,theta,L,repeat):
#circuit 6
#theta is list of the parameters
#theta length is (28)L
#L is the number of repeatation
# repeat will conjugate the first part and add next the the circuit for expressibility
# 0:No, 1: Repeat
count=0
for l in range(L):
for i in range(4):
qc.rx(theta[count],i)
count=count+1
for i in range(4):
qc.rz(theta[count],i)
count=count+1
for j in range(4):
for i in range(4):
if i!=j:
qc.crx(theta[count],3-j,3-i)
count=count+1
for i in range(4):
qc.rx(theta[count],i)
count=count+1
for i in range(4):
qc.rz(theta[count],i)
count=count+1
if repeat!=0:
for l in range(L):
for i in range(4):
qc.rz(theta[count],i)
count=count+1
for i in range(4):
qc.rx(theta[count],i)
count=count+1
for j in range(4):
for i in range(4):
if i!=j:
qc.crx(theta[count],j,i)
count=count+1
for i in range(4):
qc.rz(theta[count],i)
count=count+1
for i in range(4):
qc.rx(theta[count],i)
count=count+1
return qc
def circuit7(qc,theta,L,repeat):
#circuit 7
#theta is list of the parameters
#theta length is (19)L
#L is the number of repeatation
# repeat will conjugate the first part and add next the the circuit for expressibility
# 0:No, 1: Repeat
count=0
for l in range(L):
for i in range(4):
qc.rx(theta[count],i)
count=count+1
for i in range(4):
qc.rz(theta[count],i)
count=count+1
qc.crz(theta[count],1,0)
count=count+1
qc.crz(theta[count],3,2)
count=count+1
for i in range(4):
qc.rx(theta[count],i)
count=count+1
for i in range(4):
qc.rz(theta[count],i)
count=count+1
qc.crz(theta[count],2,1)
count=count+1
if repeat!=0:
for l in range(L):
qc.crz(theta[count],2,1)
count=count+1
for i in range(4):
qc.rz(theta[count],i)
count=count+1
for i in range(4):
qc.rx(theta[count],i)
count=count+1
qc.crz(theta[count],3,2)
count=count+1
qc.crz(theta[count],1,0)
count=count+1
for i in range(4):
qc.rz(theta[count],i)
count=count+1
for i in range(4):
qc.rx(theta[count],i)
count=count+1
return qc
def circuit8(qc,theta,L,repeat):
#circuit 8
#theta is list of the parameters
#theta length is (19)L
#L is the number of repeatation
# repeat will conjugate the first part and add next the the circuit for expressibility
# 0:No, 1: Repeat
count=0
for l in range(L):
for i in range(4):
qc.rx(theta[count],i)
count=count+1
for i in range(4):
qc.rz(theta[count],i)
count=count+1
qc.crx(theta[count],1,0)
count=count+1
qc.crx(theta[count],3,2)
count=count+1
for i in range(4):
qc.rx(theta[count],i)
count=count+1
for i in range(4):
qc.rz(theta[count],i)
count=count+1
qc.crx(theta[count],2,1)
count=count+1
if repeat!=0:
for l in range(L):
qc.crx(theta[count],2,1)
count=count+1
for i in range(4):
qc.rz(theta[count],i)
count=count+1
for i in range(4):
qc.rx(theta[count],i)
count=count+1
qc.crx(theta[count],3,2)
count=count+1
qc.crx(theta[count],1,0)
count=count+1
for i in range(4):
qc.rz(theta[count],i)
count=count+1
for i in range(4):
qc.rx(theta[count],i)
count=count+1
return qc
def circuit9(qc,theta,L,repeat):
#circuit 9
#theta is list of the parameters
#theta length is (4)L
#L is the number of repeatation
# repeat will conjugate the first part and add next the the circuit for expressibility
# 0:No, 1: Repeat
count=0
for l in range(L):
for i in range(4):
qc.h(i)
qc.cz(3,2)
qc.cz(2,1)
qc.cz(1,0)
for i in range(4):
qc.rx(theta[count],i)
count=count+1
if repeat!=0:
for l in range(L):
for i in range(4):
qc.rx(theta[count],i)
count=count+1
qc.cz(1,0)
qc.cz(2,1)
qc.cz(3,2)
for i in range(4):
qc.h(i)
return qc
def circuit10(qc,theta,L,repeat):
#circuit 10
#theta is list of the parameters
#theta length is (4)L+4
#L is the number of repeatation
# repeat will conjugate the first part and add next the the circuit for expressibility
# 0:No, 1: Repeat
count=0
for i in range(4):
qc.ry(theta[count],i)
count=count+1
for l in range(L):
qc.cz(3,2)
qc.cz(2,1)
qc.cz(1,0)
qc.cz(3,0)
for i in range(4):
qc.ry(theta[count],i)
count=count+1
if repeat!=0:
for l in range(L):
for i in range(4):
qc.ry(theta[count],i)
count=count+1
qc.cz(3,0)
qc.cz(1,0)
qc.cz(2,1)
qc.cz(3,2)
for i in range(4):
qc.ry(theta[count],i)
count=count+1
return qc
def circuit11(qc,theta,L,repeat):
#circuit 11
#theta is list of the parameters
#theta length is (12)L
#L is the number of repeatation
# repeat will conjugate the first part and add next the the circuit for expressibility
# 0:No, 1: Repeat
count=0
for l in range(L):
for i in range(4):
qc.ry(theta[count],i)
count=count+1
for i in range(4):
qc.rz(theta[count],i)
count=count+1
qc.cx(1,0)
qc.cx(3,2)
qc.ry(theta[count],1)
count=count+1
qc.ry(theta[count],2)
count=count+1
qc.rz(theta[count],1)
count=count+1
qc.rz(theta[count],2)
count=count+1
qc.cx(2,1)
if repeat!=0:
for l in range(L):
qc.cx(2,1)
qc.rz(theta[count],2)
count=count+1
qc.rz(theta[count],1)
count=count+1
qc.ry(theta[count],2)
count=count+1
qc.ry(theta[count],1)
count=count+1
qc.cx(3,2)
qc.cx(1,0)
for i in range(4):
qc.rz(theta[count],i)
count=count+1
for i in range(4):
qc.ry(theta[count],i)
count=count+1
return qc
def circuit12(qc,theta,L,repeat):
#circuit 12
#theta is list of the parameters
#theta length is (12)L
#L is the number of repeatation
# repeat will conjugate the first part and add next the the circuit for expressibility
# 0:No, 1: Repeat
count=0
for l in range(L):
for i in range(4):
qc.ry(theta[count],i)
count=count+1
for i in range(4):
qc.rz(theta[count],i)
count=count+1
qc.cz(1,0)
qc.cz(3,2)
qc.ry(theta[count],1)
count=count+1
qc.ry(theta[count],2)
count=count+1
qc.rz(theta[count],1)
count=count+1
qc.rz(theta[count],2)
count=count+1
qc.cz(2,1)
if repeat!=0:
for l in range(L):
qc.cz(2,1)
qc.rz(theta[count],2)
count=count+1
qc.rz(theta[count],1)
count=count+1
qc.ry(theta[count],2)
count=count+1
qc.ry(theta[count],1)
count=count+1
qc.cz(3,2)
qc.cz(1,0)
for i in range(4):
qc.rz(theta[count],i)
count=count+1
for i in range(4):
qc.ry(theta[count],i)
count=count+1
return qc
def circuit13(qc,theta,L,repeat):
#circuit 13
#theta is list of the parameters
#theta length is (16)L
#L is the number of repeatation
# repeat will conjugate the first part and add next the the circuit for expressibility
# 0:No, 1: Repeat
count=0
for l in range(L):
for i in range(4):
qc.ry(theta[count],i)
count=count+1
qc.crz(theta[count],3,0)
count=count+1
qc.crz(theta[count],2,3)
count=count+1
qc.crz(theta[count],1,2)
count=count+1
qc.crz(theta[count],0,1)
count=count+1
for i in range(4):
qc.ry(theta[count],i)
count=count+1
qc.crz(theta[count],3,2)
count=count+1
qc.crz(theta[count],0,3)
count=count+1
qc.crz(theta[count],1,0)
count=count+1
qc.crz(theta[count],2,1)
count=count+1
if repeat!=0:
for l in range(L):
qc.crz(theta[count],2,1)
count=count+1
qc.crz(theta[count],1,0)
count=count+1
qc.crz(theta[count],0,3)
count=count+1
qc.crz(theta[count],3,2)
count=count+1
for i in range(4):
qc.ry(theta[count],i)
count=count+1
qc.crz(theta[count],0,1)
count=count+1
qc.crz(theta[count],1,2)
count=count+1
qc.crz(theta[count],2,3)
count=count+1
qc.crz(theta[count],3,0)
count=count+1
for i in range(4):
qc.ry(theta[count],i)
count=count+1
return qc
def circuit14(qc,theta,L,repeat):
#circuit 14
#theta is list of the parameters
#theta length is (16)L
#L is the number of repeatation
# repeat will conjugate the first part and add next the the circuit for expressibility
# 0:No, 1: Repeat
count=0
for l in range(L):
for i in range(4):
qc.ry(theta[count],i)
count=count+1
qc.crx(theta[count],3,0)
count=count+1
qc.crx(theta[count],2,3)
count=count+1
qc.crx(theta[count],1,2)
count=count+1
qc.crx(theta[count],0,1)
count=count+1
for i in range(4):
qc.ry(theta[count],i)
count=count+1
qc.crx(theta[count],3,2)
count=count+1
qc.crx(theta[count],0,3)
count=count+1
qc.crx(theta[count],1,0)
count=count+1
qc.crx(theta[count],2,1)
count=count+1
if repeat!=0:
for l in range(L):
qc.crx(theta[count],2,1)
count=count+1
qc.crx(theta[count],1,0)
count=count+1
qc.crx(theta[count],0,3)
count=count+1
qc.crx(theta[count],3,2)
count=count+1
for i in range(4):
qc.ry(theta[count],i)
count=count+1
qc.crx(theta[count],0,1)
count=count+1
qc.crx(theta[count],1,2)
count=count+1
qc.crx(theta[count],2,3)
count=count+1
qc.crx(theta[count],3,0)
count=count+1
for i in range(4):
qc.ry(theta[count],i)
count=count+1
return qc
def circuit15(qc,theta,L,repeat):
#circuit 15
#theta is list of the parameters
#theta length is (8)L
#L is the number of repeatation
# repeat will conjugate the first part and add next the the circuit for expressibility
# 0:No, 1: Repeat
count=0
for l in range(L):
for i in range(4):
qc.ry(theta[count],i)
count=count+1
qc.cx(3,0)
qc.cx(2,3)
qc.cx(1,2)
qc.cx(0,1)
for i in range(4):
qc.ry(theta[count],i)
count=count+1
qc.cx(3,2)
qc.cx(0,3)
qc.cx(1,0)
qc.cx(2,1)
if repeat!=0:
for l in range(L):
qc.cx(2,1)
qc.cx(1,0)
qc.cx(0,3)
qc.cx(3,2)
for i in range(4):
qc.ry(theta[count],i)
count=count+1
qc.cx(0,1)
qc.cx(1,2)
qc.cx(2,3)
qc.cx(3,0)
for i in range(4):
qc.ry(theta[count],i)
count=count+1
return qc
def circuit16(qc,theta,L,repeat):
#circuit 16
#theta is list of the parameters
#theta length is (11)L
#L is the number of repeatation
# repeat will conjugate the first part and add next the the circuit for expressibility
# 0:No, 1: Repeat
count=0
for l in range(L):
for i in range(4):
qc.rx(theta[count],i)
count=count+1
for i in range(4):
qc.rz(theta[count],i)
count=count+1
qc.crz(theta[count],1,0)
count=count+1
qc.crz(theta[count],3,2)
count=count+1
qc.crz(theta[count],2,1)
count=count+1
if repeat!=0:
for l in range(L):
qc.crz(theta[count],2,1)
count=count+1
qc.crz(theta[count],3,2)
count=count+1
qc.crz(theta[count],1,0)
count=count+1
for i in range(4):
qc.rz(theta[count],i)
count=count+1
for i in range(4):
qc.rx(theta[count],i)
count=count+1
return qc
def circuit17(qc,theta,L,repeat):
#circuit 17
#theta is list of the parameters
#theta length is (11)L
#L is the number of repeatation
# repeat will conjugate the first part and add next the the circuit for expressibility
# 0:No, 1: Repeat
count=0
for l in range(L):
for i in range(4):
qc.rx(theta[count],i)
count=count+1
for i in range(4):
qc.rz(theta[count],i)
count=count+1
qc.crx(theta[count],1,0)
count=count+1
qc.crx(theta[count],3,2)
count=count+1
qc.crx(theta[count],2,1)
count=count+1
if repeat!=0:
for l in range(L):
qc.crx(theta[count],2,1)
count=count+1
qc.crx(theta[count],3,2)
count=count+1
qc.crx(theta[count],1,0)
count=count+1
for i in range(4):
qc.rz(theta[count],i)
count=count+1
for i in range(4):
qc.rx(theta[count],i)
count=count+1
return qc
def circuit18(qc,theta,L,repeat):
#circuit 18
#theta is list of the parameters
#theta length is (12)L
#L is the number of repeatation
# repeat will conjugate the first part and add next the the circuit for expressibility
# 0:No, 1: Repeat
count=0
for l in range(L):
for i in range(4):
qc.rx(theta[count],i)
count=count+1
for i in range(4):
qc.rz(theta[count],i)
count=count+1
qc.crz(theta[count],3,0)
count=count+1
qc.crz(theta[count],2,3)
count=count+1
qc.crz(theta[count],1,2)
count=count+1
qc.crz(theta[count],0,1)
count=count+1
if repeat!=0:
for l in range(L):
qc.crz(theta[count],0,1)
count=count+1
qc.crz(theta[count],1,2)
count=count+1
qc.crz(theta[count],2,3)
count=count+1
qc.crz(theta[count],3,0)
count=count+1
for i in range(4):
qc.rz(theta[count],i)
count=count+1
for i in range(4):
qc.rx(theta[count],i)
count=count+1
return qc
def circuit19(qc,theta,L,repeat):
#circuit 1
#theta is list of the parameters
#theta length is (12)L
#L is the number of repeatation
# repeat will conjugate the first part and add next the the circuit for expressibility
# 0:No, 1: Repeat
count=0
for l in range(L):
for i in range(4):
qc.rx(theta[count],i)
count=count+1
for i in range(4):
qc.rz(theta[count],i)
count=count+1
qc.crx(theta[count],3,0)
count=count+1
qc.crx(theta[count],2,3)
count=count+1
qc.crx(theta[count],1,2)
count=count+1
qc.crx(theta[count],0,1)
count=count+1
if repeat!=0:
for l in range(L):
qc.crx(theta[count],0,1)
count=count+1
qc.crx(theta[count],1,2)
count=count+1
qc.crx(theta[count],2,3)
count=count+1
qc.crx(theta[count],3,0)
count=count+1
for i in range(4):
qc.rz(theta[count],i)
count=count+1
for i in range(4):
qc.rx(theta[count],i)
count=count+1
return qc
|
https://github.com/bagmk/Quantum_Machine_Learning_Express
|
bagmk
|
# This code is part of Qiskit.
#
# (C) Copyright IBM 2017.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
# pylint: disable=bad-docstring-quotes,invalid-name
"""Quantum circuit object."""
from __future__ import annotations
import collections.abc
import copy
import itertools
import multiprocessing as mp
import string
import re
import warnings
import typing
from collections import OrderedDict, defaultdict, namedtuple
from typing import (
Union,
Optional,
Tuple,
Type,
TypeVar,
Sequence,
Callable,
Mapping,
Iterable,
Any,
DefaultDict,
Literal,
overload,
)
import numpy as np
from qiskit.exceptions import QiskitError
from qiskit.utils.multiprocessing import is_main_process
from qiskit.circuit.instruction import Instruction
from qiskit.circuit.gate import Gate
from qiskit.circuit.parameter import Parameter
from qiskit.circuit.exceptions import CircuitError
from qiskit.utils import optionals as _optionals
from . import _classical_resource_map
from ._utils import sort_parameters
from .classical import expr
from .parameterexpression import ParameterExpression, ParameterValueType
from .quantumregister import QuantumRegister, Qubit, AncillaRegister, AncillaQubit
from .classicalregister import ClassicalRegister, Clbit
from .parametertable import ParameterReferences, ParameterTable, ParameterView
from .parametervector import ParameterVector
from .instructionset import InstructionSet
from .operation import Operation
from .register import Register
from .bit import Bit
from .quantumcircuitdata import QuantumCircuitData, CircuitInstruction
from .delay import Delay
from .measure import Measure
from .reset import Reset
from .tools import pi_check
if typing.TYPE_CHECKING:
import qiskit # pylint: disable=cyclic-import
from qiskit.transpiler.layout import TranspileLayout # pylint: disable=cyclic-import
BitLocations = namedtuple("BitLocations", ("index", "registers"))
# The following types are not marked private to avoid leaking this "private/public" abstraction out
# into the documentation. They are not imported by circuit.__init__, nor are they meant to be.
# Arbitrary type variables for marking up generics.
S = TypeVar("S")
T = TypeVar("T")
# Types that can be coerced to a valid Qubit specifier in a circuit.
QubitSpecifier = Union[
Qubit,
QuantumRegister,
int,
slice,
Sequence[Union[Qubit, int]],
]
# Types that can be coerced to a valid Clbit specifier in a circuit.
ClbitSpecifier = Union[
Clbit,
ClassicalRegister,
int,
slice,
Sequence[Union[Clbit, int]],
]
# Generic type which is either :obj:`~Qubit` or :obj:`~Clbit`, used to specify types of functions
# which operate on either type of bit, but not both at the same time.
BitType = TypeVar("BitType", Qubit, Clbit)
# Regex pattern to match valid OpenQASM identifiers
VALID_QASM2_IDENTIFIER = re.compile("[a-z][a-zA-Z_0-9]*")
QASM2_RESERVED = {
"OPENQASM",
"qreg",
"creg",
"include",
"gate",
"opaque",
"U",
"CX",
"measure",
"reset",
"if",
"barrier",
}
class QuantumCircuit:
"""Create a new circuit.
A circuit is a list of instructions bound to some registers.
Args:
regs (list(:class:`~.Register`) or list(``int``) or list(list(:class:`~.Bit`))): The
registers to be included in the circuit.
* If a list of :class:`~.Register` objects, represents the :class:`.QuantumRegister`
and/or :class:`.ClassicalRegister` objects to include in the circuit.
For example:
* ``QuantumCircuit(QuantumRegister(4))``
* ``QuantumCircuit(QuantumRegister(4), ClassicalRegister(3))``
* ``QuantumCircuit(QuantumRegister(4, 'qr0'), QuantumRegister(2, 'qr1'))``
* If a list of ``int``, the amount of qubits and/or classical bits to include in
the circuit. It can either be a single int for just the number of quantum bits,
or 2 ints for the number of quantum bits and classical bits, respectively.
For example:
* ``QuantumCircuit(4) # A QuantumCircuit with 4 qubits``
* ``QuantumCircuit(4, 3) # A QuantumCircuit with 4 qubits and 3 classical bits``
* If a list of python lists containing :class:`.Bit` objects, a collection of
:class:`.Bit` s to be added to the circuit.
name (str): the name of the quantum circuit. If not set, an
automatically generated string will be assigned.
global_phase (float or ParameterExpression): The global phase of the circuit in radians.
metadata (dict): Arbitrary key value metadata to associate with the
circuit. This gets stored as free-form data in a dict in the
:attr:`~qiskit.circuit.QuantumCircuit.metadata` attribute. It will
not be directly used in the circuit.
Raises:
CircuitError: if the circuit name, if given, is not valid.
Examples:
Construct a simple Bell state circuit.
.. plot::
:include-source:
from qiskit import QuantumCircuit
qc = QuantumCircuit(2, 2)
qc.h(0)
qc.cx(0, 1)
qc.measure([0, 1], [0, 1])
qc.draw('mpl')
Construct a 5-qubit GHZ circuit.
.. code-block::
from qiskit import QuantumCircuit
qc = QuantumCircuit(5)
qc.h(0)
qc.cx(0, range(1, 5))
qc.measure_all()
Construct a 4-qubit Bernstein-Vazirani circuit using registers.
.. plot::
:include-source:
from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit
qr = QuantumRegister(3, 'q')
anc = QuantumRegister(1, 'ancilla')
cr = ClassicalRegister(3, 'c')
qc = QuantumCircuit(qr, anc, cr)
qc.x(anc[0])
qc.h(anc[0])
qc.h(qr[0:3])
qc.cx(qr[0:3], anc[0])
qc.h(qr[0:3])
qc.barrier(qr)
qc.measure(qr, cr)
qc.draw('mpl')
"""
instances = 0
prefix = "circuit"
# Class variable OPENQASM header
header = "OPENQASM 2.0;"
extension_lib = 'include "qelib1.inc";'
def __init__(
self,
*regs: Register | int | Sequence[Bit],
name: str | None = None,
global_phase: ParameterValueType = 0,
metadata: dict | None = None,
):
if any(not isinstance(reg, (list, QuantumRegister, ClassicalRegister)) for reg in regs):
# check if inputs are integers, but also allow e.g. 2.0
try:
valid_reg_size = all(reg == int(reg) for reg in regs)
except (ValueError, TypeError):
valid_reg_size = False
if not valid_reg_size:
raise CircuitError(
"Circuit args must be Registers or integers. (%s '%s' was "
"provided)" % ([type(reg).__name__ for reg in regs], regs)
)
regs = tuple(int(reg) for reg in regs) # cast to int
self._base_name = None
if name is None:
self._base_name = self.cls_prefix()
self._name_update()
elif not isinstance(name, str):
raise CircuitError(
"The circuit name should be a string (or None to auto-generate a name)."
)
else:
self._base_name = name
self.name = name
self._increment_instances()
# Data contains a list of instructions and their contexts,
# in the order they were applied.
self._data: list[CircuitInstruction] = []
self._op_start_times = None
# A stack to hold the instruction sets that are being built up during for-, if- and
# while-block construction. These are stored as a stripped down sequence of instructions,
# and sets of qubits and clbits, rather than a full QuantumCircuit instance because the
# builder interfaces need to wait until they are completed before they can fill in things
# like `break` and `continue`. This is because these instructions need to "operate" on the
# full width of bits, but the builder interface won't know what bits are used until the end.
self._control_flow_scopes: list[
"qiskit.circuit.controlflow.builder.ControlFlowBuilderBlock"
] = []
self.qregs: list[QuantumRegister] = []
self.cregs: list[ClassicalRegister] = []
self._qubits: list[Qubit] = []
self._clbits: list[Clbit] = []
# Dict mapping Qubit or Clbit instances to tuple comprised of 0) the
# corresponding index in circuit.{qubits,clbits} and 1) a list of
# Register-int pairs for each Register containing the Bit and its index
# within that register.
self._qubit_indices: dict[Qubit, BitLocations] = {}
self._clbit_indices: dict[Clbit, BitLocations] = {}
self._ancillas: list[AncillaQubit] = []
self._calibrations: DefaultDict[str, dict[tuple, Any]] = defaultdict(dict)
self.add_register(*regs)
# Parameter table tracks instructions with variable parameters.
self._parameter_table = ParameterTable()
# Cache to avoid re-sorting parameters
self._parameters = None
self._layout = None
self._global_phase: ParameterValueType = 0
self.global_phase = global_phase
self.duration = None
self.unit = "dt"
self.metadata = {} if metadata is None else metadata
@staticmethod
def from_instructions(
instructions: Iterable[
CircuitInstruction
| tuple[qiskit.circuit.Instruction]
| tuple[qiskit.circuit.Instruction, Iterable[Qubit]]
| tuple[qiskit.circuit.Instruction, Iterable[Qubit], Iterable[Clbit]]
],
*,
qubits: Iterable[Qubit] = (),
clbits: Iterable[Clbit] = (),
name: str | None = None,
global_phase: ParameterValueType = 0,
metadata: dict | None = None,
) -> "QuantumCircuit":
"""Construct a circuit from an iterable of CircuitInstructions.
Args:
instructions: The instructions to add to the circuit.
qubits: Any qubits to add to the circuit. This argument can be used,
for example, to enforce a particular ordering of qubits.
clbits: Any classical bits to add to the circuit. This argument can be used,
for example, to enforce a particular ordering of classical bits.
name: The name of the circuit.
global_phase: The global phase of the circuit in radians.
metadata: Arbitrary key value metadata to associate with the circuit.
Returns:
The quantum circuit.
"""
circuit = QuantumCircuit(name=name, global_phase=global_phase, metadata=metadata)
added_qubits = set()
added_clbits = set()
if qubits:
qubits = list(qubits)
circuit.add_bits(qubits)
added_qubits.update(qubits)
if clbits:
clbits = list(clbits)
circuit.add_bits(clbits)
added_clbits.update(clbits)
for instruction in instructions:
if not isinstance(instruction, CircuitInstruction):
instruction = CircuitInstruction(*instruction)
qubits = [qubit for qubit in instruction.qubits if qubit not in added_qubits]
clbits = [clbit for clbit in instruction.clbits if clbit not in added_clbits]
circuit.add_bits(qubits)
circuit.add_bits(clbits)
added_qubits.update(qubits)
added_clbits.update(clbits)
circuit._append(instruction)
return circuit
@property
def layout(self) -> Optional[TranspileLayout]:
r"""Return any associated layout information about the circuit
This attribute contains an optional :class:`~.TranspileLayout`
object. This is typically set on the output from :func:`~.transpile`
or :meth:`.PassManager.run` to retain information about the
permutations caused on the input circuit by transpilation.
There are two types of permutations caused by the :func:`~.transpile`
function, an initial layout which permutes the qubits based on the
selected physical qubits on the :class:`~.Target`, and a final layout
which is an output permutation caused by :class:`~.SwapGate`\s
inserted during routing.
"""
return self._layout
@property
def data(self) -> QuantumCircuitData:
"""Return the circuit data (instructions and context).
Returns:
QuantumCircuitData: a list-like object containing the :class:`.CircuitInstruction`\\ s
for each instruction.
"""
return QuantumCircuitData(self)
@data.setter
def data(self, data_input: Iterable):
"""Sets the circuit data from a list of instructions and context.
Args:
data_input (Iterable): A sequence of instructions with their execution contexts. The
elements must either be instances of :class:`.CircuitInstruction` (preferred), or a
3-tuple of ``(instruction, qargs, cargs)`` (legacy). In the legacy format,
``instruction`` must be an :class:`~.circuit.Instruction`, while ``qargs`` and
``cargs`` must be iterables of :class:`.Qubit` or :class:`.Clbit` specifiers
(similar to the allowed forms in calls to :meth:`append`).
"""
# If data_input is QuantumCircuitData(self), clearing self._data
# below will also empty data_input, so make a shallow copy first.
data_input = list(data_input)
self._data = []
self._parameter_table = ParameterTable()
if not data_input:
return
if isinstance(data_input[0], CircuitInstruction):
for instruction in data_input:
self.append(instruction)
else:
for instruction, qargs, cargs in data_input:
self.append(instruction, qargs, cargs)
@property
def op_start_times(self) -> list[int]:
"""Return a list of operation start times.
This attribute is enabled once one of scheduling analysis passes
runs on the quantum circuit.
Returns:
List of integers representing instruction start times.
The index corresponds to the index of instruction in :attr:`QuantumCircuit.data`.
Raises:
AttributeError: When circuit is not scheduled.
"""
if self._op_start_times is None:
raise AttributeError(
"This circuit is not scheduled. "
"To schedule it run the circuit through one of the transpiler scheduling passes."
)
return self._op_start_times
@property
def calibrations(self) -> dict:
"""Return calibration dictionary.
The custom pulse definition of a given gate is of the form
``{'gate_name': {(qubits, params): schedule}}``
"""
return dict(self._calibrations)
@calibrations.setter
def calibrations(self, calibrations: dict):
"""Set the circuit calibration data from a dictionary of calibration definition.
Args:
calibrations (dict): A dictionary of input in the format
``{'gate_name': {(qubits, gate_params): schedule}}``
"""
self._calibrations = defaultdict(dict, calibrations)
def has_calibration_for(self, instruction: CircuitInstruction | tuple):
"""Return True if the circuit has a calibration defined for the instruction context. In this
case, the operation does not need to be translated to the device basis.
"""
if isinstance(instruction, CircuitInstruction):
operation = instruction.operation
qubits = instruction.qubits
else:
operation, qubits, _ = instruction
if not self.calibrations or operation.name not in self.calibrations:
return False
qubits = tuple(self.qubits.index(qubit) for qubit in qubits)
params = []
for p in operation.params:
if isinstance(p, ParameterExpression) and not p.parameters:
params.append(float(p))
else:
params.append(p)
params = tuple(params)
return (qubits, params) in self.calibrations[operation.name]
@property
def metadata(self) -> dict:
"""The user provided metadata associated with the circuit.
The metadata for the circuit is a user provided ``dict`` of metadata
for the circuit. It will not be used to influence the execution or
operation of the circuit, but it is expected to be passed between
all transforms of the circuit (ie transpilation) and that providers will
associate any circuit metadata with the results it returns from
execution of that circuit.
"""
return self._metadata
@metadata.setter
def metadata(self, metadata: dict | None):
"""Update the circuit metadata"""
if metadata is None:
metadata = {}
warnings.warn(
"Setting metadata to None was deprecated in Terra 0.24.0 and this ability will be "
"removed in a future release. Instead, set metadata to an empty dictionary.",
DeprecationWarning,
stacklevel=2,
)
elif not isinstance(metadata, dict):
raise TypeError("Only a dictionary is accepted for circuit metadata")
self._metadata = metadata
def __str__(self) -> str:
return str(self.draw(output="text"))
def __eq__(self, other) -> bool:
if not isinstance(other, QuantumCircuit):
return False
# TODO: remove the DAG from this function
from qiskit.converters import circuit_to_dag
return circuit_to_dag(self, copy_operations=False) == circuit_to_dag(
other, copy_operations=False
)
@classmethod
def _increment_instances(cls):
cls.instances += 1
@classmethod
def cls_instances(cls) -> int:
"""Return the current number of instances of this class,
useful for auto naming."""
return cls.instances
@classmethod
def cls_prefix(cls) -> str:
"""Return the prefix to use for auto naming."""
return cls.prefix
def _name_update(self) -> None:
"""update name of instance using instance number"""
if not is_main_process():
pid_name = f"-{mp.current_process().pid}"
else:
pid_name = ""
self.name = f"{self._base_name}-{self.cls_instances()}{pid_name}"
def has_register(self, register: Register) -> bool:
"""
Test if this circuit has the register r.
Args:
register (Register): a quantum or classical register.
Returns:
bool: True if the register is contained in this circuit.
"""
has_reg = False
if isinstance(register, QuantumRegister) and register in self.qregs:
has_reg = True
elif isinstance(register, ClassicalRegister) and register in self.cregs:
has_reg = True
return has_reg
def reverse_ops(self) -> "QuantumCircuit":
"""Reverse the circuit by reversing the order of instructions.
This is done by recursively reversing all instructions.
It does not invert (adjoint) any gate.
Returns:
QuantumCircuit: the reversed circuit.
Examples:
input:
.. parsed-literal::
┌───┐
q_0: ┤ H ├─────■──────
└───┘┌────┴─────┐
q_1: ─────┤ RX(1.57) ├
└──────────┘
output:
.. parsed-literal::
┌───┐
q_0: ─────■──────┤ H ├
┌────┴─────┐└───┘
q_1: ┤ RX(1.57) ├─────
└──────────┘
"""
reverse_circ = QuantumCircuit(
self.qubits, self.clbits, *self.qregs, *self.cregs, name=self.name + "_reverse"
)
for instruction in reversed(self.data):
reverse_circ._append(instruction.replace(operation=instruction.operation.reverse_ops()))
reverse_circ.duration = self.duration
reverse_circ.unit = self.unit
return reverse_circ
def reverse_bits(self) -> "QuantumCircuit":
"""Return a circuit with the opposite order of wires.
The circuit is "vertically" flipped. If a circuit is
defined over multiple registers, the resulting circuit will have
the same registers but with their order flipped.
This method is useful for converting a circuit written in little-endian
convention to the big-endian equivalent, and vice versa.
Returns:
QuantumCircuit: the circuit with reversed bit order.
Examples:
input:
.. parsed-literal::
┌───┐
a_0: ┤ H ├──■─────────────────
└───┘┌─┴─┐
a_1: ─────┤ X ├──■────────────
└───┘┌─┴─┐
a_2: ──────────┤ X ├──■───────
└───┘┌─┴─┐
b_0: ───────────────┤ X ├──■──
└───┘┌─┴─┐
b_1: ────────────────────┤ X ├
└───┘
output:
.. parsed-literal::
┌───┐
b_0: ────────────────────┤ X ├
┌───┐└─┬─┘
b_1: ───────────────┤ X ├──■──
┌───┐└─┬─┘
a_0: ──────────┤ X ├──■───────
┌───┐└─┬─┘
a_1: ─────┤ X ├──■────────────
┌───┐└─┬─┘
a_2: ┤ H ├──■─────────────────
└───┘
"""
circ = QuantumCircuit(
list(reversed(self.qubits)),
list(reversed(self.clbits)),
name=self.name,
global_phase=self.global_phase,
)
new_qubit_map = circ.qubits[::-1]
new_clbit_map = circ.clbits[::-1]
for reg in reversed(self.qregs):
bits = [new_qubit_map[self.find_bit(qubit).index] for qubit in reversed(reg)]
circ.add_register(QuantumRegister(bits=bits, name=reg.name))
for reg in reversed(self.cregs):
bits = [new_clbit_map[self.find_bit(clbit).index] for clbit in reversed(reg)]
circ.add_register(ClassicalRegister(bits=bits, name=reg.name))
for instruction in self.data:
qubits = [new_qubit_map[self.find_bit(qubit).index] for qubit in instruction.qubits]
clbits = [new_clbit_map[self.find_bit(clbit).index] for clbit in instruction.clbits]
circ._append(instruction.replace(qubits=qubits, clbits=clbits))
return circ
def inverse(self) -> "QuantumCircuit":
"""Invert (take adjoint of) this circuit.
This is done by recursively inverting all gates.
Returns:
QuantumCircuit: the inverted circuit
Raises:
CircuitError: if the circuit cannot be inverted.
Examples:
input:
.. parsed-literal::
┌───┐
q_0: ┤ H ├─────■──────
└───┘┌────┴─────┐
q_1: ─────┤ RX(1.57) ├
└──────────┘
output:
.. parsed-literal::
┌───┐
q_0: ──────■──────┤ H ├
┌─────┴─────┐└───┘
q_1: ┤ RX(-1.57) ├─────
└───────────┘
"""
inverse_circ = QuantumCircuit(
self.qubits,
self.clbits,
*self.qregs,
*self.cregs,
name=self.name + "_dg",
global_phase=-self.global_phase,
)
for instruction in reversed(self._data):
inverse_circ._append(instruction.replace(operation=instruction.operation.inverse()))
return inverse_circ
def repeat(self, reps: int) -> "QuantumCircuit":
"""Repeat this circuit ``reps`` times.
Args:
reps (int): How often this circuit should be repeated.
Returns:
QuantumCircuit: A circuit containing ``reps`` repetitions of this circuit.
"""
repeated_circ = QuantumCircuit(
self.qubits, self.clbits, *self.qregs, *self.cregs, name=self.name + f"**{reps}"
)
# benefit of appending instructions: decomposing shows the subparts, i.e. the power
# is actually `reps` times this circuit, and it is currently much faster than `compose`.
if reps > 0:
try: # try to append as gate if possible to not disallow to_gate
inst: Instruction = self.to_gate()
except QiskitError:
inst = self.to_instruction()
for _ in range(reps):
repeated_circ._append(inst, self.qubits, self.clbits)
return repeated_circ
def power(self, power: float, matrix_power: bool = False) -> "QuantumCircuit":
"""Raise this circuit to the power of ``power``.
If ``power`` is a positive integer and ``matrix_power`` is ``False``, this implementation
defaults to calling ``repeat``. Otherwise, if the circuit is unitary, the matrix is
computed to calculate the matrix power.
Args:
power (float): The power to raise this circuit to.
matrix_power (bool): If True, the circuit is converted to a matrix and then the
matrix power is computed. If False, and ``power`` is a positive integer,
the implementation defaults to ``repeat``.
Raises:
CircuitError: If the circuit needs to be converted to a gate but it is not unitary.
Returns:
QuantumCircuit: A circuit implementing this circuit raised to the power of ``power``.
"""
if power >= 0 and isinstance(power, (int, np.integer)) and not matrix_power:
return self.repeat(power)
# attempt conversion to gate
if self.num_parameters > 0:
raise CircuitError(
"Cannot raise a parameterized circuit to a non-positive power "
"or matrix-power, please bind the free parameters: "
"{}".format(self.parameters)
)
try:
gate = self.to_gate()
except QiskitError as ex:
raise CircuitError(
"The circuit contains non-unitary operations and cannot be "
"controlled. Note that no qiskit.circuit.Instruction objects may "
"be in the circuit for this operation."
) from ex
power_circuit = QuantumCircuit(self.qubits, self.clbits, *self.qregs, *self.cregs)
power_circuit.append(gate.power(power), list(range(gate.num_qubits)))
return power_circuit
def control(
self,
num_ctrl_qubits: int = 1,
label: str | None = None,
ctrl_state: str | int | None = None,
) -> "QuantumCircuit":
"""Control this circuit on ``num_ctrl_qubits`` qubits.
Args:
num_ctrl_qubits (int): The number of control qubits.
label (str): An optional label to give the controlled operation for visualization.
ctrl_state (str or int): The control state in decimal or as a bitstring
(e.g. '111'). If None, use ``2**num_ctrl_qubits - 1``.
Returns:
QuantumCircuit: The controlled version of this circuit.
Raises:
CircuitError: If the circuit contains a non-unitary operation and cannot be controlled.
"""
try:
gate = self.to_gate()
except QiskitError as ex:
raise CircuitError(
"The circuit contains non-unitary operations and cannot be "
"controlled. Note that no qiskit.circuit.Instruction objects may "
"be in the circuit for this operation."
) from ex
controlled_gate = gate.control(num_ctrl_qubits, label, ctrl_state)
control_qreg = QuantumRegister(num_ctrl_qubits)
controlled_circ = QuantumCircuit(
control_qreg, self.qubits, *self.qregs, name=f"c_{self.name}"
)
controlled_circ.append(controlled_gate, controlled_circ.qubits)
return controlled_circ
def compose(
self,
other: Union["QuantumCircuit", Instruction],
qubits: QubitSpecifier | Sequence[QubitSpecifier] | None = None,
clbits: ClbitSpecifier | Sequence[ClbitSpecifier] | None = None,
front: bool = False,
inplace: bool = False,
wrap: bool = False,
) -> Optional["QuantumCircuit"]:
"""Compose circuit with ``other`` circuit or instruction, optionally permuting wires.
``other`` can be narrower or of equal width to ``self``.
Args:
other (qiskit.circuit.Instruction or QuantumCircuit):
(sub)circuit or instruction to compose onto self. If not a :obj:`.QuantumCircuit`,
this can be anything that :obj:`.append` will accept.
qubits (list[Qubit|int]): qubits of self to compose onto.
clbits (list[Clbit|int]): clbits of self to compose onto.
front (bool): If True, front composition will be performed. This is not possible within
control-flow builder context managers.
inplace (bool): If True, modify the object. Otherwise return composed circuit.
wrap (bool): If True, wraps the other circuit into a gate (or instruction, depending on
whether it contains only unitary instructions) before composing it onto self.
Returns:
QuantumCircuit: the composed circuit (returns None if inplace==True).
Raises:
CircuitError: if no correct wire mapping can be made between the two circuits, such as
if ``other`` is wider than ``self``.
CircuitError: if trying to emit a new circuit while ``self`` has a partially built
control-flow context active, such as the context-manager forms of :meth:`if_test`,
:meth:`for_loop` and :meth:`while_loop`.
CircuitError: if trying to compose to the front of a circuit when a control-flow builder
block is active; there is no clear meaning to this action.
Examples:
.. code-block:: python
>>> lhs.compose(rhs, qubits=[3, 2], inplace=True)
.. parsed-literal::
┌───┐ ┌─────┐ ┌───┐
lqr_1_0: ───┤ H ├─── rqr_0: ──■──┤ Tdg ├ lqr_1_0: ───┤ H ├───────────────
├───┤ ┌─┴─┐└─────┘ ├───┤
lqr_1_1: ───┤ X ├─── rqr_1: ┤ X ├─────── lqr_1_1: ───┤ X ├───────────────
┌──┴───┴──┐ └───┘ ┌──┴───┴──┐┌───┐
lqr_1_2: ┤ U1(0.1) ├ + = lqr_1_2: ┤ U1(0.1) ├┤ X ├───────
└─────────┘ └─────────┘└─┬─┘┌─────┐
lqr_2_0: ─────■───── lqr_2_0: ─────■───────■──┤ Tdg ├
┌─┴─┐ ┌─┴─┐ └─────┘
lqr_2_1: ───┤ X ├─── lqr_2_1: ───┤ X ├───────────────
└───┘ └───┘
lcr_0: 0 ═══════════ lcr_0: 0 ═══════════════════════
lcr_1: 0 ═══════════ lcr_1: 0 ═══════════════════════
"""
# pylint: disable=cyclic-import
from qiskit.circuit.controlflow.switch_case import SwitchCaseOp
if inplace and front and self._control_flow_scopes:
# If we're composing onto ourselves while in a stateful control-flow builder context,
# there's no clear meaning to composition to the "front" of the circuit.
raise CircuitError(
"Cannot compose to the front of a circuit while a control-flow context is active."
)
if not inplace and self._control_flow_scopes:
# If we're inside a stateful control-flow builder scope, even if we successfully cloned
# the partial builder scope (not simple), the scope wouldn't be controlled by an active
# `with` statement, so the output circuit would be permanently broken.
raise CircuitError(
"Cannot emit a new composed circuit while a control-flow context is active."
)
dest = self if inplace else self.copy()
# As a special case, allow composing some clbits onto no clbits - normally the destination
# has to be strictly larger. This allows composing final measurements onto unitary circuits.
if isinstance(other, QuantumCircuit):
if not self.clbits and other.clbits:
dest.add_bits(other.clbits)
for reg in other.cregs:
dest.add_register(reg)
if wrap and isinstance(other, QuantumCircuit):
other = (
other.to_gate()
if all(isinstance(ins.operation, Gate) for ins in other.data)
else other.to_instruction()
)
if not isinstance(other, QuantumCircuit):
if qubits is None:
qubits = self.qubits[: other.num_qubits]
if clbits is None:
clbits = self.clbits[: other.num_clbits]
if front:
# Need to keep a reference to the data for use after we've emptied it.
old_data = list(dest.data)
dest.clear()
dest.append(other, qubits, clbits)
for instruction in old_data:
dest._append(instruction)
else:
dest.append(other, qargs=qubits, cargs=clbits)
if inplace:
return None
return dest
if other.num_qubits > dest.num_qubits or other.num_clbits > dest.num_clbits:
raise CircuitError(
"Trying to compose with another QuantumCircuit which has more 'in' edges."
)
# number of qubits and clbits must match number in circuit or None
edge_map: dict[Qubit | Clbit, Qubit | Clbit] = {}
if qubits is None:
edge_map.update(zip(other.qubits, dest.qubits))
else:
mapped_qubits = dest.qbit_argument_conversion(qubits)
if len(mapped_qubits) != len(other.qubits):
raise CircuitError(
f"Number of items in qubits parameter ({len(mapped_qubits)}) does not"
f" match number of qubits in the circuit ({len(other.qubits)})."
)
edge_map.update(zip(other.qubits, mapped_qubits))
if clbits is None:
edge_map.update(zip(other.clbits, dest.clbits))
else:
mapped_clbits = dest.cbit_argument_conversion(clbits)
if len(mapped_clbits) != len(other.clbits):
raise CircuitError(
f"Number of items in clbits parameter ({len(mapped_clbits)}) does not"
f" match number of clbits in the circuit ({len(other.clbits)})."
)
edge_map.update(zip(other.clbits, dest.cbit_argument_conversion(clbits)))
variable_mapper = _classical_resource_map.VariableMapper(
dest.cregs, edge_map, dest.add_register
)
mapped_instrs: list[CircuitInstruction] = []
for instr in other.data:
n_qargs: list[Qubit] = [edge_map[qarg] for qarg in instr.qubits]
n_cargs: list[Clbit] = [edge_map[carg] for carg in instr.clbits]
n_op = instr.operation.copy()
if (condition := getattr(n_op, "condition", None)) is not None:
n_op.condition = variable_mapper.map_condition(condition)
if isinstance(n_op, SwitchCaseOp):
n_op.target = variable_mapper.map_target(n_op.target)
mapped_instrs.append(CircuitInstruction(n_op, n_qargs, n_cargs))
if front:
# adjust new instrs before original ones and update all parameters
mapped_instrs += dest.data
dest.clear()
append = dest._control_flow_scopes[-1].append if dest._control_flow_scopes else dest._append
for instr in mapped_instrs:
append(instr)
for gate, cals in other.calibrations.items():
dest._calibrations[gate].update(cals)
dest.global_phase += other.global_phase
if inplace:
return None
return dest
def tensor(self, other: "QuantumCircuit", inplace: bool = False) -> Optional["QuantumCircuit"]:
"""Tensor ``self`` with ``other``.
Remember that in the little-endian convention the leftmost operation will be at the bottom
of the circuit. See also
`the docs <qiskit.org/documentation/tutorials/circuits/3_summary_of_quantum_operations.html>`__
for more information.
.. parsed-literal::
┌────────┐ ┌─────┐ ┌─────┐
q_0: ┤ bottom ├ ⊗ q_0: ┤ top ├ = q_0: ─┤ top ├──
└────────┘ └─────┘ ┌┴─────┴─┐
q_1: ┤ bottom ├
└────────┘
Args:
other (QuantumCircuit): The other circuit to tensor this circuit with.
inplace (bool): If True, modify the object. Otherwise return composed circuit.
Examples:
.. plot::
:include-source:
from qiskit import QuantumCircuit
top = QuantumCircuit(1)
top.x(0);
bottom = QuantumCircuit(2)
bottom.cry(0.2, 0, 1);
tensored = bottom.tensor(top)
tensored.draw('mpl')
Returns:
QuantumCircuit: The tensored circuit (returns None if inplace==True).
"""
num_qubits = self.num_qubits + other.num_qubits
num_clbits = self.num_clbits + other.num_clbits
# If a user defined both circuits with via register sizes and not with named registers
# (e.g. QuantumCircuit(2, 2)) then we have a naming collision, as the registers are by
# default called "q" resp. "c". To still allow tensoring we define new registers of the
# correct sizes.
if (
len(self.qregs) == len(other.qregs) == 1
and self.qregs[0].name == other.qregs[0].name == "q"
):
# check if classical registers are in the circuit
if num_clbits > 0:
dest = QuantumCircuit(num_qubits, num_clbits)
else:
dest = QuantumCircuit(num_qubits)
# handle case if ``measure_all`` was called on both circuits, in which case the
# registers are both named "meas"
elif (
len(self.cregs) == len(other.cregs) == 1
and self.cregs[0].name == other.cregs[0].name == "meas"
):
cr = ClassicalRegister(self.num_clbits + other.num_clbits, "meas")
dest = QuantumCircuit(*other.qregs, *self.qregs, cr)
# Now we don't have to handle any more cases arising from special implicit naming
else:
dest = QuantumCircuit(
other.qubits,
self.qubits,
other.clbits,
self.clbits,
*other.qregs,
*self.qregs,
*other.cregs,
*self.cregs,
)
# compose self onto the output, and then other
dest.compose(other, range(other.num_qubits), range(other.num_clbits), inplace=True)
dest.compose(
self,
range(other.num_qubits, num_qubits),
range(other.num_clbits, num_clbits),
inplace=True,
)
# Replace information from tensored circuit into self when inplace = True
if inplace:
self.__dict__.update(dest.__dict__)
return None
return dest
@property
def qubits(self) -> list[Qubit]:
"""
Returns a list of quantum bits in the order that the registers were added.
"""
return self._qubits
@property
def clbits(self) -> list[Clbit]:
"""
Returns a list of classical bits in the order that the registers were added.
"""
return self._clbits
@property
def ancillas(self) -> list[AncillaQubit]:
"""
Returns a list of ancilla bits in the order that the registers were added.
"""
return self._ancillas
def __and__(self, rhs: "QuantumCircuit") -> "QuantumCircuit":
"""Overload & to implement self.compose."""
return self.compose(rhs)
def __iand__(self, rhs: "QuantumCircuit") -> "QuantumCircuit":
"""Overload &= to implement self.compose in place."""
self.compose(rhs, inplace=True)
return self
def __xor__(self, top: "QuantumCircuit") -> "QuantumCircuit":
"""Overload ^ to implement self.tensor."""
return self.tensor(top)
def __ixor__(self, top: "QuantumCircuit") -> "QuantumCircuit":
"""Overload ^= to implement self.tensor in place."""
self.tensor(top, inplace=True)
return self
def __len__(self) -> int:
"""Return number of operations in circuit."""
return len(self._data)
@typing.overload
def __getitem__(self, item: int) -> CircuitInstruction:
...
@typing.overload
def __getitem__(self, item: slice) -> list[CircuitInstruction]:
...
def __getitem__(self, item):
"""Return indexed operation."""
return self._data[item]
@staticmethod
def cast(value: S, type_: Callable[..., T]) -> Union[S, T]:
"""Best effort to cast value to type. Otherwise, returns the value."""
try:
return type_(value)
except (ValueError, TypeError):
return value
def qbit_argument_conversion(self, qubit_representation: QubitSpecifier) -> list[Qubit]:
"""
Converts several qubit representations (such as indexes, range, etc.)
into a list of qubits.
Args:
qubit_representation (Object): representation to expand
Returns:
List(Qubit): the resolved instances of the qubits.
"""
return _bit_argument_conversion(
qubit_representation, self.qubits, self._qubit_indices, Qubit
)
def cbit_argument_conversion(self, clbit_representation: ClbitSpecifier) -> list[Clbit]:
"""
Converts several classical bit representations (such as indexes, range, etc.)
into a list of classical bits.
Args:
clbit_representation (Object): representation to expand
Returns:
List(tuple): Where each tuple is a classical bit.
"""
return _bit_argument_conversion(
clbit_representation, self.clbits, self._clbit_indices, Clbit
)
def _resolve_classical_resource(self, specifier):
"""Resolve a single classical resource specifier into a concrete resource, raising an error
if the specifier is invalid.
This is slightly different to :meth:`.cbit_argument_conversion`, because it should not
unwrap :obj:`.ClassicalRegister` instances into lists, and in general it should not allow
iterables or broadcasting. It is expected to be used as a callback for things like
:meth:`.InstructionSet.c_if` to check the validity of their arguments.
Args:
specifier (Union[Clbit, ClassicalRegister, int]): a specifier of a classical resource
present in this circuit. An ``int`` will be resolved into a :obj:`.Clbit` using the
same conventions as measurement operations on this circuit use.
Returns:
Union[Clbit, ClassicalRegister]: the resolved resource.
Raises:
CircuitError: if the resource is not present in this circuit, or if the integer index
passed is out-of-bounds.
"""
if isinstance(specifier, Clbit):
if specifier not in self._clbit_indices:
raise CircuitError(f"Clbit {specifier} is not present in this circuit.")
return specifier
if isinstance(specifier, ClassicalRegister):
# This is linear complexity for something that should be constant, but QuantumCircuit
# does not currently keep a hashmap of registers, and requires non-trivial changes to
# how it exposes its registers publically before such a map can be safely stored so it
# doesn't miss updates. (Jake, 2021-11-10).
if specifier not in self.cregs:
raise CircuitError(f"Register {specifier} is not present in this circuit.")
return specifier
if isinstance(specifier, int):
try:
return self._clbits[specifier]
except IndexError:
raise CircuitError(f"Classical bit index {specifier} is out-of-range.") from None
raise CircuitError(f"Unknown classical resource specifier: '{specifier}'.")
def _validate_expr(self, node: expr.Expr) -> expr.Expr:
for var in expr.iter_vars(node):
if isinstance(var.var, Clbit):
if var.var not in self._clbit_indices:
raise CircuitError(f"Clbit {var.var} is not present in this circuit.")
elif isinstance(var.var, ClassicalRegister):
if var.var not in self.cregs:
raise CircuitError(f"Register {var.var} is not present in this circuit.")
return node
def append(
self,
instruction: Operation | CircuitInstruction,
qargs: Sequence[QubitSpecifier] | None = None,
cargs: Sequence[ClbitSpecifier] | None = None,
) -> InstructionSet:
"""Append one or more instructions to the end of the circuit, modifying the circuit in
place.
The ``qargs`` and ``cargs`` will be expanded and broadcast according to the rules of the
given :class:`~.circuit.Instruction`, and any non-:class:`.Bit` specifiers (such as
integer indices) will be resolved into the relevant instances.
If a :class:`.CircuitInstruction` is given, it will be unwrapped, verified in the context of
this circuit, and a new object will be appended to the circuit. In this case, you may not
pass ``qargs`` or ``cargs`` separately.
Args:
instruction: :class:`~.circuit.Instruction` instance to append, or a
:class:`.CircuitInstruction` with all its context.
qargs: specifiers of the :class:`.Qubit`\\ s to attach instruction to.
cargs: specifiers of the :class:`.Clbit`\\ s to attach instruction to.
Returns:
qiskit.circuit.InstructionSet: a handle to the :class:`.CircuitInstruction`\\ s that
were actually added to the circuit.
Raises:
CircuitError: if the operation passed is not an instance of :class:`~.circuit.Instruction` .
"""
if isinstance(instruction, CircuitInstruction):
operation = instruction.operation
qargs = instruction.qubits
cargs = instruction.clbits
else:
operation = instruction
# Convert input to instruction
if not isinstance(operation, Operation):
if hasattr(operation, "to_instruction"):
operation = operation.to_instruction()
if not isinstance(operation, Operation):
raise CircuitError("operation.to_instruction() is not an Operation.")
else:
if issubclass(operation, Operation):
raise CircuitError(
"Object is a subclass of Operation, please add () to "
"pass an instance of this object."
)
raise CircuitError(
"Object to append must be an Operation or have a to_instruction() method."
)
# Make copy of parameterized gate instances
if hasattr(operation, "params"):
is_parameter = any(isinstance(param, Parameter) for param in operation.params)
if is_parameter:
operation = copy.deepcopy(operation)
expanded_qargs = [self.qbit_argument_conversion(qarg) for qarg in qargs or []]
expanded_cargs = [self.cbit_argument_conversion(carg) for carg in cargs or []]
if self._control_flow_scopes:
appender = self._control_flow_scopes[-1].append
requester = self._control_flow_scopes[-1].request_classical_resource
else:
appender = self._append
requester = self._resolve_classical_resource
instructions = InstructionSet(resource_requester=requester)
if isinstance(operation, Instruction):
for qarg, carg in operation.broadcast_arguments(expanded_qargs, expanded_cargs):
self._check_dups(qarg)
instruction = CircuitInstruction(operation, qarg, carg)
appender(instruction)
instructions.add(instruction)
else:
# For Operations that are non-Instructions, we use the Instruction's default method
for qarg, carg in Instruction.broadcast_arguments(
operation, expanded_qargs, expanded_cargs
):
self._check_dups(qarg)
instruction = CircuitInstruction(operation, qarg, carg)
appender(instruction)
instructions.add(instruction)
return instructions
# Preferred new style.
@typing.overload
def _append(
self, instruction: CircuitInstruction, _qargs: None = None, _cargs: None = None
) -> CircuitInstruction:
...
# To-be-deprecated old style.
@typing.overload
def _append(
self,
operation: Operation,
qargs: Sequence[Qubit],
cargs: Sequence[Clbit],
) -> Operation:
...
def _append(
self,
instruction: CircuitInstruction | Instruction,
qargs: Sequence[Qubit] | None = None,
cargs: Sequence[Clbit] | None = None,
):
"""Append an instruction to the end of the circuit, modifying the circuit in place.
.. warning::
This is an internal fast-path function, and it is the responsibility of the caller to
ensure that all the arguments are valid; there is no error checking here. In
particular, all the qubits and clbits must already exist in the circuit and there can be
no duplicates in the list.
.. note::
This function may be used by callers other than :obj:`.QuantumCircuit` when the caller
is sure that all error-checking, broadcasting and scoping has already been performed,
and the only reference to the circuit the instructions are being appended to is within
that same function. In particular, it is not safe to call
:meth:`QuantumCircuit._append` on a circuit that is received by a function argument.
This is because :meth:`.QuantumCircuit._append` will not recognise the scoping
constructs of the control-flow builder interface.
Args:
instruction: Operation instance to append
qargs: Qubits to attach the instruction to.
cargs: Clbits to attach the instruction to.
Returns:
Operation: a handle to the instruction that was just added
:meta public:
"""
old_style = not isinstance(instruction, CircuitInstruction)
if old_style:
instruction = CircuitInstruction(instruction, qargs, cargs)
self._data.append(instruction)
if isinstance(instruction.operation, Instruction):
self._update_parameter_table(instruction)
# mark as normal circuit if a new instruction is added
self.duration = None
self.unit = "dt"
return instruction.operation if old_style else instruction
def _update_parameter_table(self, instruction: CircuitInstruction):
for param_index, param in enumerate(instruction.operation.params):
if isinstance(param, (ParameterExpression, QuantumCircuit)):
# Scoped constructs like the control-flow ops use QuantumCircuit as a parameter.
atomic_parameters = set(param.parameters)
else:
atomic_parameters = set()
for parameter in atomic_parameters:
if parameter in self._parameter_table:
self._parameter_table[parameter].add((instruction.operation, param_index))
else:
if parameter.name in self._parameter_table.get_names():
raise CircuitError(f"Name conflict on adding parameter: {parameter.name}")
self._parameter_table[parameter] = ParameterReferences(
((instruction.operation, param_index),)
)
# clear cache if new parameter is added
self._parameters = None
def add_register(self, *regs: Register | int | Sequence[Bit]) -> None:
"""Add registers."""
if not regs:
return
if any(isinstance(reg, int) for reg in regs):
# QuantumCircuit defined without registers
if len(regs) == 1 and isinstance(regs[0], int):
# QuantumCircuit with anonymous quantum wires e.g. QuantumCircuit(2)
if regs[0] == 0:
regs = ()
else:
regs = (QuantumRegister(regs[0], "q"),)
elif len(regs) == 2 and all(isinstance(reg, int) for reg in regs):
# QuantumCircuit with anonymous wires e.g. QuantumCircuit(2, 3)
if regs[0] == 0:
qregs: tuple[QuantumRegister, ...] = ()
else:
qregs = (QuantumRegister(regs[0], "q"),)
if regs[1] == 0:
cregs: tuple[ClassicalRegister, ...] = ()
else:
cregs = (ClassicalRegister(regs[1], "c"),)
regs = qregs + cregs
else:
raise CircuitError(
"QuantumCircuit parameters can be Registers or Integers."
" If Integers, up to 2 arguments. QuantumCircuit was called"
" with %s." % (regs,)
)
for register in regs:
if isinstance(register, Register) and any(
register.name == reg.name for reg in self.qregs + self.cregs
):
raise CircuitError('register name "%s" already exists' % register.name)
if isinstance(register, AncillaRegister):
for bit in register:
if bit not in self._qubit_indices:
self._ancillas.append(bit)
if isinstance(register, QuantumRegister):
self.qregs.append(register)
for idx, bit in enumerate(register):
if bit in self._qubit_indices:
self._qubit_indices[bit].registers.append((register, idx))
else:
self._qubits.append(bit)
self._qubit_indices[bit] = BitLocations(
len(self._qubits) - 1, [(register, idx)]
)
elif isinstance(register, ClassicalRegister):
self.cregs.append(register)
for idx, bit in enumerate(register):
if bit in self._clbit_indices:
self._clbit_indices[bit].registers.append((register, idx))
else:
self._clbits.append(bit)
self._clbit_indices[bit] = BitLocations(
len(self._clbits) - 1, [(register, idx)]
)
elif isinstance(register, list):
self.add_bits(register)
else:
raise CircuitError("expected a register")
def add_bits(self, bits: Iterable[Bit]) -> None:
"""Add Bits to the circuit."""
duplicate_bits = set(self._qubit_indices).union(self._clbit_indices).intersection(bits)
if duplicate_bits:
raise CircuitError(f"Attempted to add bits found already in circuit: {duplicate_bits}")
for bit in bits:
if isinstance(bit, AncillaQubit):
self._ancillas.append(bit)
if isinstance(bit, Qubit):
self._qubits.append(bit)
self._qubit_indices[bit] = BitLocations(len(self._qubits) - 1, [])
elif isinstance(bit, Clbit):
self._clbits.append(bit)
self._clbit_indices[bit] = BitLocations(len(self._clbits) - 1, [])
else:
raise CircuitError(
"Expected an instance of Qubit, Clbit, or "
"AncillaQubit, but was passed {}".format(bit)
)
def find_bit(self, bit: Bit) -> BitLocations:
"""Find locations in the circuit which can be used to reference a given :obj:`~Bit`.
Args:
bit (Bit): The bit to locate.
Returns:
namedtuple(int, List[Tuple(Register, int)]): A 2-tuple. The first element (``index``)
contains the index at which the ``Bit`` can be found (in either
:obj:`~QuantumCircuit.qubits`, :obj:`~QuantumCircuit.clbits`, depending on its
type). The second element (``registers``) is a list of ``(register, index)``
pairs with an entry for each :obj:`~Register` in the circuit which contains the
:obj:`~Bit` (and the index in the :obj:`~Register` at which it can be found).
Notes:
The circuit index of an :obj:`~AncillaQubit` will be its index in
:obj:`~QuantumCircuit.qubits`, not :obj:`~QuantumCircuit.ancillas`.
Raises:
CircuitError: If the supplied :obj:`~Bit` was of an unknown type.
CircuitError: If the supplied :obj:`~Bit` could not be found on the circuit.
"""
try:
if isinstance(bit, Qubit):
return self._qubit_indices[bit]
elif isinstance(bit, Clbit):
return self._clbit_indices[bit]
else:
raise CircuitError(f"Could not locate bit of unknown type: {type(bit)}")
except KeyError as err:
raise CircuitError(
f"Could not locate provided bit: {bit}. Has it been added to the QuantumCircuit?"
) from err
def _check_dups(self, qubits: Sequence[Qubit]) -> None:
"""Raise exception if list of qubits contains duplicates."""
squbits = set(qubits)
if len(squbits) != len(qubits):
raise CircuitError("duplicate qubit arguments")
def to_instruction(
self,
parameter_map: dict[Parameter, ParameterValueType] | None = None,
label: str | None = None,
) -> Instruction:
"""Create an Instruction out of this circuit.
Args:
parameter_map(dict): For parameterized circuits, a mapping from
parameters in the circuit to parameters to be used in the
instruction. If None, existing circuit parameters will also
parameterize the instruction.
label (str): Optional gate label.
Returns:
qiskit.circuit.Instruction: a composite instruction encapsulating this circuit
(can be decomposed back)
"""
from qiskit.converters.circuit_to_instruction import circuit_to_instruction
return circuit_to_instruction(self, parameter_map, label=label)
def to_gate(
self,
parameter_map: dict[Parameter, ParameterValueType] | None = None,
label: str | None = None,
) -> Gate:
"""Create a Gate out of this circuit.
Args:
parameter_map(dict): For parameterized circuits, a mapping from
parameters in the circuit to parameters to be used in the
gate. If None, existing circuit parameters will also
parameterize the gate.
label (str): Optional gate label.
Returns:
Gate: a composite gate encapsulating this circuit
(can be decomposed back)
"""
from qiskit.converters.circuit_to_gate import circuit_to_gate
return circuit_to_gate(self, parameter_map, label=label)
def decompose(
self,
gates_to_decompose: Type[Gate] | Sequence[Type[Gate]] | Sequence[str] | str | None = None,
reps: int = 1,
) -> "QuantumCircuit":
"""Call a decomposition pass on this circuit,
to decompose one level (shallow decompose).
Args:
gates_to_decompose (type or str or list(type, str)): Optional subset of gates
to decompose. Can be a gate type, such as ``HGate``, or a gate name, such
as 'h', or a gate label, such as 'My H Gate', or a list of any combination
of these. If a gate name is entered, it will decompose all gates with that
name, whether the gates have labels or not. Defaults to all gates in circuit.
reps (int): Optional number of times the circuit should be decomposed.
For instance, ``reps=2`` equals calling ``circuit.decompose().decompose()``.
can decompose specific gates specific time
Returns:
QuantumCircuit: a circuit one level decomposed
"""
# pylint: disable=cyclic-import
from qiskit.transpiler.passes.basis.decompose import Decompose
from qiskit.transpiler.passes.synthesis import HighLevelSynthesis
from qiskit.converters.circuit_to_dag import circuit_to_dag
from qiskit.converters.dag_to_circuit import dag_to_circuit
dag = circuit_to_dag(self)
dag = HighLevelSynthesis().run(dag)
pass_ = Decompose(gates_to_decompose)
for _ in range(reps):
dag = pass_.run(dag)
return dag_to_circuit(dag)
def qasm(
self,
formatted: bool = False,
filename: str | None = None,
encoding: str | None = None,
) -> str | None:
"""Return OpenQASM string.
Args:
formatted (bool): Return formatted Qasm string.
filename (str): Save Qasm to file with name 'filename'.
encoding (str): Optionally specify the encoding to use for the
output file if ``filename`` is specified. By default this is
set to the system's default encoding (ie whatever
``locale.getpreferredencoding()`` returns) and can be set to
any valid codec or alias from stdlib's
`codec module <https://docs.python.org/3/library/codecs.html#standard-encodings>`__
Returns:
str: If formatted=False.
Raises:
MissingOptionalLibraryError: If pygments is not installed and ``formatted`` is
``True``.
QASM2ExportError: If circuit has free parameters.
QASM2ExportError: If an operation that has no OpenQASM 2 representation is encountered.
"""
from qiskit.qasm2 import QASM2ExportError # pylint: disable=cyclic-import
if self.num_parameters > 0:
raise QASM2ExportError(
"Cannot represent circuits with unbound parameters in OpenQASM 2."
)
existing_gate_names = {
"barrier",
"measure",
"reset",
"u3",
"u2",
"u1",
"cx",
"id",
"u0",
"u",
"p",
"x",
"y",
"z",
"h",
"s",
"sdg",
"t",
"tdg",
"rx",
"ry",
"rz",
"sx",
"sxdg",
"cz",
"cy",
"swap",
"ch",
"ccx",
"cswap",
"crx",
"cry",
"crz",
"cu1",
"cp",
"cu3",
"csx",
"cu",
"rxx",
"rzz",
"rccx",
"rc3x",
"c3x",
"c3sx", # This is the Qiskit gate name, but the qelib1.inc name is 'c3sqrtx'.
"c4x",
}
# Mapping of instruction name to a pair of the source for a definition, and an OQ2 string
# that includes the `gate` or `opaque` statement that defines the gate.
gates_to_define: OrderedDict[str, tuple[Instruction, str]] = OrderedDict()
regless_qubits = [bit for bit in self.qubits if not self.find_bit(bit).registers]
regless_clbits = [bit for bit in self.clbits if not self.find_bit(bit).registers]
dummy_registers: list[QuantumRegister | ClassicalRegister] = []
if regless_qubits:
dummy_registers.append(QuantumRegister(name="qregless", bits=regless_qubits))
if regless_clbits:
dummy_registers.append(ClassicalRegister(name="cregless", bits=regless_clbits))
register_escaped_names: dict[str, QuantumRegister | ClassicalRegister] = {}
for regs in (self.qregs, self.cregs, dummy_registers):
for reg in regs:
register_escaped_names[
_make_unique(_qasm_escape_name(reg.name, "reg_"), register_escaped_names)
] = reg
bit_labels: dict[Qubit | Clbit, str] = {
bit: "%s[%d]" % (name, idx)
for name, register in register_escaped_names.items()
for (idx, bit) in enumerate(register)
}
register_definitions_qasm = "".join(
f"{'qreg' if isinstance(reg, QuantumRegister) else 'creg'} {name}[{reg.size}];\n"
for name, reg in register_escaped_names.items()
)
instruction_calls = []
for instruction in self._data:
operation = instruction.operation
if operation.name == "measure":
qubit = instruction.qubits[0]
clbit = instruction.clbits[0]
instruction_qasm = f"measure {bit_labels[qubit]} -> {bit_labels[clbit]};"
elif operation.name == "reset":
instruction_qasm = f"reset {bit_labels[instruction.qubits[0]]};"
elif operation.name == "barrier":
if not instruction.qubits:
# Barriers with no operands are invalid in (strict) OQ2, and the statement
# would have no meaning anyway.
continue
qargs = ",".join(bit_labels[q] for q in instruction.qubits)
instruction_qasm = "barrier;" if not qargs else f"barrier {qargs};"
else:
instruction_qasm = _qasm2_custom_operation_statement(
instruction, existing_gate_names, gates_to_define, bit_labels
)
instruction_calls.append(instruction_qasm)
instructions_qasm = "".join(f"{call}\n" for call in instruction_calls)
gate_definitions_qasm = "".join(f"{qasm}\n" for _, qasm in gates_to_define.values())
out = "".join(
(
self.header,
"\n",
self.extension_lib,
"\n",
gate_definitions_qasm,
register_definitions_qasm,
instructions_qasm,
)
)
if filename:
with open(filename, "w+", encoding=encoding) as file:
file.write(out)
if formatted:
_optionals.HAS_PYGMENTS.require_now("formatted OpenQASM 2 output")
import pygments
from pygments.formatters import ( # pylint: disable=no-name-in-module
Terminal256Formatter,
)
from qiskit.qasm.pygments import OpenQASMLexer
from qiskit.qasm.pygments import QasmTerminalStyle
code = pygments.highlight(
out, OpenQASMLexer(), Terminal256Formatter(style=QasmTerminalStyle)
)
print(code)
return None
return out
def draw(
self,
output: str | None = None,
scale: float | None = None,
filename: str | None = None,
style: dict | str | None = None,
interactive: bool = False,
plot_barriers: bool = True,
reverse_bits: bool = None,
justify: str | None = None,
vertical_compression: str | None = "medium",
idle_wires: bool = True,
with_layout: bool = True,
fold: int | None = None,
# The type of ax is matplotlib.axes.Axes, but this is not a fixed dependency, so cannot be
# safely forward-referenced.
ax: Any | None = None,
initial_state: bool = False,
cregbundle: bool = None,
wire_order: list = None,
):
"""Draw the quantum circuit. Use the output parameter to choose the drawing format:
**text**: ASCII art TextDrawing that can be printed in the console.
**mpl**: images with color rendered purely in Python using matplotlib.
**latex**: high-quality images compiled via latex.
**latex_source**: raw uncompiled latex output.
.. warning::
Support for :class:`~.expr.Expr` nodes in conditions and :attr:`.SwitchCaseOp.target`
fields is preliminary and incomplete. The ``text`` and ``mpl`` drawers will make a
best-effort attempt to show data dependencies, but the LaTeX-based drawers will skip
these completely.
Args:
output (str): select the output method to use for drawing the circuit.
Valid choices are ``text``, ``mpl``, ``latex``, ``latex_source``.
By default the `text` drawer is used unless the user config file
(usually ``~/.qiskit/settings.conf``) has an alternative backend set
as the default. For example, ``circuit_drawer = latex``. If the output
kwarg is set, that backend will always be used over the default in
the user config file.
scale (float): scale of image to draw (shrink if < 1.0). Only used by
the `mpl`, `latex` and `latex_source` outputs. Defaults to 1.0.
filename (str): file path to save image to. Defaults to None.
style (dict or str): dictionary of style or file name of style json file.
This option is only used by the `mpl` or `latex` output type.
If `style` is a str, it is used as the path to a json file
which contains a style dict. The file will be opened, parsed, and
then any style elements in the dict will replace the default values
in the input dict. A file to be loaded must end in ``.json``, but
the name entered here can omit ``.json``. For example,
``style='iqx.json'`` or ``style='iqx'``.
If `style` is a dict and the ``'name'`` key is set, that name
will be used to load a json file, followed by loading the other
items in the style dict. For example, ``style={'name': 'iqx'}``.
If `style` is not a str and `name` is not a key in the style dict,
then the default value from the user config file (usually
``~/.qiskit/settings.conf``) will be used, for example,
``circuit_mpl_style = iqx``.
If none of these are set, the `default` style will be used.
The search path for style json files can be specified in the user
config, for example,
``circuit_mpl_style_path = /home/user/styles:/home/user``.
See: :class:`~qiskit.visualization.qcstyle.DefaultStyle` for more
information on the contents.
interactive (bool): when set to true, show the circuit in a new window
(for `mpl` this depends on the matplotlib backend being used
supporting this). Note when used with either the `text` or the
`latex_source` output type this has no effect and will be silently
ignored. Defaults to False.
reverse_bits (bool): when set to True, reverse the bit order inside
registers for the output visualization. Defaults to False unless the
user config file (usually ``~/.qiskit/settings.conf``) has an
alternative value set. For example, ``circuit_reverse_bits = True``.
plot_barriers (bool): enable/disable drawing barriers in the output
circuit. Defaults to True.
justify (string): options are ``left``, ``right`` or ``none``. If
anything else is supplied, it defaults to left justified. It refers
to where gates should be placed in the output circuit if there is
an option. ``none`` results in each gate being placed in its own
column.
vertical_compression (string): ``high``, ``medium`` or ``low``. It
merges the lines generated by the `text` output so the drawing
will take less vertical room. Default is ``medium``. Only used by
the `text` output, will be silently ignored otherwise.
idle_wires (bool): include idle wires (wires with no circuit elements)
in output visualization. Default is True.
with_layout (bool): include layout information, with labels on the
physical layout. Default is True.
fold (int): sets pagination. It can be disabled using -1. In `text`,
sets the length of the lines. This is useful when the drawing does
not fit in the console. If None (default), it will try to guess the
console width using ``shutil.get_terminal_size()``. However, if
running in jupyter, the default line length is set to 80 characters.
In `mpl`, it is the number of (visual) layers before folding.
Default is 25.
ax (matplotlib.axes.Axes): Only used by the `mpl` backend. An optional
Axes object to be used for the visualization output. If none is
specified, a new matplotlib Figure will be created and used.
Additionally, if specified there will be no returned Figure since
it is redundant.
initial_state (bool): Optional. Adds ``|0>`` in the beginning of the wire.
Default is False.
cregbundle (bool): Optional. If set True, bundle classical registers.
Default is True, except for when ``output`` is set to ``"text"``.
wire_order (list): Optional. A list of integers used to reorder the display
of the bits. The list must have an entry for every bit with the bits
in the range 0 to (``num_qubits`` + ``num_clbits``).
Returns:
:class:`.TextDrawing` or :class:`matplotlib.figure` or :class:`PIL.Image` or
:class:`str`:
* `TextDrawing` (output='text')
A drawing that can be printed as ascii art.
* `matplotlib.figure.Figure` (output='mpl')
A matplotlib figure object for the circuit diagram.
* `PIL.Image` (output='latex')
An in-memory representation of the image of the circuit diagram.
* `str` (output='latex_source')
The LaTeX source code for visualizing the circuit diagram.
Raises:
VisualizationError: when an invalid output method is selected
ImportError: when the output methods requires non-installed libraries.
Example:
.. plot::
:include-source:
from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit
q = QuantumRegister(1)
c = ClassicalRegister(1)
qc = QuantumCircuit(q, c)
qc.h(q)
qc.measure(q, c)
qc.draw(output='mpl', style={'backgroundcolor': '#EEEEEE'})
"""
# pylint: disable=cyclic-import
from qiskit.visualization import circuit_drawer
return circuit_drawer(
self,
scale=scale,
filename=filename,
style=style,
output=output,
interactive=interactive,
plot_barriers=plot_barriers,
reverse_bits=reverse_bits,
justify=justify,
vertical_compression=vertical_compression,
idle_wires=idle_wires,
with_layout=with_layout,
fold=fold,
ax=ax,
initial_state=initial_state,
cregbundle=cregbundle,
wire_order=wire_order,
)
def size(
self,
filter_function: Callable[..., int] = lambda x: not getattr(
x.operation, "_directive", False
),
) -> int:
"""Returns total number of instructions in circuit.
Args:
filter_function (callable): a function to filter out some instructions.
Should take as input a tuple of (Instruction, list(Qubit), list(Clbit)).
By default filters out "directives", such as barrier or snapshot.
Returns:
int: Total number of gate operations.
"""
return sum(map(filter_function, self._data))
def depth(
self,
filter_function: Callable[..., int] = lambda x: not getattr(
x.operation, "_directive", False
),
) -> int:
"""Return circuit depth (i.e., length of critical path).
Args:
filter_function (callable): A function to filter instructions.
Should take as input a tuple of (Instruction, list(Qubit), list(Clbit)).
Instructions for which the function returns False are ignored in the
computation of the circuit depth.
By default filters out "directives", such as barrier or snapshot.
Returns:
int: Depth of circuit.
Notes:
The circuit depth and the DAG depth need not be the
same.
"""
# Assign each bit in the circuit a unique integer
# to index into op_stack.
bit_indices: dict[Qubit | Clbit, int] = {
bit: idx for idx, bit in enumerate(self.qubits + self.clbits)
}
# If no bits, return 0
if not bit_indices:
return 0
# A list that holds the height of each qubit
# and classical bit.
op_stack = [0] * len(bit_indices)
# Here we are playing a modified version of
# Tetris where we stack gates, but multi-qubit
# gates, or measurements have a block for each
# qubit or cbit that are connected by a virtual
# line so that they all stacked at the same depth.
# Conditional gates act on all cbits in the register
# they are conditioned on.
# The max stack height is the circuit depth.
for instruction in self._data:
levels = []
reg_ints = []
for ind, reg in enumerate(instruction.qubits + instruction.clbits):
# Add to the stacks of the qubits and
# cbits used in the gate.
reg_ints.append(bit_indices[reg])
if filter_function(instruction):
levels.append(op_stack[reg_ints[ind]] + 1)
else:
levels.append(op_stack[reg_ints[ind]])
# Assuming here that there is no conditional
# snapshots or barriers ever.
if getattr(instruction.operation, "condition", None):
# Controls operate over all bits of a classical register
# or over a single bit
if isinstance(instruction.operation.condition[0], Clbit):
condition_bits = [instruction.operation.condition[0]]
else:
condition_bits = instruction.operation.condition[0]
for cbit in condition_bits:
idx = bit_indices[cbit]
if idx not in reg_ints:
reg_ints.append(idx)
levels.append(op_stack[idx] + 1)
max_level = max(levels)
for ind in reg_ints:
op_stack[ind] = max_level
return max(op_stack)
def width(self) -> int:
"""Return number of qubits plus clbits in circuit.
Returns:
int: Width of circuit.
"""
return len(self.qubits) + len(self.clbits)
@property
def num_qubits(self) -> int:
"""Return number of qubits."""
return len(self.qubits)
@property
def num_ancillas(self) -> int:
"""Return the number of ancilla qubits."""
return len(self.ancillas)
@property
def num_clbits(self) -> int:
"""Return number of classical bits."""
return len(self.clbits)
# The stringified return type is because OrderedDict can't be subscripted before Python 3.9, and
# typing.OrderedDict wasn't added until 3.7.2. It can be turned into a proper type once 3.6
# support is dropped.
def count_ops(self) -> "OrderedDict[Instruction, int]":
"""Count each operation kind in the circuit.
Returns:
OrderedDict: a breakdown of how many operations of each kind, sorted by amount.
"""
count_ops: dict[Instruction, int] = {}
for instruction in self._data:
count_ops[instruction.operation.name] = count_ops.get(instruction.operation.name, 0) + 1
return OrderedDict(sorted(count_ops.items(), key=lambda kv: kv[1], reverse=True))
def num_nonlocal_gates(self) -> int:
"""Return number of non-local gates (i.e. involving 2+ qubits).
Conditional nonlocal gates are also included.
"""
multi_qubit_gates = 0
for instruction in self._data:
if instruction.operation.num_qubits > 1 and not getattr(
instruction.operation, "_directive", False
):
multi_qubit_gates += 1
return multi_qubit_gates
def get_instructions(self, name: str) -> list[CircuitInstruction]:
"""Get instructions matching name.
Args:
name (str): The name of instruction to.
Returns:
list(tuple): list of (instruction, qargs, cargs).
"""
return [match for match in self._data if match.operation.name == name]
def num_connected_components(self, unitary_only: bool = False) -> int:
"""How many non-entangled subcircuits can the circuit be factored to.
Args:
unitary_only (bool): Compute only unitary part of graph.
Returns:
int: Number of connected components in circuit.
"""
# Convert registers to ints (as done in depth).
bits = self.qubits if unitary_only else (self.qubits + self.clbits)
bit_indices: dict[Qubit | Clbit, int] = {bit: idx for idx, bit in enumerate(bits)}
# Start with each qubit or cbit being its own subgraph.
sub_graphs = [[bit] for bit in range(len(bit_indices))]
num_sub_graphs = len(sub_graphs)
# Here we are traversing the gates and looking to see
# which of the sub_graphs the gate joins together.
for instruction in self._data:
if unitary_only:
args = instruction.qubits
num_qargs = len(args)
else:
args = instruction.qubits + instruction.clbits
num_qargs = len(args) + (
1 if getattr(instruction.operation, "condition", None) else 0
)
if num_qargs >= 2 and not getattr(instruction.operation, "_directive", False):
graphs_touched = []
num_touched = 0
# Controls necessarily join all the cbits in the
# register that they use.
if not unitary_only:
for bit in instruction.operation.condition_bits:
idx = bit_indices[bit]
for k in range(num_sub_graphs):
if idx in sub_graphs[k]:
graphs_touched.append(k)
break
for item in args:
reg_int = bit_indices[item]
for k in range(num_sub_graphs):
if reg_int in sub_graphs[k]:
if k not in graphs_touched:
graphs_touched.append(k)
break
graphs_touched = list(set(graphs_touched))
num_touched = len(graphs_touched)
# If the gate touches more than one subgraph
# join those graphs together and return
# reduced number of subgraphs
if num_touched > 1:
connections = []
for idx in graphs_touched:
connections.extend(sub_graphs[idx])
_sub_graphs = []
for idx in range(num_sub_graphs):
if idx not in graphs_touched:
_sub_graphs.append(sub_graphs[idx])
_sub_graphs.append(connections)
sub_graphs = _sub_graphs
num_sub_graphs -= num_touched - 1
# Cannot go lower than one so break
if num_sub_graphs == 1:
break
return num_sub_graphs
def num_unitary_factors(self) -> int:
"""Computes the number of tensor factors in the unitary
(quantum) part of the circuit only.
"""
return self.num_connected_components(unitary_only=True)
def num_tensor_factors(self) -> int:
"""Computes the number of tensor factors in the unitary
(quantum) part of the circuit only.
Notes:
This is here for backwards compatibility, and will be
removed in a future release of Qiskit. You should call
`num_unitary_factors` instead.
"""
return self.num_unitary_factors()
def copy(self, name: str | None = None) -> "QuantumCircuit":
"""Copy the circuit.
Args:
name (str): name to be given to the copied circuit. If None, then the name stays the same.
Returns:
QuantumCircuit: a deepcopy of the current circuit, with the specified name
"""
cpy = self.copy_empty_like(name)
operation_copies = {
id(instruction.operation): instruction.operation.copy() for instruction in self._data
}
cpy._parameter_table = ParameterTable(
{
param: ParameterReferences(
(operation_copies[id(operation)], param_index)
for operation, param_index in self._parameter_table[param]
)
for param in self._parameter_table
}
)
cpy._data = [
instruction.replace(operation=operation_copies[id(instruction.operation)])
for instruction in self._data
]
return cpy
def copy_empty_like(self, name: str | None = None) -> "QuantumCircuit":
"""Return a copy of self with the same structure but empty.
That structure includes:
* name, calibrations and other metadata
* global phase
* all the qubits and clbits, including the registers
Args:
name (str): Name for the copied circuit. If None, then the name stays the same.
Returns:
QuantumCircuit: An empty copy of self.
"""
if not (name is None or isinstance(name, str)):
raise TypeError(
f"invalid name for a circuit: '{name}'. The name must be a string or 'None'."
)
cpy = copy.copy(self)
# copy registers correctly, in copy.copy they are only copied via reference
cpy.qregs = self.qregs.copy()
cpy.cregs = self.cregs.copy()
cpy._qubits = self._qubits.copy()
cpy._ancillas = self._ancillas.copy()
cpy._clbits = self._clbits.copy()
cpy._qubit_indices = self._qubit_indices.copy()
cpy._clbit_indices = self._clbit_indices.copy()
cpy._parameter_table = ParameterTable()
cpy._data = []
cpy._calibrations = copy.deepcopy(self._calibrations)
cpy._metadata = copy.deepcopy(self._metadata)
if name:
cpy.name = name
return cpy
def clear(self) -> None:
"""Clear all instructions in self.
Clearing the circuits will keep the metadata and calibrations.
"""
self._data.clear()
self._parameter_table.clear()
def _create_creg(self, length: int, name: str) -> ClassicalRegister:
"""Creates a creg, checking if ClassicalRegister with same name exists"""
if name in [creg.name for creg in self.cregs]:
save_prefix = ClassicalRegister.prefix
ClassicalRegister.prefix = name
new_creg = ClassicalRegister(length)
ClassicalRegister.prefix = save_prefix
else:
new_creg = ClassicalRegister(length, name)
return new_creg
def _create_qreg(self, length: int, name: str) -> QuantumRegister:
"""Creates a qreg, checking if QuantumRegister with same name exists"""
if name in [qreg.name for qreg in self.qregs]:
save_prefix = QuantumRegister.prefix
QuantumRegister.prefix = name
new_qreg = QuantumRegister(length)
QuantumRegister.prefix = save_prefix
else:
new_qreg = QuantumRegister(length, name)
return new_qreg
def reset(self, qubit: QubitSpecifier) -> InstructionSet:
"""Reset the quantum bit(s) to their default state.
Args:
qubit: qubit(s) to reset.
Returns:
qiskit.circuit.InstructionSet: handle to the added instruction.
"""
return self.append(Reset(), [qubit], [])
def measure(self, qubit: QubitSpecifier, cbit: ClbitSpecifier) -> InstructionSet:
r"""Measure a quantum bit (``qubit``) in the Z basis into a classical bit (``cbit``).
When a quantum state is measured, a qubit is projected in the computational (Pauli Z) basis
to either :math:`\lvert 0 \rangle` or :math:`\lvert 1 \rangle`. The classical bit ``cbit``
indicates the result
of that projection as a ``0`` or a ``1`` respectively. This operation is non-reversible.
Args:
qubit: qubit(s) to measure.
cbit: classical bit(s) to place the measurement result(s) in.
Returns:
qiskit.circuit.InstructionSet: handle to the added instructions.
Raises:
CircuitError: if arguments have bad format.
Examples:
In this example, a qubit is measured and the result of that measurement is stored in the
classical bit (usually expressed in diagrams as a double line):
.. code-block::
from qiskit import QuantumCircuit
circuit = QuantumCircuit(1, 1)
circuit.h(0)
circuit.measure(0, 0)
circuit.draw()
.. parsed-literal::
┌───┐┌─┐
q: ┤ H ├┤M├
└───┘└╥┘
c: 1/══════╩═
0
It is possible to call ``measure`` with lists of ``qubits`` and ``cbits`` as a shortcut
for one-to-one measurement. These two forms produce identical results:
.. code-block::
circuit = QuantumCircuit(2, 2)
circuit.measure([0,1], [0,1])
.. code-block::
circuit = QuantumCircuit(2, 2)
circuit.measure(0, 0)
circuit.measure(1, 1)
Instead of lists, you can use :class:`~qiskit.circuit.QuantumRegister` and
:class:`~qiskit.circuit.ClassicalRegister` under the same logic.
.. code-block::
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
qreg = QuantumRegister(2, "qreg")
creg = ClassicalRegister(2, "creg")
circuit = QuantumCircuit(qreg, creg)
circuit.measure(qreg, creg)
This is equivalent to:
.. code-block::
circuit = QuantumCircuit(qreg, creg)
circuit.measure(qreg[0], creg[0])
circuit.measure(qreg[1], creg[1])
"""
return self.append(Measure(), [qubit], [cbit])
def measure_active(self, inplace: bool = True) -> Optional["QuantumCircuit"]:
"""Adds measurement to all non-idle qubits. Creates a new ClassicalRegister with
a size equal to the number of non-idle qubits being measured.
Returns a new circuit with measurements if `inplace=False`.
Args:
inplace (bool): All measurements inplace or return new circuit.
Returns:
QuantumCircuit: Returns circuit with measurements when `inplace = False`.
"""
from qiskit.converters.circuit_to_dag import circuit_to_dag
if inplace:
circ = self
else:
circ = self.copy()
dag = circuit_to_dag(circ)
qubits_to_measure = [qubit for qubit in circ.qubits if qubit not in dag.idle_wires()]
new_creg = circ._create_creg(len(qubits_to_measure), "measure")
circ.add_register(new_creg)
circ.barrier()
circ.measure(qubits_to_measure, new_creg)
if not inplace:
return circ
else:
return None
def measure_all(
self, inplace: bool = True, add_bits: bool = True
) -> Optional["QuantumCircuit"]:
"""Adds measurement to all qubits.
By default, adds new classical bits in a :obj:`.ClassicalRegister` to store these
measurements. If ``add_bits=False``, the results of the measurements will instead be stored
in the already existing classical bits, with qubit ``n`` being measured into classical bit
``n``.
Returns a new circuit with measurements if ``inplace=False``.
Args:
inplace (bool): All measurements inplace or return new circuit.
add_bits (bool): Whether to add new bits to store the results.
Returns:
QuantumCircuit: Returns circuit with measurements when ``inplace=False``.
Raises:
CircuitError: if ``add_bits=False`` but there are not enough classical bits.
"""
if inplace:
circ = self
else:
circ = self.copy()
if add_bits:
new_creg = circ._create_creg(len(circ.qubits), "meas")
circ.add_register(new_creg)
circ.barrier()
circ.measure(circ.qubits, new_creg)
else:
if len(circ.clbits) < len(circ.qubits):
raise CircuitError(
"The number of classical bits must be equal or greater than "
"the number of qubits."
)
circ.barrier()
circ.measure(circ.qubits, circ.clbits[0 : len(circ.qubits)])
if not inplace:
return circ
else:
return None
def remove_final_measurements(self, inplace: bool = True) -> Optional["QuantumCircuit"]:
"""Removes final measurements and barriers on all qubits if they are present.
Deletes the classical registers that were used to store the values from these measurements
that become idle as a result of this operation, and deletes classical bits that are
referenced only by removed registers, or that aren't referenced at all but have
become idle as a result of this operation.
Measurements and barriers are considered final if they are
followed by no other operations (aside from other measurements or barriers.)
Args:
inplace (bool): All measurements removed inplace or return new circuit.
Returns:
QuantumCircuit: Returns the resulting circuit when ``inplace=False``, else None.
"""
# pylint: disable=cyclic-import
from qiskit.transpiler.passes import RemoveFinalMeasurements
from qiskit.converters import circuit_to_dag
if inplace:
circ = self
else:
circ = self.copy()
dag = circuit_to_dag(circ)
remove_final_meas = RemoveFinalMeasurements()
new_dag = remove_final_meas.run(dag)
kept_cregs = set(new_dag.cregs.values())
kept_clbits = set(new_dag.clbits)
# Filter only cregs/clbits still in new DAG, preserving original circuit order
cregs_to_add = [creg for creg in circ.cregs if creg in kept_cregs]
clbits_to_add = [clbit for clbit in circ._clbits if clbit in kept_clbits]
# Clear cregs and clbits
circ.cregs = []
circ._clbits = []
circ._clbit_indices = {}
# We must add the clbits first to preserve the original circuit
# order. This way, add_register never adds clbits and just
# creates registers that point to them.
circ.add_bits(clbits_to_add)
for creg in cregs_to_add:
circ.add_register(creg)
# Clear instruction info
circ.data.clear()
circ._parameter_table.clear()
# Set circ instructions to match the new DAG
for node in new_dag.topological_op_nodes():
# Get arguments for classical condition (if any)
inst = node.op.copy()
circ.append(inst, node.qargs, node.cargs)
if not inplace:
return circ
else:
return None
@staticmethod
def from_qasm_file(path: str) -> "QuantumCircuit":
"""Take in a QASM file and generate a QuantumCircuit object.
Args:
path (str): Path to the file for a QASM program
Return:
QuantumCircuit: The QuantumCircuit object for the input QASM
See also:
:func:`.qasm2.load`: the complete interface to the OpenQASM 2 importer.
"""
# pylint: disable=cyclic-import
from qiskit import qasm2
return qasm2.load(
path,
include_path=qasm2.LEGACY_INCLUDE_PATH,
custom_instructions=qasm2.LEGACY_CUSTOM_INSTRUCTIONS,
custom_classical=qasm2.LEGACY_CUSTOM_CLASSICAL,
strict=False,
)
@staticmethod
def from_qasm_str(qasm_str: str) -> "QuantumCircuit":
"""Take in a QASM string and generate a QuantumCircuit object.
Args:
qasm_str (str): A QASM program string
Return:
QuantumCircuit: The QuantumCircuit object for the input QASM
See also:
:func:`.qasm2.loads`: the complete interface to the OpenQASM 2 importer.
"""
# pylint: disable=cyclic-import
from qiskit import qasm2
return qasm2.loads(
qasm_str,
include_path=qasm2.LEGACY_INCLUDE_PATH,
custom_instructions=qasm2.LEGACY_CUSTOM_INSTRUCTIONS,
custom_classical=qasm2.LEGACY_CUSTOM_CLASSICAL,
strict=False,
)
@property
def global_phase(self) -> ParameterValueType:
"""Return the global phase of the circuit in radians."""
return self._global_phase
@global_phase.setter
def global_phase(self, angle: ParameterValueType):
"""Set the phase of the circuit.
Args:
angle (float, ParameterExpression): radians
"""
if isinstance(angle, ParameterExpression) and angle.parameters:
self._global_phase = angle
else:
# Set the phase to the [0, 2π) interval
angle = float(angle)
if not angle:
self._global_phase = 0
else:
self._global_phase = angle % (2 * np.pi)
@property
def parameters(self) -> ParameterView:
"""The parameters defined in the circuit.
This attribute returns the :class:`.Parameter` objects in the circuit sorted
alphabetically. Note that parameters instantiated with a :class:`.ParameterVector`
are still sorted numerically.
Examples:
The snippet below shows that insertion order of parameters does not matter.
.. code-block:: python
>>> from qiskit.circuit import QuantumCircuit, Parameter
>>> a, b, elephant = Parameter("a"), Parameter("b"), Parameter("elephant")
>>> circuit = QuantumCircuit(1)
>>> circuit.rx(b, 0)
>>> circuit.rz(elephant, 0)
>>> circuit.ry(a, 0)
>>> circuit.parameters # sorted alphabetically!
ParameterView([Parameter(a), Parameter(b), Parameter(elephant)])
Bear in mind that alphabetical sorting might be unintuitive when it comes to numbers.
The literal "10" comes before "2" in strict alphabetical sorting.
.. code-block:: python
>>> from qiskit.circuit import QuantumCircuit, Parameter
>>> angles = [Parameter("angle_1"), Parameter("angle_2"), Parameter("angle_10")]
>>> circuit = QuantumCircuit(1)
>>> circuit.u(*angles, 0)
>>> circuit.draw()
┌─────────────────────────────┐
q: ┤ U(angle_1,angle_2,angle_10) ├
└─────────────────────────────┘
>>> circuit.parameters
ParameterView([Parameter(angle_1), Parameter(angle_10), Parameter(angle_2)])
To respect numerical sorting, a :class:`.ParameterVector` can be used.
.. code-block:: python
>>> from qiskit.circuit import QuantumCircuit, Parameter, ParameterVector
>>> x = ParameterVector("x", 12)
>>> circuit = QuantumCircuit(1)
>>> for x_i in x:
... circuit.rx(x_i, 0)
>>> circuit.parameters
ParameterView([
ParameterVectorElement(x[0]), ParameterVectorElement(x[1]),
ParameterVectorElement(x[2]), ParameterVectorElement(x[3]),
..., ParameterVectorElement(x[11])
])
Returns:
The sorted :class:`.Parameter` objects in the circuit.
"""
# parameters from gates
if self._parameters is None:
self._parameters = sort_parameters(self._unsorted_parameters())
# return as parameter view, which implements the set and list interface
return ParameterView(self._parameters)
@property
def num_parameters(self) -> int:
"""The number of parameter objects in the circuit."""
return len(self._unsorted_parameters())
def _unsorted_parameters(self) -> set[Parameter]:
"""Efficiently get all parameters in the circuit, without any sorting overhead."""
parameters = set(self._parameter_table)
if isinstance(self.global_phase, ParameterExpression):
parameters.update(self.global_phase.parameters)
return parameters
@overload
def assign_parameters(
self,
parameters: Union[Mapping[Parameter, ParameterValueType], Sequence[ParameterValueType]],
inplace: Literal[False] = ...,
*,
flat_input: bool = ...,
strict: bool = ...,
) -> "QuantumCircuit":
...
@overload
def assign_parameters(
self,
parameters: Union[Mapping[Parameter, ParameterValueType], Sequence[ParameterValueType]],
inplace: Literal[True] = ...,
*,
flat_input: bool = ...,
strict: bool = ...,
) -> None:
...
def assign_parameters( # pylint: disable=missing-raises-doc
self,
parameters: Union[Mapping[Parameter, ParameterValueType], Sequence[ParameterValueType]],
inplace: bool = False,
*,
flat_input: bool = False,
strict: bool = True,
) -> Optional["QuantumCircuit"]:
"""Assign parameters to new parameters or values.
If ``parameters`` is passed as a dictionary, the keys must be :class:`.Parameter`
instances in the current circuit. The values of the dictionary can either be numeric values
or new parameter objects.
If ``parameters`` is passed as a list or array, the elements are assigned to the
current parameters in the order of :attr:`parameters` which is sorted
alphabetically (while respecting the ordering in :class:`.ParameterVector` objects).
The values can be assigned to the current circuit object or to a copy of it.
Args:
parameters: Either a dictionary or iterable specifying the new parameter values.
inplace: If False, a copy of the circuit with the bound parameters is returned.
If True the circuit instance itself is modified.
flat_input: If ``True`` and ``parameters`` is a mapping type, it is assumed to be
exactly a mapping of ``{parameter: value}``. By default (``False``), the mapping
may also contain :class:`.ParameterVector` keys that point to a corresponding
sequence of values, and these will be unrolled during the mapping.
strict: If ``False``, any parameters given in the mapping that are not used in the
circuit will be ignored. If ``True`` (the default), an error will be raised
indicating a logic error.
Raises:
CircuitError: If parameters is a dict and contains parameters not present in the
circuit.
ValueError: If parameters is a list/array and the length mismatches the number of free
parameters in the circuit.
Returns:
A copy of the circuit with bound parameters if ``inplace`` is False, otherwise None.
Examples:
Create a parameterized circuit and assign the parameters in-place.
.. plot::
:include-source:
from qiskit.circuit import QuantumCircuit, Parameter
circuit = QuantumCircuit(2)
params = [Parameter('A'), Parameter('B'), Parameter('C')]
circuit.ry(params[0], 0)
circuit.crx(params[1], 0, 1)
circuit.draw('mpl')
circuit.assign_parameters({params[0]: params[2]}, inplace=True)
circuit.draw('mpl')
Bind the values out-of-place by list and get a copy of the original circuit.
.. plot::
:include-source:
from qiskit.circuit import QuantumCircuit, ParameterVector
circuit = QuantumCircuit(2)
params = ParameterVector('P', 2)
circuit.ry(params[0], 0)
circuit.crx(params[1], 0, 1)
bound_circuit = circuit.assign_parameters([1, 2])
bound_circuit.draw('mpl')
circuit.draw('mpl')
"""
if inplace:
target = self
else:
target = self.copy()
target._increment_instances()
target._name_update()
# Normalise the inputs into simple abstract interfaces, so we've dispatched the "iteration"
# logic in one place at the start of the function. This lets us do things like calculate
# and cache expensive properties for (e.g.) the sequence format only if they're used; for
# many large, close-to-hardware circuits, we won't need the extra handling for
# `global_phase` or recursive definition binding.
#
# During normalisation, be sure to reference 'parameters' and related things from 'self' not
# 'target' so we can take advantage of any caching we might be doing.
if isinstance(parameters, dict):
raw_mapping = parameters if flat_input else self._unroll_param_dict(parameters)
our_parameters = self._unsorted_parameters()
if strict and (extras := raw_mapping.keys() - our_parameters):
raise CircuitError(
f"Cannot bind parameters ({', '.join(str(x) for x in extras)}) not present in"
" the circuit."
)
parameter_binds = _ParameterBindsDict(raw_mapping, our_parameters)
else:
our_parameters = self.parameters
if len(parameters) != len(our_parameters):
raise ValueError(
"Mismatching number of values and parameters. For partial binding "
"please pass a dictionary of {parameter: value} pairs."
)
parameter_binds = _ParameterBindsSequence(our_parameters, parameters)
# Clear out the parameter table for the relevant entries, since we'll be binding those.
# Any new references to parameters are reinserted as part of the bind.
target._parameters = None
# This is deliberately eager, because we want the side effect of clearing the table.
all_references = [
(parameter, value, target._parameter_table.pop(parameter, ()))
for parameter, value in parameter_binds.items()
]
seen_operations = {}
# The meat of the actual binding for regular operations.
for to_bind, bound_value, references in all_references:
update_parameters = (
tuple(bound_value.parameters)
if isinstance(bound_value, ParameterExpression)
else ()
)
for operation, index in references:
seen_operations[id(operation)] = operation
assignee = operation.params[index]
if isinstance(assignee, ParameterExpression):
new_parameter = assignee.assign(to_bind, bound_value)
for parameter in update_parameters:
if parameter not in target._parameter_table:
target._parameter_table[parameter] = ParameterReferences(())
target._parameter_table[parameter].add((operation, index))
if not new_parameter.parameters:
if new_parameter.is_real():
new_parameter = (
int(new_parameter)
if new_parameter._symbol_expr.is_integer
else float(new_parameter)
)
else:
new_parameter = complex(new_parameter)
new_parameter = operation.validate_parameter(new_parameter)
elif isinstance(assignee, QuantumCircuit):
new_parameter = assignee.assign_parameters(
{to_bind: bound_value}, inplace=False, flat_input=True
)
else:
raise RuntimeError( # pragma: no cover
f"Saw an unknown type during symbolic binding: {assignee}."
" This may indicate an internal logic error in symbol tracking."
)
operation.params[index] = new_parameter
# After we've been through everything at the top level, make a single visit to each
# operation we've seen, rebinding its definition if necessary.
for operation in seen_operations.values():
if (
definition := getattr(operation, "_definition", None)
) is not None and definition.num_parameters:
definition.assign_parameters(
parameter_binds.mapping, inplace=True, flat_input=True, strict=False
)
if isinstance(target.global_phase, ParameterExpression):
new_phase = target.global_phase
for parameter in new_phase.parameters & parameter_binds.mapping.keys():
new_phase = new_phase.assign(parameter, parameter_binds.mapping[parameter])
target.global_phase = new_phase
# Finally, assign the parameters inside any of the calibrations. We don't track these in
# the `ParameterTable`, so we manually reconstruct things.
def map_calibration(qubits, parameters, schedule):
modified = False
new_parameters = list(parameters)
for i, parameter in enumerate(new_parameters):
if not isinstance(parameter, ParameterExpression):
continue
if not (contained := parameter.parameters & parameter_binds.mapping.keys()):
continue
for to_bind in contained:
parameter = parameter.assign(to_bind, parameter_binds.mapping[to_bind])
if not parameter.parameters:
parameter = (
int(parameter) if parameter._symbol_expr.is_integer else float(parameter)
)
new_parameters[i] = parameter
modified = True
if modified:
schedule.assign_parameters(parameter_binds.mapping)
return (qubits, tuple(new_parameters)), schedule
target._calibrations = defaultdict(
dict,
(
(
gate,
dict(
map_calibration(qubits, parameters, schedule)
for (qubits, parameters), schedule in calibrations.items()
),
)
for gate, calibrations in target._calibrations.items()
),
)
return None if inplace else target
@staticmethod
def _unroll_param_dict(
parameter_binds: Mapping[Parameter, ParameterValueType]
) -> Mapping[Parameter, ParameterValueType]:
out = {}
for parameter, value in parameter_binds.items():
if isinstance(parameter, ParameterVector):
if len(parameter) != len(value):
raise CircuitError(
f"Parameter vector '{parameter.name}' has length {len(parameter)},"
f" but was assigned to {len(value)} values."
)
out.update(zip(parameter, value))
else:
out[parameter] = value
return out
def bind_parameters(
self, values: Union[Mapping[Parameter, float], Sequence[float]]
) -> "QuantumCircuit":
"""Assign numeric parameters to values yielding a new circuit.
If the values are given as list or array they are bound to the circuit in the order
of :attr:`parameters` (see the docstring for more details).
To assign new Parameter objects or bind the values in-place, without yielding a new
circuit, use the :meth:`assign_parameters` method.
Args:
values: ``{parameter: value, ...}`` or ``[value1, value2, ...]``
Raises:
CircuitError: If values is a dict and contains parameters not present in the circuit.
TypeError: If values contains a ParameterExpression.
Returns:
Copy of self with assignment substitution.
"""
if isinstance(values, dict):
if any(isinstance(value, ParameterExpression) for value in values.values()):
raise TypeError(
"Found ParameterExpression in values; use assign_parameters() instead."
)
return self.assign_parameters(values)
else:
if any(isinstance(value, ParameterExpression) for value in values):
raise TypeError(
"Found ParameterExpression in values; use assign_parameters() instead."
)
return self.assign_parameters(values)
def barrier(self, *qargs: QubitSpecifier, label=None) -> InstructionSet:
"""Apply :class:`~.library.Barrier`. If ``qargs`` is empty, applies to all qubits
in the circuit.
Args:
qargs (QubitSpecifier): Specification for one or more qubit arguments.
label (str): The string label of the barrier.
Returns:
qiskit.circuit.InstructionSet: handle to the added instructions.
"""
from .barrier import Barrier
qubits: list[QubitSpecifier] = []
if not qargs: # None
qubits.extend(self.qubits)
for qarg in qargs:
if isinstance(qarg, QuantumRegister):
qubits.extend([qarg[j] for j in range(qarg.size)])
elif isinstance(qarg, list):
qubits.extend(qarg)
elif isinstance(qarg, range):
qubits.extend(list(qarg))
elif isinstance(qarg, slice):
qubits.extend(self.qubits[qarg])
else:
qubits.append(qarg)
return self.append(Barrier(len(qubits), label=label), qubits, [])
def delay(
self,
duration: ParameterValueType,
qarg: QubitSpecifier | None = None,
unit: str = "dt",
) -> InstructionSet:
"""Apply :class:`~.circuit.Delay`. If qarg is ``None``, applies to all qubits.
When applying to multiple qubits, delays with the same duration will be created.
Args:
duration (int or float or ParameterExpression): duration of the delay.
qarg (Object): qubit argument to apply this delay.
unit (str): unit of the duration. Supported units: ``'s'``, ``'ms'``, ``'us'``,
``'ns'``, ``'ps'``, and ``'dt'``. Default is ``'dt'``, i.e. integer time unit
depending on the target backend.
Returns:
qiskit.circuit.InstructionSet: handle to the added instructions.
Raises:
CircuitError: if arguments have bad format.
"""
qubits: list[QubitSpecifier] = []
if qarg is None: # -> apply delays to all qubits
for q in self.qubits:
qubits.append(q)
else:
if isinstance(qarg, QuantumRegister):
qubits.extend([qarg[j] for j in range(qarg.size)])
elif isinstance(qarg, list):
qubits.extend(qarg)
elif isinstance(qarg, (range, tuple)):
qubits.extend(list(qarg))
elif isinstance(qarg, slice):
qubits.extend(self.qubits[qarg])
else:
qubits.append(qarg)
instructions = InstructionSet(resource_requester=self._resolve_classical_resource)
for q in qubits:
inst: tuple[
Instruction, Sequence[QubitSpecifier] | None, Sequence[ClbitSpecifier] | None
] = (Delay(duration, unit), [q], [])
self.append(*inst)
instructions.add(*inst)
return instructions
def h(self, qubit: QubitSpecifier) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.HGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
qubit: The qubit(s) to apply the gate to.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.h import HGate
return self.append(HGate(), [qubit], [])
def ch(
self,
control_qubit: QubitSpecifier,
target_qubit: QubitSpecifier,
label: str | None = None,
ctrl_state: str | int | None = None,
) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.CHGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
control_qubit: The qubit(s) used as the control.
target_qubit: The qubit(s) targeted by the gate.
label: The string label of the gate in the circuit.
ctrl_state:
The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling
on the '1' state.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.h import CHGate
return self.append(
CHGate(label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], []
)
def i(self, qubit: QubitSpecifier) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.IGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
qubit: The qubit(s) to apply the gate to.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.i import IGate
return self.append(IGate(), [qubit], [])
def id(self, qubit: QubitSpecifier) -> InstructionSet: # pylint: disable=invalid-name
"""Apply :class:`~qiskit.circuit.library.IGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
qubit: The qubit(s) to apply the gate to.
Returns:
A handle to the instructions created.
See Also:
QuantumCircuit.i: the same function.
"""
return self.i(qubit)
def ms(self, theta: ParameterValueType, qubits: Sequence[QubitSpecifier]) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.MSGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
theta: The angle of the rotation.
qubits: The qubits to apply the gate to.
Returns:
A handle to the instructions created.
"""
# pylint: disable=cyclic-import
from .library.generalized_gates.gms import MSGate
return self.append(MSGate(len(qubits), theta), qubits)
def p(self, theta: ParameterValueType, qubit: QubitSpecifier) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.PhaseGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
theta: THe angle of the rotation.
qubit: The qubit(s) to apply the gate to.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.p import PhaseGate
return self.append(PhaseGate(theta), [qubit], [])
def cp(
self,
theta: ParameterValueType,
control_qubit: QubitSpecifier,
target_qubit: QubitSpecifier,
label: str | None = None,
ctrl_state: str | int | None = None,
) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.CPhaseGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
theta: The angle of the rotation.
control_qubit: The qubit(s) used as the control.
target_qubit: The qubit(s) targeted by the gate.
label: The string label of the gate in the circuit.
ctrl_state:
The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling
on the '1' state.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.p import CPhaseGate
return self.append(
CPhaseGate(theta, label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], []
)
def mcp(
self,
lam: ParameterValueType,
control_qubits: Sequence[QubitSpecifier],
target_qubit: QubitSpecifier,
) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.MCPhaseGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
lam: The angle of the rotation.
control_qubits: The qubits used as the controls.
target_qubit: The qubit(s) targeted by the gate.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.p import MCPhaseGate
num_ctrl_qubits = len(control_qubits)
return self.append(
MCPhaseGate(lam, num_ctrl_qubits), control_qubits[:] + [target_qubit], []
)
def r(
self, theta: ParameterValueType, phi: ParameterValueType, qubit: QubitSpecifier
) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.RGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
theta: The angle of the rotation.
phi: The angle of the axis of rotation in the x-y plane.
qubit: The qubit(s) to apply the gate to.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.r import RGate
return self.append(RGate(theta, phi), [qubit], [])
def rv(
self,
vx: ParameterValueType,
vy: ParameterValueType,
vz: ParameterValueType,
qubit: QubitSpecifier,
) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.RVGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Rotation around an arbitrary rotation axis :math:`v`, where :math:`|v|` is the angle of
rotation in radians.
Args:
vx: x-component of the rotation axis.
vy: y-component of the rotation axis.
vz: z-component of the rotation axis.
qubit: The qubit(s) to apply the gate to.
Returns:
A handle to the instructions created.
"""
from .library.generalized_gates.rv import RVGate
return self.append(RVGate(vx, vy, vz), [qubit], [])
def rccx(
self,
control_qubit1: QubitSpecifier,
control_qubit2: QubitSpecifier,
target_qubit: QubitSpecifier,
) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.RCCXGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
control_qubit1: The qubit(s) used as the first control.
control_qubit2: The qubit(s) used as the second control.
target_qubit: The qubit(s) targeted by the gate.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.x import RCCXGate
return self.append(RCCXGate(), [control_qubit1, control_qubit2, target_qubit], [])
def rcccx(
self,
control_qubit1: QubitSpecifier,
control_qubit2: QubitSpecifier,
control_qubit3: QubitSpecifier,
target_qubit: QubitSpecifier,
) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.RC3XGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
control_qubit1: The qubit(s) used as the first control.
control_qubit2: The qubit(s) used as the second control.
control_qubit3: The qubit(s) used as the third control.
target_qubit: The qubit(s) targeted by the gate.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.x import RC3XGate
return self.append(
RC3XGate(), [control_qubit1, control_qubit2, control_qubit3, target_qubit], []
)
def rx(
self, theta: ParameterValueType, qubit: QubitSpecifier, label: str | None = None
) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.RXGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
theta: The rotation angle of the gate.
qubit: The qubit(s) to apply the gate to.
label: The string label of the gate in the circuit.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.rx import RXGate
return self.append(RXGate(theta, label=label), [qubit], [])
def crx(
self,
theta: ParameterValueType,
control_qubit: QubitSpecifier,
target_qubit: QubitSpecifier,
label: str | None = None,
ctrl_state: str | int | None = None,
) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.CRXGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
theta: The angle of the rotation.
control_qubit: The qubit(s) used as the control.
target_qubit: The qubit(s) targeted by the gate.
label: The string label of the gate in the circuit.
ctrl_state:
The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling
on the '1' state.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.rx import CRXGate
return self.append(
CRXGate(theta, label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], []
)
def rxx(
self, theta: ParameterValueType, qubit1: QubitSpecifier, qubit2: QubitSpecifier
) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.RXXGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
theta: The angle of the rotation.
qubit1: The qubit(s) to apply the gate to.
qubit2: The qubit(s) to apply the gate to.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.rxx import RXXGate
return self.append(RXXGate(theta), [qubit1, qubit2], [])
def ry(
self, theta: ParameterValueType, qubit: QubitSpecifier, label: str | None = None
) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.RYGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
theta: The rotation angle of the gate.
qubit: The qubit(s) to apply the gate to.
label: The string label of the gate in the circuit.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.ry import RYGate
return self.append(RYGate(theta, label=label), [qubit], [])
def cry(
self,
theta: ParameterValueType,
control_qubit: QubitSpecifier,
target_qubit: QubitSpecifier,
label: str | None = None,
ctrl_state: str | int | None = None,
) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.CRYGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
theta: The angle of the rotation.
control_qubit: The qubit(s) used as the control.
target_qubit: The qubit(s) targeted by the gate.
label: The string label of the gate in the circuit.
ctrl_state:
The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling
on the '1' state.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.ry import CRYGate
return self.append(
CRYGate(theta, label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], []
)
def ryy(
self, theta: ParameterValueType, qubit1: QubitSpecifier, qubit2: QubitSpecifier
) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.RYYGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
theta: The rotation angle of the gate.
qubit1: The qubit(s) to apply the gate to.
qubit2: The qubit(s) to apply the gate to.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.ryy import RYYGate
return self.append(RYYGate(theta), [qubit1, qubit2], [])
def rz(self, phi: ParameterValueType, qubit: QubitSpecifier) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.RZGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
phi: The rotation angle of the gate.
qubit: The qubit(s) to apply the gate to.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.rz import RZGate
return self.append(RZGate(phi), [qubit], [])
def crz(
self,
theta: ParameterValueType,
control_qubit: QubitSpecifier,
target_qubit: QubitSpecifier,
label: str | None = None,
ctrl_state: str | int | None = None,
) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.CRZGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
theta: The angle of the rotation.
control_qubit: The qubit(s) used as the control.
target_qubit: The qubit(s) targeted by the gate.
label: The string label of the gate in the circuit.
ctrl_state:
The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling
on the '1' state.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.rz import CRZGate
return self.append(
CRZGate(theta, label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], []
)
def rzx(
self, theta: ParameterValueType, qubit1: QubitSpecifier, qubit2: QubitSpecifier
) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.RZXGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
theta: The rotation angle of the gate.
qubit1: The qubit(s) to apply the gate to.
qubit2: The qubit(s) to apply the gate to.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.rzx import RZXGate
return self.append(RZXGate(theta), [qubit1, qubit2], [])
def rzz(
self, theta: ParameterValueType, qubit1: QubitSpecifier, qubit2: QubitSpecifier
) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.RZZGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
theta: The rotation angle of the gate.
qubit1: The qubit(s) to apply the gate to.
qubit2: The qubit(s) to apply the gate to.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.rzz import RZZGate
return self.append(RZZGate(theta), [qubit1, qubit2], [])
def ecr(self, qubit1: QubitSpecifier, qubit2: QubitSpecifier) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.ECRGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
qubit1, qubit2: The qubits to apply the gate to.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.ecr import ECRGate
return self.append(ECRGate(), [qubit1, qubit2], [])
def s(self, qubit: QubitSpecifier) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.SGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
qubit: The qubit(s) to apply the gate to.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.s import SGate
return self.append(SGate(), [qubit], [])
def sdg(self, qubit: QubitSpecifier) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.SdgGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
qubit: The qubit(s) to apply the gate to.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.s import SdgGate
return self.append(SdgGate(), [qubit], [])
def cs(
self,
control_qubit: QubitSpecifier,
target_qubit: QubitSpecifier,
label: str | None = None,
ctrl_state: str | int | None = None,
) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.CSGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
control_qubit: The qubit(s) used as the control.
target_qubit: The qubit(s) targeted by the gate.
label: The string label of the gate in the circuit.
ctrl_state:
The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling
on the '1' state.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.s import CSGate
return self.append(
CSGate(label=label, ctrl_state=ctrl_state),
[control_qubit, target_qubit],
[],
)
def csdg(
self,
control_qubit: QubitSpecifier,
target_qubit: QubitSpecifier,
label: str | None = None,
ctrl_state: str | int | None = None,
) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.CSdgGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
control_qubit: The qubit(s) used as the control.
target_qubit: The qubit(s) targeted by the gate.
label: The string label of the gate in the circuit.
ctrl_state:
The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling
on the '1' state.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.s import CSdgGate
return self.append(
CSdgGate(label=label, ctrl_state=ctrl_state),
[control_qubit, target_qubit],
[],
)
def swap(self, qubit1: QubitSpecifier, qubit2: QubitSpecifier) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.SwapGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
qubit1, qubit2: The qubits to apply the gate to.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.swap import SwapGate
return self.append(SwapGate(), [qubit1, qubit2], [])
def iswap(self, qubit1: QubitSpecifier, qubit2: QubitSpecifier) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.iSwapGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
qubit1, qubit2: The qubits to apply the gate to.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.iswap import iSwapGate
return self.append(iSwapGate(), [qubit1, qubit2], [])
def cswap(
self,
control_qubit: QubitSpecifier,
target_qubit1: QubitSpecifier,
target_qubit2: QubitSpecifier,
label: str | None = None,
ctrl_state: str | int | None = None,
) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.CSwapGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
control_qubit: The qubit(s) used as the control.
target_qubit1: The qubit(s) targeted by the gate.
target_qubit2: The qubit(s) targeted by the gate.
label: The string label of the gate in the circuit.
ctrl_state:
The control state in decimal, or as a bitstring (e.g. ``'1'``). Defaults to controlling
on the ``'1'`` state.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.swap import CSwapGate
return self.append(
CSwapGate(label=label, ctrl_state=ctrl_state),
[control_qubit, target_qubit1, target_qubit2],
[],
)
def fredkin(
self,
control_qubit: QubitSpecifier,
target_qubit1: QubitSpecifier,
target_qubit2: QubitSpecifier,
) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.CSwapGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
control_qubit: The qubit(s) used as the control.
target_qubit1: The qubit(s) targeted by the gate.
target_qubit2: The qubit(s) targeted by the gate.
Returns:
A handle to the instructions created.
See Also:
QuantumCircuit.cswap: the same function with a different name.
"""
return self.cswap(control_qubit, target_qubit1, target_qubit2)
def sx(self, qubit: QubitSpecifier) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.SXGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
qubit: The qubit(s) to apply the gate to.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.sx import SXGate
return self.append(SXGate(), [qubit], [])
def sxdg(self, qubit: QubitSpecifier) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.SXdgGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
qubit: The qubit(s) to apply the gate to.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.sx import SXdgGate
return self.append(SXdgGate(), [qubit], [])
def csx(
self,
control_qubit: QubitSpecifier,
target_qubit: QubitSpecifier,
label: str | None = None,
ctrl_state: str | int | None = None,
) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.CSXGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
control_qubit: The qubit(s) used as the control.
target_qubit: The qubit(s) targeted by the gate.
label: The string label of the gate in the circuit.
ctrl_state:
The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling
on the '1' state.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.sx import CSXGate
return self.append(
CSXGate(label=label, ctrl_state=ctrl_state),
[control_qubit, target_qubit],
[],
)
def t(self, qubit: QubitSpecifier) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.TGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
qubit: The qubit(s) to apply the gate to.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.t import TGate
return self.append(TGate(), [qubit], [])
def tdg(self, qubit: QubitSpecifier) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.TdgGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
qubit: The qubit(s) to apply the gate to.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.t import TdgGate
return self.append(TdgGate(), [qubit], [])
def u(
self,
theta: ParameterValueType,
phi: ParameterValueType,
lam: ParameterValueType,
qubit: QubitSpecifier,
) -> InstructionSet:
r"""Apply :class:`~qiskit.circuit.library.UGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
theta: The :math:`\theta` rotation angle of the gate.
phi: The :math:`\phi` rotation angle of the gate.
lam: The :math:`\lambda` rotation angle of the gate.
qubit: The qubit(s) to apply the gate to.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.u import UGate
return self.append(UGate(theta, phi, lam), [qubit], [])
def cu(
self,
theta: ParameterValueType,
phi: ParameterValueType,
lam: ParameterValueType,
gamma: ParameterValueType,
control_qubit: QubitSpecifier,
target_qubit: QubitSpecifier,
label: str | None = None,
ctrl_state: str | int | None = None,
) -> InstructionSet:
r"""Apply :class:`~qiskit.circuit.library.CUGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
theta: The :math:`\theta` rotation angle of the gate.
phi: The :math:`\phi` rotation angle of the gate.
lam: The :math:`\lambda` rotation angle of the gate.
gamma: The global phase applied of the U gate, if applied.
control_qubit: The qubit(s) used as the control.
target_qubit: The qubit(s) targeted by the gate.
label: The string label of the gate in the circuit.
ctrl_state:
The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling
on the '1' state.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.u import CUGate
return self.append(
CUGate(theta, phi, lam, gamma, label=label, ctrl_state=ctrl_state),
[control_qubit, target_qubit],
[],
)
def x(self, qubit: QubitSpecifier, label: str | None = None) -> InstructionSet:
r"""Apply :class:`~qiskit.circuit.library.XGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
qubit: The qubit(s) to apply the gate to.
label: The string label of the gate in the circuit.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.x import XGate
return self.append(XGate(label=label), [qubit], [])
def cx(
self,
control_qubit: QubitSpecifier,
target_qubit: QubitSpecifier,
label: str | None = None,
ctrl_state: str | int | None = None,
) -> InstructionSet:
r"""Apply :class:`~qiskit.circuit.library.CXGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
control_qubit: The qubit(s) used as the control.
target_qubit: The qubit(s) targeted by the gate.
label: The string label of the gate in the circuit.
ctrl_state:
The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling
on the '1' state.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.x import CXGate
return self.append(
CXGate(label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], []
)
def cnot(
self,
control_qubit: QubitSpecifier,
target_qubit: QubitSpecifier,
label: str | None = None,
ctrl_state: str | int | None = None,
) -> InstructionSet:
r"""Apply :class:`~qiskit.circuit.library.CXGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
control_qubit: The qubit(s) used as the control.
target_qubit: The qubit(s) targeted by the gate.
label: The string label of the gate in the circuit.
ctrl_state:
The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling
on the '1' state.
Returns:
A handle to the instructions created.
See Also:
QuantumCircuit.cx: the same function with a different name.
"""
return self.cx(control_qubit, target_qubit, label, ctrl_state)
def dcx(self, qubit1: QubitSpecifier, qubit2: QubitSpecifier) -> InstructionSet:
r"""Apply :class:`~qiskit.circuit.library.DCXGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
qubit1: The qubit(s) to apply the gate to.
qubit2: The qubit(s) to apply the gate to.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.dcx import DCXGate
return self.append(DCXGate(), [qubit1, qubit2], [])
def ccx(
self,
control_qubit1: QubitSpecifier,
control_qubit2: QubitSpecifier,
target_qubit: QubitSpecifier,
ctrl_state: str | int | None = None,
) -> InstructionSet:
r"""Apply :class:`~qiskit.circuit.library.CCXGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
control_qubit1: The qubit(s) used as the first control.
control_qubit2: The qubit(s) used as the second control.
target_qubit: The qubit(s) targeted by the gate.
ctrl_state:
The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling
on the '1' state.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.x import CCXGate
return self.append(
CCXGate(ctrl_state=ctrl_state),
[control_qubit1, control_qubit2, target_qubit],
[],
)
def toffoli(
self,
control_qubit1: QubitSpecifier,
control_qubit2: QubitSpecifier,
target_qubit: QubitSpecifier,
) -> InstructionSet:
r"""Apply :class:`~qiskit.circuit.library.CCXGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
control_qubit1: The qubit(s) used as the first control.
control_qubit2: The qubit(s) used as the second control.
target_qubit: The qubit(s) targeted by the gate.
Returns:
A handle to the instructions created.
See Also:
QuantumCircuit.ccx: the same gate with a different name.
"""
return self.ccx(control_qubit1, control_qubit2, target_qubit)
def mcx(
self,
control_qubits: Sequence[QubitSpecifier],
target_qubit: QubitSpecifier,
ancilla_qubits: QubitSpecifier | Sequence[QubitSpecifier] | None = None,
mode: str = "noancilla",
) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.MCXGate`.
The multi-cX gate can be implemented using different techniques, which use different numbers
of ancilla qubits and have varying circuit depth. These modes are:
- ``'noancilla'``: Requires 0 ancilla qubits.
- ``'recursion'``: Requires 1 ancilla qubit if more than 4 controls are used, otherwise 0.
- ``'v-chain'``: Requires 2 less ancillas than the number of control qubits.
- ``'v-chain-dirty'``: Same as for the clean ancillas (but the circuit will be longer).
For the full matrix form of this gate, see the underlying gate documentation.
Args:
control_qubits: The qubits used as the controls.
target_qubit: The qubit(s) targeted by the gate.
ancilla_qubits: The qubits used as the ancillae, if the mode requires them.
mode: The choice of mode, explained further above.
Returns:
A handle to the instructions created.
Raises:
ValueError: if the given mode is not known, or if too few ancilla qubits are passed.
AttributeError: if no ancilla qubits are passed, but some are needed.
"""
from .library.standard_gates.x import MCXGrayCode, MCXRecursive, MCXVChain
num_ctrl_qubits = len(control_qubits)
available_implementations = {
"noancilla": MCXGrayCode(num_ctrl_qubits),
"recursion": MCXRecursive(num_ctrl_qubits),
"v-chain": MCXVChain(num_ctrl_qubits, False),
"v-chain-dirty": MCXVChain(num_ctrl_qubits, dirty_ancillas=True),
# outdated, previous names
"advanced": MCXRecursive(num_ctrl_qubits),
"basic": MCXVChain(num_ctrl_qubits, dirty_ancillas=False),
"basic-dirty-ancilla": MCXVChain(num_ctrl_qubits, dirty_ancillas=True),
}
# check ancilla input
if ancilla_qubits:
_ = self.qbit_argument_conversion(ancilla_qubits)
try:
gate = available_implementations[mode]
except KeyError as ex:
all_modes = list(available_implementations.keys())
raise ValueError(
f"Unsupported mode ({mode}) selected, choose one of {all_modes}"
) from ex
if hasattr(gate, "num_ancilla_qubits") and gate.num_ancilla_qubits > 0:
required = gate.num_ancilla_qubits
if ancilla_qubits is None:
raise AttributeError(f"No ancillas provided, but {required} are needed!")
# convert ancilla qubits to a list if they were passed as int or qubit
if not hasattr(ancilla_qubits, "__len__"):
ancilla_qubits = [ancilla_qubits]
if len(ancilla_qubits) < required:
actually = len(ancilla_qubits)
raise ValueError(f"At least {required} ancillas required, but {actually} given.")
# size down if too many ancillas were provided
ancilla_qubits = ancilla_qubits[:required]
else:
ancilla_qubits = []
return self.append(gate, control_qubits[:] + [target_qubit] + ancilla_qubits[:], [])
def mct(
self,
control_qubits: Sequence[QubitSpecifier],
target_qubit: QubitSpecifier,
ancilla_qubits: QubitSpecifier | Sequence[QubitSpecifier] | None = None,
mode: str = "noancilla",
) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.MCXGate`.
The multi-cX gate can be implemented using different techniques, which use different numbers
of ancilla qubits and have varying circuit depth. These modes are:
- ``'noancilla'``: Requires 0 ancilla qubits.
- ``'recursion'``: Requires 1 ancilla qubit if more than 4 controls are used, otherwise 0.
- ``'v-chain'``: Requires 2 less ancillas than the number of control qubits.
- ``'v-chain-dirty'``: Same as for the clean ancillas (but the circuit will be longer).
For the full matrix form of this gate, see the underlying gate documentation.
Args:
control_qubits: The qubits used as the controls.
target_qubit: The qubit(s) targeted by the gate.
ancilla_qubits: The qubits used as the ancillae, if the mode requires them.
mode: The choice of mode, explained further above.
Returns:
A handle to the instructions created.
Raises:
ValueError: if the given mode is not known, or if too few ancilla qubits are passed.
AttributeError: if no ancilla qubits are passed, but some are needed.
See Also:
QuantumCircuit.mcx: the same gate with a different name.
"""
return self.mcx(control_qubits, target_qubit, ancilla_qubits, mode)
def y(self, qubit: QubitSpecifier) -> InstructionSet:
r"""Apply :class:`~qiskit.circuit.library.YGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
qubit: The qubit(s) to apply the gate to.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.y import YGate
return self.append(YGate(), [qubit], [])
def cy(
self,
control_qubit: QubitSpecifier,
target_qubit: QubitSpecifier,
label: str | None = None,
ctrl_state: str | int | None = None,
) -> InstructionSet:
r"""Apply :class:`~qiskit.circuit.library.CYGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
control_qubit: The qubit(s) used as the controls.
target_qubit: The qubit(s) targeted by the gate.
label: The string label of the gate in the circuit.
ctrl_state:
The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling
on the '1' state.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.y import CYGate
return self.append(
CYGate(label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], []
)
def z(self, qubit: QubitSpecifier) -> InstructionSet:
r"""Apply :class:`~qiskit.circuit.library.ZGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
qubit: The qubit(s) to apply the gate to.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.z import ZGate
return self.append(ZGate(), [qubit], [])
def cz(
self,
control_qubit: QubitSpecifier,
target_qubit: QubitSpecifier,
label: str | None = None,
ctrl_state: str | int | None = None,
) -> InstructionSet:
r"""Apply :class:`~qiskit.circuit.library.CZGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
control_qubit: The qubit(s) used as the controls.
target_qubit: The qubit(s) targeted by the gate.
label: The string label of the gate in the circuit.
ctrl_state:
The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling
on the '1' state.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.z import CZGate
return self.append(
CZGate(label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], []
)
def ccz(
self,
control_qubit1: QubitSpecifier,
control_qubit2: QubitSpecifier,
target_qubit: QubitSpecifier,
label: str | None = None,
ctrl_state: str | int | None = None,
) -> InstructionSet:
r"""Apply :class:`~qiskit.circuit.library.CCZGate`.
For the full matrix form of this gate, see the underlying gate documentation.
Args:
control_qubit1: The qubit(s) used as the first control.
control_qubit2: The qubit(s) used as the second control.
target_qubit: The qubit(s) targeted by the gate.
label: The string label of the gate in the circuit.
ctrl_state:
The control state in decimal, or as a bitstring (e.g. '10'). Defaults to controlling
on the '11' state.
Returns:
A handle to the instructions created.
"""
from .library.standard_gates.z import CCZGate
return self.append(
CCZGate(label=label, ctrl_state=ctrl_state),
[control_qubit1, control_qubit2, target_qubit],
[],
)
def pauli(
self,
pauli_string: str,
qubits: Sequence[QubitSpecifier],
) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.library.PauliGate`.
Args:
pauli_string: A string representing the Pauli operator to apply, e.g. 'XX'.
qubits: The qubits to apply this gate to.
Returns:
A handle to the instructions created.
"""
from qiskit.circuit.library.generalized_gates.pauli import PauliGate
return self.append(PauliGate(pauli_string), qubits, [])
def _push_scope(
self,
qubits: Iterable[Qubit] = (),
clbits: Iterable[Clbit] = (),
registers: Iterable[Register] = (),
allow_jumps: bool = True,
forbidden_message: Optional[str] = None,
):
"""Add a scope for collecting instructions into this circuit.
This should only be done by the control-flow context managers, which will handle cleaning up
after themselves at the end as well.
Args:
qubits: Any qubits that this scope should automatically use.
clbits: Any clbits that this scope should automatically use.
allow_jumps: Whether this scope allows jumps to be used within it.
forbidden_message: If given, all attempts to add instructions to this scope will raise a
:exc:`.CircuitError` with this message.
"""
# pylint: disable=cyclic-import
from qiskit.circuit.controlflow.builder import ControlFlowBuilderBlock
# Chain resource requests so things like registers added to inner scopes via conditions are
# requested in the outer scope as well.
if self._control_flow_scopes:
resource_requester = self._control_flow_scopes[-1].request_classical_resource
else:
resource_requester = self._resolve_classical_resource
self._control_flow_scopes.append(
ControlFlowBuilderBlock(
qubits,
clbits,
resource_requester=resource_requester,
registers=registers,
allow_jumps=allow_jumps,
forbidden_message=forbidden_message,
)
)
def _pop_scope(self) -> "qiskit.circuit.controlflow.builder.ControlFlowBuilderBlock":
"""Finish a scope used in the control-flow builder interface, and return it to the caller.
This should only be done by the control-flow context managers, since they naturally
synchronise the creation and deletion of stack elements."""
return self._control_flow_scopes.pop()
def _peek_previous_instruction_in_scope(self) -> CircuitInstruction:
"""Return the instruction 3-tuple of the most recent instruction in the current scope, even
if that scope is currently under construction.
This function is only intended for use by the control-flow ``if``-statement builders, which
may need to modify a previous instruction."""
if self._control_flow_scopes:
return self._control_flow_scopes[-1].peek()
if not self._data:
raise CircuitError("This circuit contains no instructions.")
return self._data[-1]
def _pop_previous_instruction_in_scope(self) -> CircuitInstruction:
"""Return the instruction 3-tuple of the most recent instruction in the current scope, even
if that scope is currently under construction, and remove it from that scope.
This function is only intended for use by the control-flow ``if``-statement builders, which
may need to replace a previous instruction with another.
"""
if self._control_flow_scopes:
return self._control_flow_scopes[-1].pop()
if not self._data:
raise CircuitError("This circuit contains no instructions.")
instruction = self._data.pop()
if isinstance(instruction.operation, Instruction):
self._update_parameter_table_on_instruction_removal(instruction)
return instruction
def _update_parameter_table_on_instruction_removal(self, instruction: CircuitInstruction):
"""Update the :obj:`.ParameterTable` of this circuit given that an instance of the given
``instruction`` has just been removed from the circuit.
.. note::
This does not account for the possibility for the same instruction instance being added
more than once to the circuit. At the time of writing (2021-11-17, main commit 271a82f)
there is a defensive ``deepcopy`` of parameterised instructions inside
:meth:`.QuantumCircuit.append`, so this should be safe. Trying to account for it would
involve adding a potentially quadratic-scaling loop to check each entry in ``data``.
"""
atomic_parameters: list[tuple[Parameter, int]] = []
for index, parameter in enumerate(instruction.operation.params):
if isinstance(parameter, (ParameterExpression, QuantumCircuit)):
atomic_parameters.extend((p, index) for p in parameter.parameters)
for atomic_parameter, index in atomic_parameters:
new_entries = self._parameter_table[atomic_parameter].copy()
new_entries.discard((instruction.operation, index))
if not new_entries:
del self._parameter_table[atomic_parameter]
# Invalidate cache.
self._parameters = None
else:
self._parameter_table[atomic_parameter] = new_entries
@typing.overload
def while_loop(
self,
condition: tuple[ClassicalRegister | Clbit, int] | expr.Expr,
body: None,
qubits: None,
clbits: None,
*,
label: str | None,
) -> "qiskit.circuit.controlflow.while_loop.WhileLoopContext":
...
@typing.overload
def while_loop(
self,
condition: tuple[ClassicalRegister | Clbit, int] | expr.Expr,
body: "QuantumCircuit",
qubits: Sequence[QubitSpecifier],
clbits: Sequence[ClbitSpecifier],
*,
label: str | None,
) -> InstructionSet:
...
def while_loop(self, condition, body=None, qubits=None, clbits=None, *, label=None):
"""Create a ``while`` loop on this circuit.
There are two forms for calling this function. If called with all its arguments (with the
possible exception of ``label``), it will create a
:obj:`~qiskit.circuit.controlflow.WhileLoopOp` with the given ``body``. If ``body`` (and
``qubits`` and ``clbits``) are *not* passed, then this acts as a context manager, which
will automatically build a :obj:`~qiskit.circuit.controlflow.WhileLoopOp` when the scope
finishes. In this form, you do not need to keep track of the qubits or clbits you are
using, because the scope will handle it for you.
Example usage::
from qiskit.circuit import QuantumCircuit, Clbit, Qubit
bits = [Qubit(), Qubit(), Clbit()]
qc = QuantumCircuit(bits)
with qc.while_loop((bits[2], 0)):
qc.h(0)
qc.cx(0, 1)
qc.measure(0, 0)
Args:
condition (Tuple[Union[ClassicalRegister, Clbit], int]): An equality condition to be
checked prior to executing ``body``. The left-hand side of the condition must be a
:obj:`~ClassicalRegister` or a :obj:`~Clbit`, and the right-hand side must be an
integer or boolean.
body (Optional[QuantumCircuit]): The loop body to be repeatedly executed. Omit this to
use the context-manager mode.
qubits (Optional[Sequence[Qubit]]): The circuit qubits over which the loop body should
be run. Omit this to use the context-manager mode.
clbits (Optional[Sequence[Clbit]]): The circuit clbits over which the loop body should
be run. Omit this to use the context-manager mode.
label (Optional[str]): The string label of the instruction in the circuit.
Returns:
InstructionSet or WhileLoopContext: If used in context-manager mode, then this should be
used as a ``with`` resource, which will infer the block content and operands on exit.
If the full form is used, then this returns a handle to the instructions created.
Raises:
CircuitError: if an incorrect calling convention is used.
"""
# pylint: disable=cyclic-import
from qiskit.circuit.controlflow.while_loop import WhileLoopOp, WhileLoopContext
if isinstance(condition, expr.Expr):
condition = self._validate_expr(condition)
else:
condition = (self._resolve_classical_resource(condition[0]), condition[1])
if body is None:
if qubits is not None or clbits is not None:
raise CircuitError(
"When using 'while_loop' as a context manager,"
" you cannot pass qubits or clbits."
)
return WhileLoopContext(self, condition, label=label)
elif qubits is None or clbits is None:
raise CircuitError(
"When using 'while_loop' with a body, you must pass qubits and clbits."
)
return self.append(WhileLoopOp(condition, body, label), qubits, clbits)
@typing.overload
def for_loop(
self,
indexset: Iterable[int],
loop_parameter: Parameter | None,
body: None,
qubits: None,
clbits: None,
*,
label: str | None,
) -> "qiskit.circuit.controlflow.for_loop.ForLoopContext":
...
@typing.overload
def for_loop(
self,
indexset: Iterable[int],
loop_parameter: Union[Parameter, None],
body: "QuantumCircuit",
qubits: Sequence[QubitSpecifier],
clbits: Sequence[ClbitSpecifier],
*,
label: str | None,
) -> InstructionSet:
...
def for_loop(
self, indexset, loop_parameter=None, body=None, qubits=None, clbits=None, *, label=None
):
"""Create a ``for`` loop on this circuit.
There are two forms for calling this function. If called with all its arguments (with the
possible exception of ``label``), it will create a
:class:`~qiskit.circuit.ForLoopOp` with the given ``body``. If ``body`` (and
``qubits`` and ``clbits``) are *not* passed, then this acts as a context manager, which,
when entered, provides a loop variable (unless one is given, in which case it will be
reused) and will automatically build a :class:`~qiskit.circuit.ForLoopOp` when the
scope finishes. In this form, you do not need to keep track of the qubits or clbits you are
using, because the scope will handle it for you.
For example::
from qiskit import QuantumCircuit
qc = QuantumCircuit(2, 1)
with qc.for_loop(range(5)) as i:
qc.h(0)
qc.cx(0, 1)
qc.measure(0, 0)
qc.break_loop().c_if(0, True)
Args:
indexset (Iterable[int]): A collection of integers to loop over. Always necessary.
loop_parameter (Optional[Parameter]): The parameter used within ``body`` to which
the values from ``indexset`` will be assigned. In the context-manager form, if this
argument is not supplied, then a loop parameter will be allocated for you and
returned as the value of the ``with`` statement. This will only be bound into the
circuit if it is used within the body.
If this argument is ``None`` in the manual form of this method, ``body`` will be
repeated once for each of the items in ``indexset`` but their values will be
ignored.
body (Optional[QuantumCircuit]): The loop body to be repeatedly executed. Omit this to
use the context-manager mode.
qubits (Optional[Sequence[QubitSpecifier]]): The circuit qubits over which the loop body
should be run. Omit this to use the context-manager mode.
clbits (Optional[Sequence[ClbitSpecifier]]): The circuit clbits over which the loop body
should be run. Omit this to use the context-manager mode.
label (Optional[str]): The string label of the instruction in the circuit.
Returns:
InstructionSet or ForLoopContext: depending on the call signature, either a context
manager for creating the for loop (it will automatically be added to the circuit at the
end of the block), or an :obj:`~InstructionSet` handle to the appended loop operation.
Raises:
CircuitError: if an incorrect calling convention is used.
"""
# pylint: disable=cyclic-import
from qiskit.circuit.controlflow.for_loop import ForLoopOp, ForLoopContext
if body is None:
if qubits is not None or clbits is not None:
raise CircuitError(
"When using 'for_loop' as a context manager, you cannot pass qubits or clbits."
)
return ForLoopContext(self, indexset, loop_parameter, label=label)
elif qubits is None or clbits is None:
raise CircuitError(
"When using 'for_loop' with a body, you must pass qubits and clbits."
)
return self.append(ForLoopOp(indexset, loop_parameter, body, label), qubits, clbits)
@typing.overload
def if_test(
self,
condition: tuple[ClassicalRegister | Clbit, int],
true_body: None,
qubits: None,
clbits: None,
*,
label: str | None,
) -> "qiskit.circuit.controlflow.if_else.IfContext":
...
@typing.overload
def if_test(
self,
condition: tuple[ClassicalRegister | Clbit, int],
true_body: "QuantumCircuit",
qubits: Sequence[QubitSpecifier],
clbits: Sequence[ClbitSpecifier],
*,
label: str | None = None,
) -> InstructionSet:
...
def if_test(
self,
condition,
true_body=None,
qubits=None,
clbits=None,
*,
label=None,
):
"""Create an ``if`` statement on this circuit.
There are two forms for calling this function. If called with all its arguments (with the
possible exception of ``label``), it will create a
:obj:`~qiskit.circuit.IfElseOp` with the given ``true_body``, and there will be
no branch for the ``false`` condition (see also the :meth:`.if_else` method). However, if
``true_body`` (and ``qubits`` and ``clbits``) are *not* passed, then this acts as a context
manager, which can be used to build ``if`` statements. The return value of the ``with``
statement is a chainable context manager, which can be used to create subsequent ``else``
blocks. In this form, you do not need to keep track of the qubits or clbits you are using,
because the scope will handle it for you.
For example::
from qiskit.circuit import QuantumCircuit, Qubit, Clbit
bits = [Qubit(), Qubit(), Qubit(), Clbit(), Clbit()]
qc = QuantumCircuit(bits)
qc.h(0)
qc.cx(0, 1)
qc.measure(0, 0)
qc.h(0)
qc.cx(0, 1)
qc.measure(0, 1)
with qc.if_test((bits[3], 0)) as else_:
qc.x(2)
with else_:
qc.h(2)
qc.z(2)
Args:
condition (Tuple[Union[ClassicalRegister, Clbit], int]): A condition to be evaluated at
circuit runtime which, if true, will trigger the evaluation of ``true_body``. Can be
specified as either a tuple of a ``ClassicalRegister`` to be tested for equality
with a given ``int``, or as a tuple of a ``Clbit`` to be compared to either a
``bool`` or an ``int``.
true_body (Optional[QuantumCircuit]): The circuit body to be run if ``condition`` is
true.
qubits (Optional[Sequence[QubitSpecifier]]): The circuit qubits over which the if/else
should be run.
clbits (Optional[Sequence[ClbitSpecifier]]): The circuit clbits over which the if/else
should be run.
label (Optional[str]): The string label of the instruction in the circuit.
Returns:
InstructionSet or IfContext: depending on the call signature, either a context
manager for creating the ``if`` block (it will automatically be added to the circuit at
the end of the block), or an :obj:`~InstructionSet` handle to the appended conditional
operation.
Raises:
CircuitError: If the provided condition references Clbits outside the
enclosing circuit.
CircuitError: if an incorrect calling convention is used.
Returns:
A handle to the instruction created.
"""
# pylint: disable=cyclic-import
from qiskit.circuit.controlflow.if_else import IfElseOp, IfContext
if isinstance(condition, expr.Expr):
condition = self._validate_expr(condition)
else:
condition = (self._resolve_classical_resource(condition[0]), condition[1])
if true_body is None:
if qubits is not None or clbits is not None:
raise CircuitError(
"When using 'if_test' as a context manager, you cannot pass qubits or clbits."
)
# We can only allow jumps if we're in a loop block, but the default path (no scopes)
# also allows adding jumps to support the more verbose internal mode.
in_loop = bool(self._control_flow_scopes and self._control_flow_scopes[-1].allow_jumps)
return IfContext(self, condition, in_loop=in_loop, label=label)
elif qubits is None or clbits is None:
raise CircuitError("When using 'if_test' with a body, you must pass qubits and clbits.")
return self.append(IfElseOp(condition, true_body, None, label), qubits, clbits)
def if_else(
self,
condition: tuple[ClassicalRegister, int] | tuple[Clbit, int] | tuple[Clbit, bool],
true_body: "QuantumCircuit",
false_body: "QuantumCircuit",
qubits: Sequence[QubitSpecifier],
clbits: Sequence[ClbitSpecifier],
label: str | None = None,
) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.IfElseOp`.
.. note::
This method does not have an associated context-manager form, because it is already
handled by the :meth:`.if_test` method. You can use the ``else`` part of that with
something such as::
from qiskit.circuit import QuantumCircuit, Qubit, Clbit
bits = [Qubit(), Qubit(), Clbit()]
qc = QuantumCircuit(bits)
qc.h(0)
qc.cx(0, 1)
qc.measure(0, 0)
with qc.if_test((bits[2], 0)) as else_:
qc.h(0)
with else_:
qc.x(0)
Args:
condition: A condition to be evaluated at circuit runtime which,
if true, will trigger the evaluation of ``true_body``. Can be
specified as either a tuple of a ``ClassicalRegister`` to be
tested for equality with a given ``int``, or as a tuple of a
``Clbit`` to be compared to either a ``bool`` or an ``int``.
true_body: The circuit body to be run if ``condition`` is true.
false_body: The circuit to be run if ``condition`` is false.
qubits: The circuit qubits over which the if/else should be run.
clbits: The circuit clbits over which the if/else should be run.
label: The string label of the instruction in the circuit.
Raises:
CircuitError: If the provided condition references Clbits outside the
enclosing circuit.
Returns:
A handle to the instruction created.
"""
# pylint: disable=cyclic-import
from qiskit.circuit.controlflow.if_else import IfElseOp
if isinstance(condition, expr.Expr):
condition = self._validate_expr(condition)
else:
condition = (self._resolve_classical_resource(condition[0]), condition[1])
return self.append(IfElseOp(condition, true_body, false_body, label), qubits, clbits)
@typing.overload
def switch(
self,
target: Union[ClbitSpecifier, ClassicalRegister],
cases: None,
qubits: None,
clbits: None,
*,
label: Optional[str],
) -> "qiskit.circuit.controlflow.switch_case.SwitchContext":
...
@typing.overload
def switch(
self,
target: Union[ClbitSpecifier, ClassicalRegister],
cases: Iterable[Tuple[typing.Any, QuantumCircuit]],
qubits: Sequence[QubitSpecifier],
clbits: Sequence[ClbitSpecifier],
*,
label: Optional[str],
) -> InstructionSet:
...
def switch(self, target, cases=None, qubits=None, clbits=None, *, label=None):
"""Create a ``switch``/``case`` structure on this circuit.
There are two forms for calling this function. If called with all its arguments (with the
possible exception of ``label``), it will create a :class:`.SwitchCaseOp` with the given
case structure. If ``cases`` (and ``qubits`` and ``clbits``) are *not* passed, then this
acts as a context manager, which will automatically build a :class:`.SwitchCaseOp` when the
scope finishes. In this form, you do not need to keep track of the qubits or clbits you are
using, because the scope will handle it for you.
Example usage::
from qiskit.circuit import QuantumCircuit, ClassicalRegister, QuantumRegister
qreg = QuantumRegister(3)
creg = ClassicalRegister(3)
qc = QuantumCircuit(qreg, creg)
qc.h([0, 1, 2])
qc.measure([0, 1, 2], [0, 1, 2])
with qc.switch(creg) as case:
with case(0):
qc.x(0)
with case(1, 2):
qc.z(1)
with case(case.DEFAULT):
qc.cx(0, 1)
Args:
target (Union[ClassicalRegister, Clbit]): The classical value to switch one. This must
be integer-like.
cases (Iterable[Tuple[typing.Any, QuantumCircuit]]): A sequence of case specifiers.
Each tuple defines one case body (the second item). The first item of the tuple can
be either a single integer value, the special value :data:`.CASE_DEFAULT`, or a
tuple of several integer values. Each of the integer values will be tried in turn;
control will then pass to the body corresponding to the first match.
:data:`.CASE_DEFAULT` matches all possible values. Omit in context-manager form.
qubits (Sequence[Qubit]): The circuit qubits over which all case bodies execute. Omit in
context-manager form.
clbits (Sequence[Clbit]): The circuit clbits over which all case bodies execute. Omit in
context-manager form.
label (Optional[str]): The string label of the instruction in the circuit.
Returns:
InstructionSet or SwitchCaseContext: If used in context-manager mode, then this should
be used as a ``with`` resource, which will return an object that can be repeatedly
entered to produce cases for the switch statement. If the full form is used, then this
returns a handle to the instructions created.
Raises:
CircuitError: if an incorrect calling convention is used.
"""
# pylint: disable=cyclic-import
from qiskit.circuit.controlflow.switch_case import SwitchCaseOp, SwitchContext
if isinstance(target, expr.Expr):
target = self._validate_expr(target)
else:
target = self._resolve_classical_resource(target)
if cases is None:
if qubits is not None or clbits is not None:
raise CircuitError(
"When using 'switch' as a context manager, you cannot pass qubits or clbits."
)
in_loop = bool(self._control_flow_scopes and self._control_flow_scopes[-1].allow_jumps)
return SwitchContext(self, target, in_loop=in_loop, label=label)
if qubits is None or clbits is None:
raise CircuitError("When using 'switch' with cases, you must pass qubits and clbits.")
return self.append(SwitchCaseOp(target, cases, label=label), qubits, clbits)
def break_loop(self) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.BreakLoopOp`.
.. warning::
If you are using the context-manager "builder" forms of :meth:`.if_test`,
:meth:`.for_loop` or :meth:`.while_loop`, you can only call this method if you are
within a loop context, because otherwise the "resource width" of the operation cannot be
determined. This would quickly lead to invalid circuits, and so if you are trying to
construct a reusable loop body (without the context managers), you must also use the
non-context-manager form of :meth:`.if_test` and :meth:`.if_else`. Take care that the
:obj:`.BreakLoopOp` instruction must span all the resources of its containing loop, not
just the immediate scope.
Returns:
A handle to the instruction created.
Raises:
CircuitError: if this method was called within a builder context, but not contained
within a loop.
"""
# pylint: disable=cyclic-import
from qiskit.circuit.controlflow.break_loop import BreakLoopOp, BreakLoopPlaceholder
if self._control_flow_scopes:
operation = BreakLoopPlaceholder()
resources = operation.placeholder_resources()
return self.append(operation, resources.qubits, resources.clbits)
return self.append(BreakLoopOp(self.num_qubits, self.num_clbits), self.qubits, self.clbits)
def continue_loop(self) -> InstructionSet:
"""Apply :class:`~qiskit.circuit.ContinueLoopOp`.
.. warning::
If you are using the context-manager "builder" forms of :meth:`.if_test`,
:meth:`.for_loop` or :meth:`.while_loop`, you can only call this method if you are
within a loop context, because otherwise the "resource width" of the operation cannot be
determined. This would quickly lead to invalid circuits, and so if you are trying to
construct a reusable loop body (without the context managers), you must also use the
non-context-manager form of :meth:`.if_test` and :meth:`.if_else`. Take care that the
:class:`~qiskit.circuit.ContinueLoopOp` instruction must span all the resources of its
containing loop, not just the immediate scope.
Returns:
A handle to the instruction created.
Raises:
CircuitError: if this method was called within a builder context, but not contained
within a loop.
"""
# pylint: disable=cyclic-import
from qiskit.circuit.controlflow.continue_loop import ContinueLoopOp, ContinueLoopPlaceholder
if self._control_flow_scopes:
operation = ContinueLoopPlaceholder()
resources = operation.placeholder_resources()
return self.append(operation, resources.qubits, resources.clbits)
return self.append(
ContinueLoopOp(self.num_qubits, self.num_clbits), self.qubits, self.clbits
)
def add_calibration(
self,
gate: Union[Gate, str],
qubits: Sequence[int],
# Schedule has the type `qiskit.pulse.Schedule`, but `qiskit.pulse` cannot be imported
# while this module is, and so Sphinx will not accept a forward reference to it. Sphinx
# needs the types available at runtime, whereas mypy will accept it, because it handles the
# type checking by static analysis.
schedule,
params: Sequence[ParameterValueType] | None = None,
) -> None:
"""Register a low-level, custom pulse definition for the given gate.
Args:
gate (Union[Gate, str]): Gate information.
qubits (Union[int, Tuple[int]]): List of qubits to be measured.
schedule (Schedule): Schedule information.
params (Optional[List[Union[float, Parameter]]]): A list of parameters.
Raises:
Exception: if the gate is of type string and params is None.
"""
def _format(operand):
try:
# Using float/complex value as a dict key is not good idea.
# This makes the mapping quite sensitive to the rounding error.
# However, the mechanism is already tied to the execution model (i.e. pulse gate)
# and we cannot easily update this rule.
# The same logic exists in DAGCircuit.add_calibration.
evaluated = complex(operand)
if np.isreal(evaluated):
evaluated = float(evaluated.real)
if evaluated.is_integer():
evaluated = int(evaluated)
return evaluated
except TypeError:
# Unassigned parameter
return operand
if isinstance(gate, Gate):
params = gate.params
gate = gate.name
if params is not None:
params = tuple(map(_format, params))
else:
params = ()
self._calibrations[gate][(tuple(qubits), params)] = schedule
# Functions only for scheduled circuits
def qubit_duration(self, *qubits: Union[Qubit, int]) -> float:
"""Return the duration between the start and stop time of the first and last instructions,
excluding delays, over the supplied qubits. Its time unit is ``self.unit``.
Args:
*qubits: Qubits within ``self`` to include.
Returns:
Return the duration between the first start and last stop time of non-delay instructions
"""
return self.qubit_stop_time(*qubits) - self.qubit_start_time(*qubits)
def qubit_start_time(self, *qubits: Union[Qubit, int]) -> float:
"""Return the start time of the first instruction, excluding delays,
over the supplied qubits. Its time unit is ``self.unit``.
Return 0 if there are no instructions over qubits
Args:
*qubits: Qubits within ``self`` to include. Integers are allowed for qubits, indicating
indices of ``self.qubits``.
Returns:
Return the start time of the first instruction, excluding delays, over the qubits
Raises:
CircuitError: if ``self`` is a not-yet scheduled circuit.
"""
if self.duration is None:
# circuit has only delays, this is kind of scheduled
for instruction in self._data:
if not isinstance(instruction.operation, Delay):
raise CircuitError(
"qubit_start_time undefined. Circuit must be scheduled first."
)
return 0
qubits = [self.qubits[q] if isinstance(q, int) else q for q in qubits]
starts = {q: 0 for q in qubits}
dones = {q: False for q in qubits}
for instruction in self._data:
for q in qubits:
if q in instruction.qubits:
if isinstance(instruction.operation, Delay):
if not dones[q]:
starts[q] += instruction.operation.duration
else:
dones[q] = True
if len(qubits) == len([done for done in dones.values() if done]): # all done
return min(start for start in starts.values())
return 0 # If there are no instructions over bits
def qubit_stop_time(self, *qubits: Union[Qubit, int]) -> float:
"""Return the stop time of the last instruction, excluding delays, over the supplied qubits.
Its time unit is ``self.unit``.
Return 0 if there are no instructions over qubits
Args:
*qubits: Qubits within ``self`` to include. Integers are allowed for qubits, indicating
indices of ``self.qubits``.
Returns:
Return the stop time of the last instruction, excluding delays, over the qubits
Raises:
CircuitError: if ``self`` is a not-yet scheduled circuit.
"""
if self.duration is None:
# circuit has only delays, this is kind of scheduled
for instruction in self._data:
if not isinstance(instruction.operation, Delay):
raise CircuitError(
"qubit_stop_time undefined. Circuit must be scheduled first."
)
return 0
qubits = [self.qubits[q] if isinstance(q, int) else q for q in qubits]
stops = {q: self.duration for q in qubits}
dones = {q: False for q in qubits}
for instruction in reversed(self._data):
for q in qubits:
if q in instruction.qubits:
if isinstance(instruction.operation, Delay):
if not dones[q]:
stops[q] -= instruction.operation.duration
else:
dones[q] = True
if len(qubits) == len([done for done in dones.values() if done]): # all done
return max(stop for stop in stops.values())
return 0 # If there are no instructions over bits
class _ParameterBindsDict:
__slots__ = ("mapping", "allowed_keys")
def __init__(self, mapping, allowed_keys):
self.mapping = mapping
self.allowed_keys = allowed_keys
def items(self):
"""Iterator through all the keys in the mapping that we care about. Wrapping the main
mapping allows us to avoid reconstructing a new 'dict', but just use the given 'mapping'
without any copy / reconstruction."""
for parameter, value in self.mapping.items():
if parameter in self.allowed_keys:
yield parameter, value
class _ParameterBindsSequence:
__slots__ = ("parameters", "values", "mapping_cache")
def __init__(self, parameters, values):
self.parameters = parameters
self.values = values
self.mapping_cache = None
def items(self):
"""Iterator through all the keys in the mapping that we care about."""
return zip(self.parameters, self.values)
@property
def mapping(self):
"""Cached version of a mapping. This is only generated on demand."""
if self.mapping_cache is None:
self.mapping_cache = dict(zip(self.parameters, self.values))
return self.mapping_cache
# Used by the OQ2 exporter. Just needs to have enough parameters to support the largest standard
# (non-controlled) gate in our standard library. We have to use the same `Parameter` instances each
# time so the equality comparisons will work.
_QASM2_FIXED_PARAMETERS = [Parameter("param0"), Parameter("param1"), Parameter("param2")]
def _qasm2_custom_operation_statement(
instruction, existing_gate_names, gates_to_define, bit_labels
):
operation = _qasm2_define_custom_operation(
instruction.operation, existing_gate_names, gates_to_define
)
# Insert qasm representation of the original instruction
if instruction.clbits:
bits = itertools.chain(instruction.qubits, instruction.clbits)
else:
bits = instruction.qubits
bits_qasm = ",".join(bit_labels[j] for j in bits)
instruction_qasm = f"{_instruction_qasm2(operation)} {bits_qasm};"
return instruction_qasm
def _qasm2_define_custom_operation(operation, existing_gate_names, gates_to_define):
"""Extract a custom definition from the given operation, and append any necessary additional
subcomponents' definitions to the ``gates_to_define`` ordered dictionary.
Returns a potentially new :class:`.Instruction`, which should be used for the
:meth:`~.Instruction.qasm` call (it may have been renamed)."""
# pylint: disable=cyclic-import
from qiskit.circuit import library as lib
from qiskit.qasm2 import QASM2ExportError
if operation.name in existing_gate_names:
return operation
# Check instructions names or label are valid
escaped = _qasm_escape_name(operation.name, "gate_")
if escaped != operation.name:
operation = operation.copy(name=escaped)
# These are built-in gates that are known to be safe to construct by passing the correct number
# of `Parameter` instances positionally, and have no other information. We can't guarantee that
# if they've been subclassed, though. This is a total hack; ideally we'd be able to inspect the
# "calling" signatures of Qiskit `Gate` objects to know whether they're safe to re-parameterise.
known_good_parameterized = {
lib.PhaseGate,
lib.RGate,
lib.RXGate,
lib.RXXGate,
lib.RYGate,
lib.RYYGate,
lib.RZGate,
lib.RZXGate,
lib.RZZGate,
lib.XXMinusYYGate,
lib.XXPlusYYGate,
lib.UGate,
lib.U1Gate,
lib.U2Gate,
lib.U3Gate,
}
# In known-good situations we want to use a manually parametrised object as the source of the
# definition, but still continue to return the given object as the call-site object.
if type(operation) in known_good_parameterized:
parameterized_operation = type(operation)(*_QASM2_FIXED_PARAMETERS[: len(operation.params)])
elif hasattr(operation, "_qasm2_decomposition"):
new_op = operation._qasm2_decomposition()
parameterized_operation = operation = new_op.copy(
name=_qasm_escape_name(new_op.name, "gate_")
)
else:
parameterized_operation = operation
# If there's an _equal_ operation in the existing circuits to be defined, then our job is done.
previous_definition_source, _ = gates_to_define.get(operation.name, (None, None))
if parameterized_operation == previous_definition_source:
return operation
# Otherwise, if there's a naming clash, we need a unique name.
if operation.name in gates_to_define:
operation = _rename_operation(operation)
new_name = operation.name
if parameterized_operation.params:
parameters_qasm = (
"(" + ",".join(f"param{i}" for i in range(len(parameterized_operation.params))) + ")"
)
else:
parameters_qasm = ""
if operation.num_qubits == 0:
raise QASM2ExportError(
f"OpenQASM 2 cannot represent '{operation.name}, which acts on zero qubits."
)
if operation.num_clbits != 0:
raise QASM2ExportError(
f"OpenQASM 2 cannot represent '{operation.name}', which acts on {operation.num_clbits}"
" classical bits."
)
qubits_qasm = ",".join(f"q{i}" for i in range(parameterized_operation.num_qubits))
parameterized_definition = getattr(parameterized_operation, "definition", None)
if parameterized_definition is None:
gates_to_define[new_name] = (
parameterized_operation,
f"opaque {new_name}{parameters_qasm} {qubits_qasm};",
)
else:
qubit_labels = {bit: f"q{i}" for i, bit in enumerate(parameterized_definition.qubits)}
body_qasm = " ".join(
_qasm2_custom_operation_statement(
instruction, existing_gate_names, gates_to_define, qubit_labels
)
for instruction in parameterized_definition.data
)
# if an inner operation has the same name as the actual operation, it needs to be renamed
if operation.name in gates_to_define:
operation = _rename_operation(operation)
new_name = operation.name
definition_qasm = f"gate {new_name}{parameters_qasm} {qubits_qasm} {{ {body_qasm} }}"
gates_to_define[new_name] = (parameterized_operation, definition_qasm)
return operation
def _rename_operation(operation):
"""Returns the operation with a new name following this pattern: {operation name}_{operation id}"""
new_name = f"{operation.name}_{id(operation)}"
updated_operation = operation.copy(name=new_name)
return updated_operation
def _qasm_escape_name(name: str, prefix: str) -> str:
"""Returns a valid OpenQASM identifier, using `prefix` as a prefix if necessary. `prefix` must
itself be a valid identifier."""
# Replace all non-ASCII-word characters (letters, digits, underscore) with the underscore.
escaped_name = re.sub(r"\W", "_", name, flags=re.ASCII)
if (
not escaped_name
or escaped_name[0] not in string.ascii_lowercase
or escaped_name in QASM2_RESERVED
):
escaped_name = prefix + escaped_name
return escaped_name
def _instruction_qasm2(operation):
"""Return an OpenQASM 2 string for the instruction."""
from qiskit.qasm2 import QASM2ExportError # pylint: disable=cyclic-import
if operation.name == "c3sx":
qasm2_call = "c3sqrtx"
else:
qasm2_call = operation.name
if operation.params:
qasm2_call = "{}({})".format(
qasm2_call,
",".join([pi_check(i, output="qasm", eps=1e-12) for i in operation.params]),
)
if operation.condition is not None:
if not isinstance(operation.condition[0], ClassicalRegister):
raise QASM2ExportError(
"OpenQASM 2 can only condition on registers, but got '{operation.condition[0]}'"
)
qasm2_call = (
"if(%s==%d) " % (operation.condition[0].name, operation.condition[1]) + qasm2_call
)
return qasm2_call
def _make_unique(name: str, already_defined: collections.abc.Set[str]) -> str:
"""Generate a name by suffixing the given stem that is unique within the defined set."""
if name not in already_defined:
return name
used = {in_use[len(name) :] for in_use in already_defined if in_use.startswith(name)}
characters = (string.digits + string.ascii_letters) if name else string.ascii_letters
for parts in itertools.chain.from_iterable(
itertools.product(characters, repeat=n) for n in itertools.count(1)
):
suffix = "".join(parts)
if suffix not in used:
return name + suffix
# This isn't actually reachable because the above loop is infinite.
return name
def _bit_argument_conversion(specifier, bit_sequence, bit_set, type_) -> list[Bit]:
"""Get the list of bits referred to by the specifier ``specifier``.
Valid types for ``specifier`` are integers, bits of the correct type (as given in ``type_``), or
iterables of one of those two scalar types. Integers are interpreted as indices into the
sequence ``bit_sequence``. All allowed bits must be in ``bit_set`` (which should implement
fast lookup), which is assumed to contain the same bits as ``bit_sequence``.
Returns:
List[Bit]: a list of the specified bits from ``bits``.
Raises:
CircuitError: if an incorrect type or index is encountered, if the same bit is specified
more than once, or if the specifier is to a bit not in the ``bit_set``.
"""
# The duplication between this function and `_bit_argument_conversion_scalar` is so that fast
# paths return as quickly as possible, and all valid specifiers will resolve without needing to
# try/catch exceptions (which is too slow for inner-loop code).
if isinstance(specifier, type_):
if specifier in bit_set:
return [specifier]
raise CircuitError(f"Bit '{specifier}' is not in the circuit.")
if isinstance(specifier, (int, np.integer)):
try:
return [bit_sequence[specifier]]
except IndexError as ex:
raise CircuitError(
f"Index {specifier} out of range for size {len(bit_sequence)}."
) from ex
# Slices can't raise IndexError - they just return an empty list.
if isinstance(specifier, slice):
return bit_sequence[specifier]
try:
return [
_bit_argument_conversion_scalar(index, bit_sequence, bit_set, type_)
for index in specifier
]
except TypeError as ex:
message = (
f"Incorrect bit type: expected '{type_.__name__}' but got '{type(specifier).__name__}'"
if isinstance(specifier, Bit)
else f"Invalid bit index: '{specifier}' of type '{type(specifier)}'"
)
raise CircuitError(message) from ex
def _bit_argument_conversion_scalar(specifier, bit_sequence, bit_set, type_):
if isinstance(specifier, type_):
if specifier in bit_set:
return specifier
raise CircuitError(f"Bit '{specifier}' is not in the circuit.")
if isinstance(specifier, (int, np.integer)):
try:
return bit_sequence[specifier]
except IndexError as ex:
raise CircuitError(
f"Index {specifier} out of range for size {len(bit_sequence)}."
) from ex
message = (
f"Incorrect bit type: expected '{type_.__name__}' but got '{type(specifier).__name__}'"
if isinstance(specifier, Bit)
else f"Invalid bit index: '{specifier}' of type '{type(specifier)}'"
)
raise CircuitError(message)
|
https://github.com/Q-MAB/Qiskit-FashionMNIST-Case
|
Q-MAB
|
#####################################################################################################
#This code is part of the Medium story 'Hybrid Quantum-Classical Neural Network for classification
#of images in FashionMNIST dataset' case study. Parts of the code may have been imported from Qiskit
#textbook and from the exercises of the Qiskit global summer school on Quantum Machine Learning. The
#case study was a challenge to put insights from the Qiskit summer school into practice and explore
#the hybrid quantum-classical neural network architecture.
#A story on the case study can be found here: medium.com/QMAB
#####################################################################################################
import numpy as np
import torch
from torchvision.transforms import ToTensor
from torch import cat, no_grad, manual_seed
from torchvision import datasets, transforms
import torch.optim as optim
from torch.nn import (Module, Conv2d, Linear, Dropout2d, NLLLoss, MaxPool2d, Flatten, Sequential, ReLU)
from torch.autograd import Function
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from torch import Tensor
from torch.optim import LBFGS
from torchviz import make_dot
import torchvision
from torchvision.io import read_image
from torch.autograd import Variable
import qiskit
from qiskit import transpile, assemble
from qiskit.visualization import *
from qiskit import Aer, QuantumCircuit
from qiskit.utils import QuantumInstance
from qiskit.opflow import AerPauliExpectation
from qiskit.circuit import Parameter
from qiskit.circuit.library import RealAmplitudes, ZZFeatureMap
from qiskit_machine_learning.neural_networks import CircuitQNN, TwoLayerQNN
from qiskit_machine_learning.connectors import TorchConnector
import matplotlib.pyplot as plt
import os
import pandas as pd
# Declare Quantum instance
qi = QuantumInstance(Aer.get_backend('aer_simulator_statevector'))
### Training and test data downloaded from FashionMNIST and transformed into tensors ###
training_data = datasets.FashionMNIST(
root="data",
train=True,
download=True,
transform=ToTensor()
)
test_data = datasets.FashionMNIST(
root="data",
train=False,
download=True,
transform=ToTensor()
)
### Inspecting the images in the training data set with their labels ###
labels_map = {
0: "T-Shirt",
1: "Trouser",
2: "Pullover",
3: "Dress",
4: "Coat",
5: "Sandal",
6: "Shirt",
7: "Sneaker",
8: "Bag",
9: "Ankle Boot",
}
figure = plt.figure(figsize=(8, 8))
cols, rows = 3, 3
for i in range(1, cols * rows + 1):
sample_idx = torch.randint(len(training_data), size=(1,)).item()
img, label = training_data[sample_idx]
figure.add_subplot(rows, cols, i)
plt.title(labels_map[label])
plt.axis("off")
plt.imshow(img.squeeze(), cmap="gray")
plt.show()
### Load training data into Torch DataLoader ###
X_train = training_data
n_samples = 500
batch_size = 64
# Filter out labels (originally 0-9), leaving only labels 0 and 1
idx = np.append(np.where(X_train.targets == 0)[0][:n_samples],
np.where(X_train.targets == 1)[0][:n_samples])
X_train.data = X_train.data[idx]
X_train.targets = X_train.targets[idx]
# A torch dataloader is defined with filtered data
train_loader = DataLoader(X_train, batch_size=64, shuffle=True)
# Load test data into Torch DataLoader
X_test = test_data
# Filter out labels (originally 0-9), leaving only labels 0 and 1
idx = np.append(np.where(X_test.targets == 0)[0][:n_samples],
np.where(X_test.targets == 1)[0][:n_samples])
X_test.data = X_test.data[idx]
X_test.targets = X_test.targets[idx]
# Define torch dataloader with filtered data
test_loader = DataLoader(X_test, batch_size=64, shuffle=True)
### Two layer QNN constructed ###
feature_map = ZZFeatureMap(feature_dimension=2, entanglement='linear')
ansatz = RealAmplitudes(2, reps=1, entanglement='linear')
qnn2 = TwoLayerQNN(2, feature_map, ansatz, input_gradients=True, exp_val=AerPauliExpectation(), quantum_instance=qi)
print(qnn2.operator)
### Torch NN module from Qiskit ###
class Net(Module):
def __init__(self):
super().__init__()
self.conv1 = Conv2d(1, 2, kernel_size=5)
self.conv2 = Conv2d(2, 16, kernel_size=5)
self.dropout = Dropout2d()
self.fc1 = Linear(256, 64)
self.fc2 = Linear(64, 2) # 2-dimensional input to QNN
self.qnn = TorchConnector(qnn2) # Apply torch connector, weights chosen
self.fc3 = Linear(1, 1) # uniformly at random from interval [-1,1].
# 1-dimensional output from QNN
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.max_pool2d(x, 2)
x = F.relu(self.conv2(x))
x = F.max_pool2d(x, 2)
x = self.dropout(x)
x = x.view(x.shape[0], -1)
x = F.relu(self.fc1(x))
x = self.fc2(x)
x = self.qnn(x) # apply QNN
x = self.fc3(x)
return torch.cat((x, 1 - x), -1)
### Model trained and the loss computed ###
model = Net()
optimizer = optim.Adam(model.parameters(), lr=0.001)
loss_func = nn.NLLLoss()
epochs = 20
loss_list = []
model.train()
for epoch in range(epochs):
total_loss = []
for batch_idx, (data, target) in enumerate(train_loader):
optimizer.zero_grad()
# Forward pass
output = model(data)
# Calculating loss
loss = loss_func(output, target)
# Backward pass
loss.backward()
# Optimize the weights
optimizer.step()
total_loss.append(loss.item())
loss_list.append(sum(total_loss)/len(total_loss))
print('Training [{:.0f}%]\tLoss: {:.4f}'.format(
100. * (epoch + 1) / epochs, loss_list[-1]))
### Loss convergence plotted ###
plt.plot(loss_list)
plt.title('Hybrid NN Training Convergence')
plt.xlabel('Training Iterations')
plt.ylabel('Neg. Log Likelihood Loss')
plt.show()
### Model evaluated ###
model.eval()
with torch.no_grad():
correct = 0
for batch_idx, (data, target) in enumerate(test_loader):
output = model(data)
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
loss = loss_func(output, target)
total_loss.append(loss.item())
print('Performance on test data:\n\tLoss: {:.4f}\n\tAccuracy: {:.1f}%'.format(
sum(total_loss) / len(total_loss),
(correct / len(test_loader) / batch_size) * 100))
### Predicted images displayed. Either T-shirt or Trouser ###
n_samples_show = 5
count = 0
fig, axes = plt.subplots(nrows=1, ncols=n_samples_show, figsize=(15, 5))
model.eval()
with no_grad():
for batch_idx, (data, target) in enumerate(test_loader):
if count == n_samples_show:
break
output = model(data[0:1])
if len(output.shape) == 1:
output = output.reshape(1, *output.shape)
pred = output.argmax(dim=1, keepdim=True)
axes[count].imshow(data[0].numpy().squeeze(), cmap='gray')
axes[count].set_xticks([])
axes[count].set_yticks([])
if pred.item() == 0:
axes[count].set_title('Predicted item: T-Shirt')
elif pred.item() == 1:
axes[count].set_title('Predicted item: Trouser')
count += 1
|
https://github.com/qiskit-community/prototype-quantum-kernel-training
|
qiskit-community
|
# pylint: disable=import-error, wrong-import-position, pointless-statement
import os
import sys
import numpy as np
from qiskit import QuantumCircuit
from qiskit.circuit import ParameterVector
from qiskit_machine_learning.kernels import TrainableFidelityQuantumKernel
NUM_QUBITS = 3
fm = QuantumCircuit(NUM_QUBITS)
input_params = ip = ParameterVector("x", NUM_QUBITS)
training_params = tp = ParameterVector("θ", NUM_QUBITS)
for i in range(NUM_QUBITS):
fm.h(i)
fm.ry(tp[i], i)
for i in range(NUM_QUBITS):
fm.crx(ip[i], (i) % NUM_QUBITS, (i + 1) % NUM_QUBITS)
# Define a Quantum Kernel using our trainable feature map
qk = TrainableFidelityQuantumKernel(
feature_map=fm, training_parameters=training_params[:NUM_QUBITS]
)
print("input_params:", input_params)
print("training_params:", training_params)
qk.feature_map.draw()
# Bind parameters to numeric values
param_binds = {tp[0]: np.pi / 2, tp[1]: np.pi / 3, tp[2]: np.pi / 4}
qk.assign_training_parameters(param_binds)
qk.parameter_values
# Create incomplete training param bindings
param_binds = {tp[0]: np.pi / 6, tp[1]: np.pi / 5}
qk.assign_training_parameters(param_binds)
qk.parameter_values
# Create incomplete user param bindings
param_binds = {tp[0]: tp[0], tp[1]: tp[0] + tp[2], tp[2]: tp[2]}
qk.assign_training_parameters(param_binds)
qk.parameter_values
qk = TrainableFidelityQuantumKernel(feature_map=fm, training_parameters=training_params)
qk.feature_map.draw()
param_values = [np.pi / 7, tp[1], np.pi / 9]
qk.assign_training_parameters(param_values)
qk.parameter_values
param_values = [np.pi / 7, np.pi / 6, np.pi / 9]
qk.assign_training_parameters(param_values)
qk.parameter_values
param_values = [tp[0], tp[1], tp[2]]
qk.assign_training_parameters(param_values)
qk.parameter_values
#import qiskit.tools.jupyter
#
#%qiskit_version_table
#%qiskit_copyright
|
https://github.com/qiskit-community/prototype-quantum-kernel-training
|
qiskit-community
|
# pylint: disable=import-error, wrong-import-position, too-few-public-methods, no-self-use, function-redefined
from functools import partial
from typing import Sequence
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
# Import Qiskit packages
from qiskit.circuit.library import ZZFeatureMap
from qiskit_machine_learning.kernels import TrainableFidelityQuantumKernel
from qiskit_machine_learning.utils.loss_functions import KernelLoss
feature_map = ZZFeatureMap(3)
user_params = [feature_map.parameters[1]]
qkernel = TrainableFidelityQuantumKernel(
feature_map=feature_map,
training_parameters=user_params,
)
X_train = [[0.6, 0.2], [0.5, 0.3], [0.3, 0.7], [0.1, 0.5]]
y_train = np.array([0, 0, 1, 1])
class CustomKernelLoss(KernelLoss):
"""Example Kernel Loss class"""
def __init__(self, **kwargs):
self.kwargs = kwargs
# Evaluate the Loss of a trainable quantum kernel
# at a particular setting of user_param_values on
# a particular dataset.
def evaluate(
self,
parameter_values: Sequence[float],
quantum_kernel: TrainableFidelityQuantumKernel,
data: np.ndarray,
labels: np.ndarray,
):
# Bind the user parameter values
quantum_kernel.assign_training_parameters(parameter_values)
kernel_matrix = quantum_kernel.evaluate(data)
return labels.T @ kernel_matrix @ labels
# Wrap our evaluate method so to produce a callable
# which maps user_param_values to loss scores.
def get_variational_callable(
self,
quantum_kernel: TrainableFidelityQuantumKernel,
data: np.ndarray,
labels: np.ndarray,
):
return partial(
self.evaluate, quantum_kernel=quantum_kernel, data=data, labels=labels
)
kernel_loss = CustomKernelLoss().get_variational_callable(qkernel, X_train, y_train)
print(kernel_loss([0.75]))
NUM_GRID_POINTS = 100
loss_values = [kernel_loss([val]) for val in np.linspace(0, 2 * np.pi, NUM_GRID_POINTS)]
plt.rcParams["font.size"] = 15
plt.figure(figsize=(8, 4))
plt.plot(np.linspace(0, 2 * np.pi, NUM_GRID_POINTS), loss_values)
plt.xlabel("θ")
plt.ylabel("Kernel Loss")
plt.show()
def kernel_loss_full(training_param_values, kernel, data, labels):
kernel.assign_training_parameters(training_param_values)
kernel_matrix = kernel.evaluate(data)
return labels.T @ kernel_matrix @ labels
kernel_loss = partial(kernel_loss_full, kernel=qkernel, data=X_train, labels=y_train)
print(kernel_loss([0.75]))
#import qiskit.tools.jupyter
#
#%qiskit_version_table
#%qiskit_copyright
|
https://github.com/qiskit-community/prototype-quantum-kernel-training
|
qiskit-community
|
from typing import List, Callable, Union
from qiskit import QuantumCircuit
from qiskit.circuit import ParameterVector
# To visualize circuit creation process
from qiskit.visualization import circuit_drawer
# For a dataset with 12 features; and 2 features per qubit
FEATURE_DIMENSION = 12
NUM_QUBITS = int(FEATURE_DIMENSION / 2)
# Qiskit feature maps should generally be QuantumCircuits or extensions of QuantumCircuit
feature_map = QuantumCircuit(NUM_QUBITS)
user_params = ParameterVector("θ", NUM_QUBITS)
# Create circuit layer with trainable parameters
for i in range(NUM_QUBITS):
feature_map.ry(user_params[i], feature_map.qubits[i])
print(circuit_drawer(feature_map))
# Linear entanglement
entanglement = [[i, i + 1] for i in range(NUM_QUBITS - 1)]
for source, target in entanglement:
feature_map.cz(feature_map.qubits[source], feature_map.qubits[target])
feature_map.barrier()
print(circuit_drawer(feature_map))
input_params = ParameterVector("x", FEATURE_DIMENSION)
for i in range(NUM_QUBITS):
feature_map.rz(input_params[2 * i + 1], feature_map.qubits[i])
feature_map.rx(input_params[2 * i], feature_map.qubits[i])
print(circuit_drawer(feature_map))
class ExampleFeatureMap(QuantumCircuit):
"""The Example Feature Map circuit"""
def __init__(
self,
feature_dimension: int,
entanglement: Union[str, List[List[int]], Callable[[int], List[int]]] = None,
name: str = "ExampleFeatureMap",
) -> None:
"""Create a new Example Feature Map circuit.
Args:
feature_dimension: The number of features
entanglement: Entanglement scheme to be used in second layer
name: Name of QuantumCircuit object
Raises:
ValueError: ExampleFeatureMap requires an even number of input features
"""
if (feature_dimension % 2) != 0:
raise ValueError(
"""
Example feature map requires an even number of input features.
"""
)
self.feature_dimension = feature_dimension
self.entanglement = entanglement
self.training_parameters = None
# Call the QuantumCircuit initialization
num_qubits = feature_dimension / 2
super().__init__(
num_qubits,
name=name,
)
# Build the feature map circuit
self._generate_feature_map()
def _generate_feature_map(self):
# If no entanglement scheme specified, use linear entanglement
if self.entanglement is None:
self.entanglement = [[i, i + 1] for i in range(self.num_qubits - 1)]
# Vector of data parameters
input_params = ParameterVector("x", self.feature_dimension)
training_params = ParameterVector("θ", self.num_qubits)
# Create an initial rotation layer of trainable parameters
for i in range(self.num_qubits):
self.ry(training_params[i], self.qubits[i])
self.training_parameters = training_params
# Create the entanglement layer
for source, target in self.entanglement:
self.cz(self.qubits[source], self.qubits[target])
self.barrier()
# Create a circuit representation of the data group
for i in range(self.num_qubits):
self.rz(input_params[2 * i + 1], self.qubits[i])
self.rx(input_params[2 * i], self.qubits[i])
feature_map = ExampleFeatureMap(feature_dimension=10)
circuit_drawer(feature_map)
#import qiskit.tools.jupyter
#
#%qiskit_version_table
#%qiskit_copyright
|
https://github.com/qiskit-community/prototype-quantum-kernel-training
|
qiskit-community
|
# pylint: disable=protected-access
from qiskit.circuit.library import ZZFeatureMap
from qiskit.circuit import ParameterVector
# Define a (non-parameterized) feature map from the Qiskit circuit library
fm = ZZFeatureMap(2)
input_params = fm.parameters
fm.draw()
# split params into two disjoint sets
input_params = fm.parameters[::2]
training_params = fm.parameters[1::2]
print("input_params:", input_params)
print("training_params:", training_params)
# define new parameter vectors for the input and user parameters
new_input_params = ParameterVector("x", len(input_params))
new_training_params = ParameterVector("θ", len(training_params))
# resassign the origin feature map parameters
param_reassignments = {}
for i, p in enumerate(input_params):
param_reassignments[p] = new_input_params[i]
for i, p in enumerate(training_params):
param_reassignments[p] = new_training_params[i]
fm.assign_parameters(param_reassignments, inplace=True)
input_params = new_input_params
training_params = new_training_params
print("input_params:", input_params)
print("training_params:", training_params)
fm.draw()
# Define two circuits
circ1 = ZZFeatureMap(2)
circ2 = ZZFeatureMap(2)
input_params = circ1.parameters
training_params = ParameterVector("θ", 2)
# Reassign new parameters to circ2 so there are no name collisions
circ2.assign_parameters(training_params, inplace=True)
# Compose to build a parameterized feature map
fm = circ2.compose(circ1)
print("input_params:", list(input_params))
print("training_params:", training_params)
fm.draw()
#import qiskit.tools.jupyter
#
#%qiskit_version_table
#%qiskit_copyright
|
https://github.com/qiskit-community/prototype-quantum-kernel-training
|
qiskit-community
|
# pylint: disable=import-error, wrong-import-position
# Python imports
import os
import sys
# External imports
from pylab import cm
from sklearn import metrics
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# Qiskit imports
from qiskit.visualization import circuit_drawer
from qiskit_algorithms.optimizers import SPSA
from qiskit_machine_learning.kernels import TrainableFidelityQuantumKernel
from qiskit_machine_learning.kernels.algorithms import QuantumKernelTrainer
from qiskit_machine_learning.algorithms import QSVC
# Put this repository on the Python path and import qkt pkgs
module_path = os.path.abspath(os.path.join("../.."))
sys.path.append(module_path)
from qkt.feature_maps import CovariantFeatureMap
from qkt.utils import train_test_split, QKTCallback
# Load the dataset and split into train and test sets
DATA_FILEPATH = "../../data/dataset_graph7.csv"
X_train, y_train, X_test, y_test = train_test_split(DATA_FILEPATH)
num_features = np.shape(X_train)[1]
entangler_map = [[0, 2], [3, 4], [2, 5], [1, 4], [2, 3], [4, 6]]
# Note that [[0,1],[2,3],[4,5],[6,7],[8,9],[1,2],[3,4],[5,6],[7,8]]
# is a suitable input for the 10-qubit dataset
fm = CovariantFeatureMap(
feature_dimension=num_features,
entanglement=entangler_map,
single_training_parameter=True,
)
circuit_drawer(fm)
print(fm.training_parameters)
# Instantiate quantum kernel
quant_kernel = TrainableFidelityQuantumKernel(
feature_map=fm, training_parameters=fm.training_parameters
)
# Set up the optimizer
cb_qkt = QKTCallback()
spsa_opt = SPSA(
maxiter=5, callback=cb_qkt.callback, learning_rate=0.1, perturbation=0.1
)
# Instantiate a quantum kernel trainer.
qkt = QuantumKernelTrainer(
quantum_kernel=quant_kernel,
loss="svc_loss",
optimizer=spsa_opt,
initial_point=[0.1] * len(fm.training_parameters),
)
# Train the kernel using QKT directly
qka_results = qkt.fit(X_train, y_train)
optimized_kernel = qka_results.quantum_kernel
# Use QSVC for classification
qsvc = QSVC(quantum_kernel=optimized_kernel)
# Fit the QSVC
qsvc.fit(X_train, y_train)
# Predict the labels
labels_test = qsvc.predict(X_test)
# Evaluate the test accuracy
accuracy_test = metrics.balanced_accuracy_score(y_true=y_test, y_pred=labels_test)
print(f"accuracy test: {accuracy_test}")
plot_data = cb_qkt.get_callback_data() # callback data
K = optimized_kernel.evaluate(
X_train
) # kernel matrix evaluated on the training samples
plt.rcParams["font.size"] = 20
fig, ax = plt.subplots(1, 2, figsize=(14, 5))
ax[0].plot(
[i + 1 for i in range(len(plot_data[0]))], np.array(plot_data[2]), c="k", marker="o"
)
ax[0].set_xlabel("Iterations")
ax[0].set_ylabel("Loss")
ax[1].imshow(K, cmap=cm.get_cmap("bwr", 20))
ax[1].axis("off")
fig.tight_layout()
plt.show()
#import qiskit.tools.jupyter
#
#%qiskit_version_table
#%qiskit_copyright
|
https://github.com/qiskit-community/prototype-quantum-kernel-training
|
qiskit-community
|
# This code is part of Qiskit.
#
# (C) Copyright IBM 2021.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""Create a new Covariant Feature Map circuit."""
import copy
from typing import Callable, Optional, Union, List, Dict, Any
import numpy as np
from qiskit import QuantumCircuit
from qiskit.circuit import ParameterVector
class CovariantFeatureMap(QuantumCircuit):
"""The Covariant Feature Map circuit.
On 3 qubits and a linear entanglement, the circuit is represented by:
.. parsed-literal::
┌──────────────┐ ░ ┌─────────────────┐┌─────────────────┐
q_0: ┤ Ry(θ_par[0]) ├─■─────░─┤ Rz(-2*x_par[1]) ├┤ Rx(-2*x_par[0]) ├
├──────────────┤ │ ░ ├─────────────────┤├─────────────────┤
q_1: ┤ Ry(θ_par[1]) ├─■──■──░─┤ Rz(-2*x_par[3]) ├┤ Rx(-2*x_par[2]) ├
├──────────────┤ │ ░ ├─────────────────┤├─────────────────┤
q_2: ┤ Ry(θ_par[2]) ├────■──░─┤ Rz(-2*x_par[5]) ├┤ Rx(-2*x_par[4]) ├
└──────────────┘ ░ └─────────────────┘└─────────────────┘
where θ_par is a vector of trainable feature map parameters and x_par is a
vector of data-bound feature map parameters.
"""
def __init__(
self,
feature_dimension: int,
entanglement: Union[str, List[List[int]], Callable[[int], List[int]]] = None,
single_training_parameter: bool = False,
name: str = "CovariantFeatureMap",
) -> None:
"""Create a new Covariant Feature Map circuit.
Args:
feature_dimension: The number of features
insert_barriers: If True, barriers are inserted around the entanglement layer
"""
if (feature_dimension % 2) != 0:
raise ValueError(
"""
Covariant feature map requires an even number of input features.
"""
)
self.feature_dimension = feature_dimension
self.entanglement = entanglement
self.single_training_parameter = single_training_parameter
self.training_parameters = None
self.input_parameters = None
num_qubits = feature_dimension / 2
super().__init__(
num_qubits,
name=name,
)
self._generate_feature_map()
@property
def settings(self) -> Dict[str, Any]:
training_parameters_list = [param for param in self.training_parameters]
input_parameters_list = [param for param in self.input_parameters]
return {
"feature_dimension": self.feature_dimension,
"entanglement": self.entanglement,
"single_training_parameter": self.single_training_parameter,
"training_parameters": training_parameters_list,
"input_parameters": input_parameters_list,
}
def _generate_feature_map(self):
# If no entanglement scheme specified, use linear entanglement
if self.entanglement is None:
self.entanglement = [[i, i + 1] for i in range(self.num_qubits - 1)]
# Vector of data parameters
input_params = ParameterVector("x_par", self.feature_dimension)
# Use a single parameter to rotate each qubit if sharing is desired
if self.single_training_parameter:
training_params = ParameterVector("\u03B8_par", 1)
# Create an initial rotation layer using a single Parameter
for i in range(self.num_qubits):
self.ry(training_params[0], self.qubits[i])
# Train each qubit's initial rotation individually
else:
training_params = ParameterVector("\u03B8_par", self.num_qubits)
# Create an initial rotation layer of trainable parameters
for i in range(self.num_qubits):
self.ry(training_params[i], self.qubits[i])
self.training_parameters = training_params
self.input_parameters = input_params
# Create the entanglement layer
for source, target in self.entanglement:
self.cz(self.qubits[source], self.qubits[target])
self.barrier()
# Create a circuit representation of the data group
for i in range(self.num_qubits):
self.rz(-2 * input_params[2 * i + 1], self.qubits[i])
self.rx(-2 * input_params[2 * i], self.qubits[i])
|
https://github.com/Qottmann/Quantum-anomaly-detection
|
Qottmann
|
import time
import itertools
import numpy as np
import qiskit
from qiskit import *
from qiskit.quantum_info import Statevector
from qiskit.algorithms.optimizers import COBYLA, L_BFGS_B, SPSA
from qiskit.opflow.state_fns import StateFn, CircuitStateFn
from qiskit.providers.aer import StatevectorSimulator, AerSimulator
from qiskit.opflow import CircuitSampler
from qiskit.ignis.mitigation.measurement import CompleteMeasFitter # you will need to pip install qiskit-ignis
from qiskit.ignis.mitigation.measurement import complete_meas_cal
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
from matplotlib.colors import BoundaryNorm
cmap = plt.get_cmap("plasma") #'viridis'
from modules.utils import *
from qae import *
import datetime
print(qiskit.__version__, np.__version__)
IBMQ.load_account() # this then automatically loads your saved account
provider = IBMQ.get_provider(hub='ibm-q-research')
device = provider.backend.ibmq_jakarta # 6 bogota ; 4 rome
### Real device execution:
backend_device = device
### Simulation with noise profile from real device
backend_sim = qiskit.providers.aer.AerSimulator.from_backend(device)
### Simulation without noise
#backend = qiskit.providers.aer.AerSimulator()
### Preliminaries
L = 5
num_trash = 2
anti = -1 # 1 for ferromagnetic Ising model, -1 for antiferromagnet
run_VQE = True # whether or not you want to compute the VQE values; or load them
execute = True
real_device = True # want to execute on real device
# dataset to be loaded (optimal VQE parameters)
# if execute_VQE is True this will overwrite the file
filename = f'data/jakarta_execute'
# Give a _unique_ name to this notebook execution, the results will be stored in the appropriate files with that name
name = "jakarta_execute"
# uncomment in case you want to toy around with the ansatz circuit
rotation_blocks = "ry"
entanglement_blocks = "cz" # cx
entanglement = "sca" #"sca"
reps = 1
ansatz_config = dict(rotation_blocks = rotation_blocks, entanglement_blocks = entanglement_blocks, reps = reps, entanglement = entanglement)
ansatz = qiskit.circuit.library.TwoLocal(L, **ansatz_config)
ansatz.draw("mpl")
tansatz = qiskit.transpile(ansatz, backend_sim)
tansatz.draw("mpl")
coupling_map = device.configuration().coupling_map
noise_model = qiskit.providers.aer.noise.NoiseModel.from_backend(device)
basis_gates = noise_model.basis_gates
qi_device = qiskit.utils.QuantumInstance(backend=backend_device, # , seed_simulator=seed, seed_transpiler=seed
shots = 1000,
coupling_map=coupling_map,
#noise_model=noise_model, # comment out on real device execution
measurement_error_mitigation_cls= CompleteMeasFitter,
cals_matrix_refresh_period=30 #How often to refresh the calibration matrix in measurement mitigation. in minutes
)
qi_sim = qiskit.utils.QuantumInstance(backend=backend_sim, # , seed_simulator=seed, seed_transpiler=seed
shots = 1000,
coupling_map=coupling_map,
noise_model=noise_model, # comment out on real device execution
measurement_error_mitigation_cls= CompleteMeasFitter,
cals_matrix_refresh_period=30 #How often to refresh the calibration matrix in measurement mitigation. in minutes
)
if not real_device:
backend_device = backend_sim
qi_device = qi_sim
if run_VQE:
maxiter = [200,200,200,200,200,
200,200,200,150,150,
150,150,100,100,100,
60,30,30,30,30
]
maxiter = maxiter[::2]
maxiter = [150] + [50] * 9
logspace_size = len(maxiter)
gx_vals = np.logspace(-2,2,logspace_size)
#gz_vals = [0.] # np.logspace(-2,2,logspace_size)
counts = []
values = []
params = []
def store_intermediate_result(eval_count, parameters, mean, std):
counts.append(eval_count)
values.append(mean)
params.append(parameters)
opt_params = []
gx_list = []
gz_list = []
countss, valuess, paramss = [], [], []
Qmag_sim, QZ_sim, Qen_sim= [],[], []
Smag, Sen = [], []
#maxiter = [200,200,200,200, 150, 60, 60, 60, 60, 60]
for j,gx in enumerate(gx_vals):
gz = 0
t0 = datetime.datetime.now()
counts = []
values = []
params = []
optimizer = SPSA(maxiter=maxiter[j], blocking=True) # ,learning_rate=0.05, perturbation=0.05,; setting that speeds things up sometimes
vqe = qiskit.algorithms.VQE(ansatz=ansatz,
initial_point = opt_params[j-1] if j != 0 else None,
optimizer=optimizer,
callback=store_intermediate_result, # comment out on real device
quantum_instance=qi_sim
)
H = QHIsing(L,anti,np.float32(gx),np.float32(gz))
result = vqe.compute_minimum_eigenvalue(H, aux_operators = [QNKron(L,Z,I,i) for i in range(L)]) #ED with Qiskit VQE
Qen_sim.append(result.eigenvalue)
QZ_sim.append(result.aux_operator_eigenvalues[:,0]) # real-space resolved Z expectation value
Qmag_sim.append(np.mean(anti**np.arange(L) * result.aux_operator_eigenvalues[:,0]))
countss.append(counts)
valuess.append(values)
paramss.append(params)
# uncomment if you want to recompute ED results (typically provided with guess parameters)
ED_state, ED_E, ham = ising_groundstate(L, anti, np.float32(gx), np.float32(gz))
Sen.append(ED_E)
Smag.append(ED_state.T.conj()@Mag(L,anti)@ED_state)
print(f"ED energy: {Sen[j]} ;; VQE energy: {Qen_sim[j]} ;; diff {Qen_sim[j] - Sen[j]}")
print(f"ED mag: {Smag[j]} ;; VQE mag: {Qmag_sim[j]} ;; diff {Qmag_sim[j] - Smag[j]}")
gx_list.append(gx)
gz_list.append(gz)
opt_params.append(sort_params(result.optimal_parameters))
print(f"{j+1} / {len(gx_vals)}, gx = {gx:.2f}, gz = {gz:.2f}, time : {(datetime.datetime.now() - t0)}")
fig, axs = plt.subplots(ncols=2,figsize=(6,3))
axs[0].plot(countss[j],valuess[j])
axs[1].plot(result.aux_operator_eigenvalues[:,0])
plt.show()
np.savez(filename, gx_list=gx_list, gz_list=gz_list, opt_params=opt_params,
Qmag_sim=Qmag_sim, Qen_sim=Qen_sim, QZ_sim = QZ_sim, Sen=Sen, Smag=Smag,
ansatz_config=ansatz_config,
paramss = paramss, valuess = valuess, countss = countss
)
temp = np.load(filename + ".npz",allow_pickle=True)
gx_list = temp["gx_list"]
gz = 0
gz_list = [0]
Qmag_sim = temp["Qmag_sim"]
QZ_sim = temp["QZ_sim"]
Qen_sim = temp["Qen_sim"]
Sen = temp["Sen"]
Smag = temp["Smag"]
opt_params = temp["opt_params"]
paramss = temp["paramss"]; valuess = temp["valuess"]; countss = temp["countss"]
# This just recomputes the magnetization with the parameters provided by guess_params
if execute:
QZ_executed=np.zeros((len(opt_params),L), dtype="complex")
Qmag_executed=np.zeros((len(opt_params),), dtype="complex")
for j in range(len(opt_params)):
t0 = datetime.datetime.now()
gx = gx_list[j]
#H = QHIsing(L, anti, np.float32(gx), np.float32(gz)) # build Hamiltonian Op
state = ansatz.assign_parameters(opt_params[j])
for i in range(L):
meas_outcome = ~StateFn(QNKron(L,Z,I,i)) @ StateFn(state)
QZ_executed[j,i] = CircuitSampler(qi_device).convert(meas_outcome).eval() #https://quantumcomputing.stackexchange.com/questions/12080/evaluating-expectation-values-of-operators-in-qiskit
Qmag_executed[j] = np.mean(anti**np.arange(L) * QZ_executed[j])
#e_outcome = ~StateFn(H) @ StateFn(state)
#Qen_executed[j] = CircuitSampler(qi).convert(e_outcome).eval()
print(f"{j+1} / {len(opt_params)}, QZ: {Qmag_executed[j]}, gx = {gx:.2f}, gz = {gz:.2f}, time : {(datetime.datetime.now() - t0)}")
fig, ax = plt.subplots(ncols=1,figsize=(4,3))
ax.plot(QZ_sim[j].real)
ax.plot(QZ_executed[j].real,"x--")
ax.set_title("S exec = {:.3f}, S sim = {:.3f}".format(Qmag_executed[j].real, Qmag_sim[j].real))
plt.show()
np.savez(filename + f"_executed_-real-device-{real_device}", gx_list=gx_list, gz_list=gz_list, opt_params=opt_params,
Qmag_sim=Qmag_sim, Qen_sim=Qen_sim, QZ_sim = QZ_sim, Sen=Sen, Smag=Smag,
Qmag_device= Qmag_executed, QZ_device = QZ_executed,
ansatz_config=ansatz_config,
paramss = paramss, valuess = valuess, countss = countss
)
fig, axs = plt.subplots(ncols=2, figsize=(10,5))
ax = axs[0]
ax.plot(gx_list, np.abs(Smag),"x--", label="ED")
ax.plot(gx_list, np.abs(Qmag_sim),"x--", label="VQE sim")
ax.plot(gx_list, np.abs(Qmag_executed),"x--", label="VQE ibmq_rome")
ax.set_xscale("log")
ax.legend()
ax = axs[1]
ax.plot(gx_list, Smag,"x--", label="ED")
ax.plot(gx_list, Qmag_sim,"x--", label="VQE sim")
ax.plot(gx_list, Qmag_executed,"x--", label="VQE ibmq_rome")
ax.set_xscale("log")
ax.legend()
##############################################################################
### II - Anomaly Detection ##################################################
##############################################################################
trash_qubits_idxs = [1,3]
# Using our QAEAnsatz class (described in qae.py)
circ = QAEAnsatz(num_qubits = L, num_trash_qubits= num_trash, trash_qubits_idxs = trash_qubits_idxs, measure_trash=True)
circ.draw("mpl")
# an example with assigned parameters
circ = circ.assign_parameters(np.arange(L*2+2))
circ.draw("mpl")
def prepare_circuit(thetas, L=6, num_trash=2, init_state=None, measurement=True, vqe=True):
QAE_circ = QAEAnsatz(num_qubits = L, num_trash_qubits= num_trash, trash_qubits_idxs = trash_qubits_idxs, measure_trash=measurement).assign_parameters(thetas)
if vqe:
VQE_circ = ansatz.assign_parameters(init_state)
fullcirc = VQE_circ + QAE_circ
else:
fullcirc = QAE_circ.initialize(init_state, qreg)
return fullcirc
# example of the full circuit with random parameters assigned
fullcirc = prepare_circuit(np.random.rand(L*2+2),L,num_trash, vqe=True, init_state=opt_params[0])
fullcirc.draw("mpl")
backend = backend_device
def calibrate_circuit(L, nums_trash=trash_qubits_idxs,shots=1000):
# outputs a CompleteMeasFitter object for alter use
num_trash = len(nums_trash)
qreg = QuantumRegister(L, 'q')
# obtain calibration matrix
qubit_list = [i for i in nums_trash] # only need to calibrate the trash qubits
circlabel = f'mcal_{datetime.datetime.now()}'
meas_calibs, state_labels = complete_meas_cal(qubit_list=qubit_list, qr=qreg, circlabel=circlabel)
cal_job = backend.run(meas_calibs, shots=shots) #, noise_model=noise_model)
cal_results = cal_job.result()
meas_fitter = CompleteMeasFitter(cal_results, state_labels, circlabel=circlabel)
print(circlabel, meas_fitter.cal_matrix)
return meas_fitter
def run_circuit(thetas, L, num_trash, init_state, vqe=True, shots=100, meas_fitter = None):
circ = prepare_circuit(thetas, L, num_trash, init_state, vqe=vqe)
tcirc = qiskit.transpile(circ, backend)
# Execute the circuit
job_sim = backend.run(tcirc, shots=shots) # , seed_simulator=123, seed_transpiler=234 fix seed to make it reproducible
result = job_sim.result()
# Results without mitigation
counts = result.get_counts()
if meas_fitter != None:
# Get the filter object
meas_filter = meas_fitter.filter
# Results with mitigation
mitigated_results = meas_filter.apply(result)
counts = mitigated_results.get_counts(0)
return counts
phis = opt_params # the parameters for the VQE initialization
def cost_function_single(thetas, L, num_trash, p, shots=1000, vqe=True, param_encoding=False, x=0, meas_fitter=None):
""" Optimizes circuit """
if vqe:
init_state = phis[p]
else:
J, gx, gz = p
init_state, _ = ising_groundstate(L, J, gx, gz)
if param_encoding: thetas = feature_encoding(thetas, x)
out = run_circuit(thetas, L, num_trash, init_state, vqe=vqe, shots=shots, meas_fitter=meas_fitter)
cost = out.get('11', 0)*2 + out.get('01', 0) + out.get('10', 0)
return cost/shots
def cost_function(thetas, L, num_trash, ising_params, shots=1000, vqe=True, param_encoding=False, x=0, meas_fitter=None):
""" Optimizes circuit """
cost = 0.
n_samples = len(ising_params)
for i, p in enumerate(ising_params):
if param_encoding:
cost += cost_function_single(thetas, L, num_trash, p, shots, vqe, param_encoding, x[i], meas_fitter=meas_fitter)
else:
cost += cost_function_single(thetas, L, num_trash, p, shots, vqe, param_encoding, meas_fitter=meas_fitter)
return cost/n_samples
def optimize(ising_params, L=6, num_trash=2, thetas=None, shots=1000, max_iter=400, vqe=True, param_encoding=False, x=0, pick_optimizer = None,
meas_fitter=None, callback=True):
if thetas is None:
n_params = (2*L+2)*2 if param_encoding else (2*L+2)
thetas = np.random.uniform(0, 2*np.pi, n_params) # initial parameters without feature encoding
print("Initial cost: {:.3f}".format(cost_function(thetas, L, num_trash, ising_params, shots, vqe, param_encoding, x)))
counts, values, accepted = [], [], []
def store_intermediate_result(eval_count, parameters, mean, std, ac):
# counts.append(eval_count)
values.append(mean)
accepted.append(ac)
# Initialize optimizer
if pick_optimizer == "cobyla":
optimizer = COBYLA(maxiter=max_iter, tol=0.0001)
if pick_optimizer == "adam" or pick_optimizer == "ADAM":
optimizer = qiskit.algorithms.optimizers.ADAM(maxiter=max_iter)
# optimizer = L_BFGS_B(maxfun=300, maxiter=max_iter)#, factr=10, iprint=- 1, epsilon=1e-08)
if pick_optimizer == "spsa" or pick_optimizer == None:
optimizer = SPSA(maxiter=max_iter,
blocking=True,
callback=store_intermediate_result if callback else None,
learning_rate=0.1,
perturbation=0.1
) # recommended from qiskit (first iteraction takes quite long)
# to reduce time figure out optimal learning rate and perturbation in advance
start_time = time.time()
ret = optimizer.optimize(
num_vars=len(thetas),
objective_function=(lambda thetas: cost_function(thetas, L, num_trash, ising_params, shots, vqe, param_encoding, x, meas_fitter=meas_fitter)),
initial_point=thetas
)
print("Time: {:.5f} sec".format(time.time()-start_time))
print(ret)
return ret[0], values, accepted
backend = backend_sim
#calibration
meas_fitter = calibrate_circuit(L, nums_trash=trash_qubits_idxs)
# Training
phys_params = [0]
thetas_opt, loss, accepted = optimize(phys_params, max_iter=90, L=5, meas_fitter=meas_fitter) #, pick_optimizer="adam")
plt.plot(loss)
np.savez(filename + "run1_thetas_opt_sim", thetas_opt=thetas_opt, loss=loss, accepted=accepted)
# Inference; note that on the real device each inference points takes about ~30 seconds
cost = np.zeros((len(gx_vals)))
thetas_opt = np.load(filename + "run1_thetas_opt_sim.npz",allow_pickle=True)["thetas_opt"]
shots = 1000
for i,p in enumerate(list(gx_list)):
t0 = datetime.datetime.now()
cost[i] = cost_function_single(thetas_opt, L, num_trash, i, shots=shots, meas_fitter=meas_fitter) # np.random.uniform(0, 2*np.pi, 2*L+2) random parameters to check if training "does" something - result was: with random paremters just get noise, so yes, it "does" something
print(f"{i} / {len(gx_list)} - execution time {datetime.datetime.now()-t0}")
np.savez(filename + "run1_sim",cost=cost, thetas_opt=thetas_opt, loss=loss, accepted = accepted)
# Training
phys_params = [0]
thetas_opt, loss, accepted = optimize(phys_params, max_iter=90, L=5, meas_fitter=meas_fitter) #, pick_optimizer="adam")
plt.plot(loss)
np.savez(filename + "run1_thetas_opt_sim2", thetas_opt=thetas_opt, loss=loss, accepted=accepted)
# Inference; note that on the real device each inference points takes about ~30 seconds
cost = np.zeros((len(gx_vals)))
thetas_opt = np.load(filename + "run1_thetas_opt_sim2.npz",allow_pickle=True)["thetas_opt"]
shots = 1000
for i,p in enumerate(list(gx_list)):
t0 = datetime.datetime.now()
cost[i] = cost_function_single(thetas_opt, L, num_trash, i, shots=shots, meas_fitter=meas_fitter) # np.random.uniform(0, 2*np.pi, 2*L+2) random parameters to check if training "does" something - result was: with random paremters just get noise, so yes, it "does" something
print(f"{i} / {len(gx_list)} - execution time {datetime.datetime.now()-t0}")
np.savez(filename + "run1_sim2",cost=cost, thetas_opt=thetas_opt, loss=loss, accepted = accepted)
fig, axs = plt.subplots(ncols=2, figsize=(10,5))
temp0 = np.load(filename + "run1_sim2.npz",allow_pickle=True)
cost = temp0["cost"] ;
ax = axs[0]
ax.plot(gx_list, cost,"X--", label="cost")
ax.plot(gx_list, np.abs(Qmag_sim),"x--", label="VQE res")
ax.plot(gx_list, np.abs(Qmag_executed),"x--", label="VQE resample")
ax.set_xscale("log")
ax.legend()
ax = axs[1]
ax.plot(gx_list, cost,"X--", label="cost")
ax.plot(gx_list, Qmag_sim,"x--", label="VQE res")
ax.plot(gx_list, Qmag_executed,"x--", label="VQE resample")
ax.set_xscale("log")
ax.legend()
# Training
phys_params = [-1]
thetas_opt, loss, accepted = optimize(phys_params, max_iter=40, L=5, meas_fitter=meas_fitter) #, pick_optimizer="adam")
plt.plot(loss)
np.savez(filename + "run2_thetas_opt_sim2", thetas_opt=thetas_opt, loss=loss, accepted=accepted)
# Inference; note that on the real device each inference points takes about ~30 seconds
cost = np.zeros((len(gx_vals)))
thetas_opt = np.load(filename + "run2_thetas_opt_sim2.npz",allow_pickle=True)["thetas_opt"]
shots = 1000
for i,p in enumerate(list(gx_list)):
t0 = datetime.datetime.now()
cost[i] = cost_function_single(thetas_opt, L, num_trash, i, shots=shots, meas_fitter=meas_fitter) # np.random.uniform(0, 2*np.pi, 2*L+2) random parameters to check if training "does" something - result was: with random paremters just get noise, so yes, it "does" something
print(f"{i} / {len(gx_list)} - execution time {datetime.datetime.now()-t0}")
np.savez(filename + "run2_sim2",cost=cost, thetas_opt=thetas_opt, loss=loss, accepted = accepted)
fig, axs = plt.subplots(ncols=2, figsize=(10,5))
temp0 = np.load(filename + "run2_sim2.npz",allow_pickle=True)
cost = temp0["cost"] ;
ax = axs[0]
ax.plot(gx_list, cost,"X--", label="cost")
ax.plot(gx_list, np.abs(Qmag_sim),"x--", label="VQE res")
ax.plot(gx_list, np.abs(Qmag_executed),"x--", label="VQE resample")
ax.set_xscale("log")
ax.legend()
ax = axs[1]
ax.plot(gx_list, cost,"X--", label="cost")
ax.plot(gx_list, Qmag_sim,"x--", label="VQE res")
ax.plot(gx_list, Qmag_executed,"x--", label="VQE resample")
ax.set_xscale("log")
ax.legend()
backend = backend_device
meas_fitter = calibrate_circuit(L, nums_trash=trash_qubits_idxs)
# Training
phys_params = [0]
thetas_guess = np.load(filename + "run1_thetas_opt_sim.npz",allow_pickle=True)["thetas_opt"]
thetas_opt, loss, accepted = optimize(phys_params, thetas= thetas_guess, callback=False, max_iter=15, L=5, meas_fitter=meas_fitter) #, pick_optimizer="adam")
plt.plot(loss)
np.savez(filename + "run1_thetas_opt_device", thetas_opt=thetas_opt, loss=loss, accepted=accepted)
# Inference; note that on the real device each inference points takes about ~30 seconds
cost = np.zeros((len(gx_vals)))
thetas_opt = np.load(filename + "run1_thetas_opt_device.npz",allow_pickle=True)["thetas_opt"]
shots = 1000
for i,p in enumerate(list(gx_list)):
t0 = datetime.datetime.now()
cost[i] = cost_function_single(thetas_opt, L, num_trash, i, shots=shots, meas_fitter=meas_fitter) # np.random.uniform(0, 2*np.pi, 2*L+2) random parameters to check if training "does" something - result was: with random paremters just get noise, so yes, it "does" something
print(f"{i} / {len(gx_list)} - execution time {datetime.datetime.now()-t0}")
np.savez(filename + "run1_device",cost=cost, thetas_opt=thetas_opt, loss=loss, accepted = accepted)
fig, axs = plt.subplots(ncols=2, figsize=(10,5))
cost_device = np.load(filename + "run1_device.npz",allow_pickle=True)["cost"]
cost_sim = np.load(filename + "run1_sim.npz",allow_pickle=True)["cost"]
ax = axs[0]
ax.plot(gx_list, cost_sim,"X--", label="cost sim.")
ax.plot(gx_list, cost_device,"X--", label="cost ibmq_jakarta")
ax.plot(gx_list, np.abs(Qmag_sim),"x--", label="$\hat{S}$ sim")
ax.plot(gx_list, np.abs(Qmag_executed),"x--", label="$\hat{S}$ ibmq_jakarta")
ax.set_xscale("log")
ax.legend()
ax = axs[1]
ax.plot(gx_list, cost_sim,"X--", label="cost sim.")
ax.plot(gx_list, cost_device,"X--", label="cost ibmq_jakarta")
ax.plot(gx_list, Qmag_sim,"x--", label="$\hat{S}$ sim")
ax.plot(gx_list, Qmag_executed,"x--", label="$\hat{S}$ ibmq_jakarta")
ax.set_xscale("log")
ax.legend()
# Training
phys_params = [0]
thetas_guess = np.load(filename + "run1_thetas_opt_sim2.npz",allow_pickle=True)["thetas_opt"]
thetas_opt, loss, accepted = optimize(phys_params, thetas= thetas_guess, callback=False, max_iter=25, L=5, meas_fitter=meas_fitter) #, pick_optimizer="adam")
plt.plot(loss)
np.savez(filename + "run1_thetas_opt_device2", thetas_opt=thetas_opt, loss=loss, accepted=accepted)
# Inference; note that on the real device each inference points takes about ~30 seconds
cost = np.zeros((len(gx_vals)))
thetas_opt = np.load(filename + "run1_thetas_opt_device2.npz",allow_pickle=True)["thetas_opt"]
shots = 1000
for i,p in enumerate(list(gx_list)):
t0 = datetime.datetime.now()
cost[i] = cost_function_single(thetas_opt, L, num_trash, i, shots=shots, meas_fitter=meas_fitter) # np.random.uniform(0, 2*np.pi, 2*L+2) random parameters to check if training "does" something - result was: with random paremters just get noise, so yes, it "does" something
print(f"{i} / {len(gx_list)} - execution time {datetime.datetime.now()-t0}")
np.savez(filename + "run1_device2",cost=cost, thetas_opt=thetas_opt, loss=loss, accepted = accepted)
filename
fig, axs = plt.subplots(ncols=2, figsize=(10,5))
cost_device = np.load(filename + "run1_device2.npz",allow_pickle=True)["cost"]
cost_sim = np.load(filename + "run1_sim2.npz",allow_pickle=True)["cost"]
ax = axs[0]
ax.plot(gx_list, cost_sim,"X--", label="cost sim.")
ax.plot(gx_list, cost_device,"X--", label="cost ibmq_jakarta")
ax.plot(gx_list, np.abs(Qmag_sim),"x--", label="$\hat{S}$ sim")
ax.plot(gx_list, np.abs(Qmag_executed),"x--", label="$\hat{S}$ ibmq_jakarta")
ax.set_xscale("log")
ax.legend()
ax = axs[1]
ax.plot(gx_list, cost_sim,"X--", label="cost sim.")
ax.plot(gx_list, cost_device,"X--", label="cost ibmq_jakarta")
ax.plot(gx_list, Qmag_sim,"x--", label="$\hat{S}$ sim")
ax.plot(gx_list, Qmag_executed,"x--", label="$\hat{S}$ ibmq_jakarta")
ax.set_xscale("log")
ax.legend()
meas_fitter = calibrate_circuit(L, nums_trash=trash_qubits_idxs)
# Inference; note that on the real device each inference points takes about ~30 seconds
cost = np.zeros((len(gx_vals)))
thetas_opt = np.load(filename + "run2_thetas_opt_sim.npz",allow_pickle=True)["thetas_opt"]
shots = 1000
for i,p in enumerate(list(gx_list)):
t0 = datetime.datetime.now()
cost[i] = cost_function_single(thetas_opt, L, num_trash, i, shots=shots, meas_fitter=meas_fitter) # np.random.uniform(0, 2*np.pi, 2*L+2) random parameters to check if training "does" something - result was: with random paremters just get noise, so yes, it "does" something
print(f"{i} / {len(gx_list)} - execution time {datetime.datetime.now()-t0}")
filename
np.savez(filename + "run2_device",cost=cost, thetas_opt=thetas_opt, loss=loss, accepted = accepted)
fig, axs = plt.subplots(ncols=2, figsize=(10,5))
cost_device = np.load(filename + "run2_device.npz",allow_pickle=True)["cost"]
cost_sim = np.load(filename + "run2_sim.npz",allow_pickle=True)["cost"]
ax = axs[0]
ax.plot(gx_list, cost_sim,"X--", label="cost sim.")
ax.plot(gx_list, cost_device,"X--", label="cost ibmq_jakarta")
ax.plot(gx_list, np.abs(Qmag_sim),"x--", label="$\hat{S}$ sim")
ax.plot(gx_list, np.abs(Qmag_executed),"x--", label="$\hat{S}$ ibmq_jakarta")
ax.set_xscale("log")
ax.legend()
ax = axs[1]
ax.plot(gx_list, cost_sim,"X--", label="cost sim.")
ax.plot(gx_list, cost_device,"X--", label="cost ibmq_jakarta")
ax.plot(gx_list, Qmag_sim,"x--", label="$\hat{S}$ sim")
ax.plot(gx_list, Qmag_executed,"x--", label="$\hat{S}$ ibmq_jakarta")
ax.set_xscale("log")
ax.legend()
meas_fitter = calibrate_circuit(L, nums_trash=trash_qubits_idxs)
# Training
phys_params = [-1]
thetas_guess = np.load(filename + "run2_thetas_opt_sim.npz",allow_pickle=True)["thetas_opt"]
thetas_opt, loss, accepted = optimize(phys_params, thetas= thetas_guess, callback=False, max_iter=25, L=5, meas_fitter=meas_fitter) #, pick_optimizer="adam")
plt.plot(loss)
np.savez(filename + "run2_thetas_opt_device_overnight", thetas_opt=thetas_opt, loss=loss, accepted=accepted)
# Inference; note that on the real device each inference points takes about ~30 seconds
cost = np.zeros((len(gx_vals)))
thetas_opt = np.load(filename + "run2_thetas_opt_device_overnight.npz",allow_pickle=True)["thetas_opt"]
shots = 1000
for i,p in enumerate(list(gx_list)):
t0 = datetime.datetime.now()
cost[i] = cost_function_single(thetas_opt, L, num_trash, i, shots=shots, meas_fitter=meas_fitter) # np.random.uniform(0, 2*np.pi, 2*L+2) random parameters to check if training "does" something - result was: with random paremters just get noise, so yes, it "does" something
print(f"{i} / {len(gx_list)} - execution time {datetime.datetime.now()-t0}")
np.savez(filename + "run2_device_overnight",cost=cost, thetas_opt=thetas_opt, loss=loss, accepted = accepted)
fig, axs = plt.subplots(ncols=2, figsize=(10,5))
cost_device = np.load(filename + "run2_device_overnight.npz",allow_pickle=True)["cost"]
cost_sim = np.load(filename + "run2_sim2.npz",allow_pickle=True)["cost"]
ax = axs[0]
ax.plot(gx_list, cost_sim,"X--", label="cost sim.")
ax.plot(gx_list, cost_device,"X--", label="cost ibmq_jakarta")
ax.plot(gx_list, np.abs(Qmag_sim),"x--", label="$\hat{S}$ sim")
ax.plot(gx_list, np.abs(Qmag_executed),"x--", label="$\hat{S}$ ibmq_jakarta")
ax.set_xscale("log")
ax.legend()
ax = axs[1]
ax.plot(gx_list, cost_sim,"X--", label="cost sim.")
ax.plot(gx_list, cost_device,"X--", label="cost ibmq_jakarta")
ax.plot(gx_list, Qmag_sim,"x--", label="$\hat{S}$ sim")
ax.plot(gx_list, Qmag_executed,"x--", label="$\hat{S}$ ibmq_jakarta")
ax.set_xscale("log")
ax.legend()
|
https://github.com/Qottmann/Quantum-anomaly-detection
|
Qottmann
|
%%capture
%pip install qiskit
%pip install qiskit_ibm_provider
%pip install qiskit-aer
# Importing standard Qiskit libraries
from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit, QuantumCircuit, transpile, Aer
from qiskit_ibm_provider import IBMProvider
from qiskit.tools.jupyter import *
from qiskit.visualization import *
from qiskit.circuit.library import C3XGate
# Importing matplotlib
import matplotlib.pyplot as plt
# Importing Numpy, Cmath and math
import numpy as np
import os, math, cmath
from numpy import pi
# Other imports
from IPython.display import display, Math, Latex
# Specify the path to your env file
env_file_path = 'config.env'
# Load environment variables from the file
os.environ.update(line.strip().split('=', 1) for line in open(env_file_path) if '=' in line and not line.startswith('#'))
# Load IBM Provider API KEY
IBMP_API_KEY = os.environ.get('IBMP_API_KEY')
# Loading your IBM Quantum account(s)
IBMProvider.save_account(IBMP_API_KEY, overwrite=True)
# Run the quantum circuit on a statevector simulator backend
backend = Aer.get_backend('statevector_simulator')
qc_b1 = QuantumCircuit(2, 2)
qc_b1.h(0)
qc_b1.cx(0, 1)
qc_b1.draw(output='mpl', style="iqp")
sv = backend.run(qc_b1).result().get_statevector()
sv.draw(output='latex', prefix = "|\Phi^+\\rangle = ")
qc_b2 = QuantumCircuit(2, 2)
qc_b2.x(0)
qc_b2.h(0)
qc_b2.cx(0, 1)
qc_b2.draw(output='mpl', style="iqp")
sv = backend.run(qc_b2).result().get_statevector()
sv.draw(output='latex', prefix = "|\Phi^-\\rangle = ")
qc_b2 = QuantumCircuit(2, 2)
qc_b2.h(0)
qc_b2.cx(0, 1)
qc_b2.z(0)
qc_b2.draw(output='mpl', style="iqp")
sv = backend.run(qc_b2).result().get_statevector()
sv.draw(output='latex', prefix = "|\Phi^-\\rangle = ")
qc_b3 = QuantumCircuit(2, 2)
qc_b3.x(1)
qc_b3.h(0)
qc_b3.cx(0, 1)
qc_b3.draw(output='mpl', style="iqp")
sv = backend.run(qc_b3).result().get_statevector()
sv.draw(output='latex', prefix = "|\Psi^+\\rangle = ")
qc_b3 = QuantumCircuit(2, 2)
qc_b3.h(0)
qc_b3.cx(0, 1)
qc_b3.x(0)
qc_b3.draw(output='mpl', style="iqp")
sv = backend.run(qc_b3).result().get_statevector()
sv.draw(output='latex', prefix = "|\Psi^+\\rangle = ")
qc_b4 = QuantumCircuit(2, 2)
qc_b4.x(0)
qc_b4.h(0)
qc_b4.x(1)
qc_b4.cx(0, 1)
qc_b4.draw(output='mpl', style="iqp")
sv = backend.run(qc_b4).result().get_statevector()
sv.draw(output='latex', prefix = "|\Psi^-\\rangle = ")
qc_b4 = QuantumCircuit(2, 2)
qc_b4.h(0)
qc_b4.cx(0, 1)
qc_b4.x(0)
qc_b4.z(1)
qc_b4.draw(output='mpl', style="iqp")
sv = backend.run(qc_b4).result().get_statevector()
sv.draw(output='latex', prefix = "|\Psi^-\\rangle = ")
def sv_latex_from_qc(qc, backend):
sv = backend.run(qc).result().get_statevector()
return sv.draw(output='latex')
qc_ej2 = QuantumCircuit(4, 4)
qc_ej2.x(0)
sv_latex_from_qc(qc_ej2, backend)
qc_ej2 = QuantumCircuit(4, 4)
qc_ej2.x(1)
sv_latex_from_qc(qc_ej2, backend)
qc_ej2 = QuantumCircuit(4, 4)
qc_ej2.x(0)
qc_ej2.x(1)
sv_latex_from_qc(qc_ej2, backend)
qc_ej2 = QuantumCircuit(4, 4)
qc_ej2.x(2)
sv_latex_from_qc(qc_ej2, backend)
qc_ej2 = QuantumCircuit(4, 4)
qc_ej2.x(0)
qc_ej2.x(2)
sv_latex_from_qc(qc_ej2, backend)
qc_ej2 = QuantumCircuit(4, 4)
qc_ej2.x(0)
qc_ej2.x(2)
sv_latex_from_qc(qc_ej2, backend)
qc_ej2 = QuantumCircuit(4, 4)
qc_ej2.x(0)
qc_ej2.x(1)
qc_ej2.x(2)
sv_latex_from_qc(qc_ej2, backend)
qc_ej2 = QuantumCircuit(4, 4)
qc_ej2.x(3)
sv_latex_from_qc(qc_ej2, backend)
def circuit_adder (num):
if num<1 or num>8:
raise ValueError("Out of range") ## El enunciado limita el sumador a los valores entre 1 y 8. Quitar esta restricción sería directo.
# Definición del circuito base que vamos a construir
qreg_q = QuantumRegister(4, 'q')
creg_c = ClassicalRegister(1, 'c')
circuit = QuantumCircuit(qreg_q, creg_c)
qbit_position = 0
for element in reversed(np.binary_repr(num)):
if (element=='1'):
circuit.barrier()
match qbit_position:
case 0: # +1
circuit.append(C3XGate(), [qreg_q[0], qreg_q[1], qreg_q[2], qreg_q[3]])
circuit.ccx(qreg_q[0], qreg_q[1], qreg_q[2])
circuit.cx(qreg_q[0], qreg_q[1])
circuit.x(qreg_q[0])
case 1: # +2
circuit.ccx(qreg_q[1], qreg_q[2], qreg_q[3])
circuit.cx(qreg_q[1], qreg_q[2])
circuit.x(qreg_q[1])
case 2: # +4
circuit.cx(qreg_q[2], qreg_q[3])
circuit.x(qreg_q[2])
case 3: # +8
circuit.x(qreg_q[3])
qbit_position+=1
return circuit
add_3 = circuit_adder(3)
add_3.draw(output='mpl', style="iqp")
qc_test_2 = QuantumCircuit(4, 4)
qc_test_2.x(1)
qc_test_2_plus_3 = qc_test_2.compose(add_3)
qc_test_2_plus_3.draw(output='mpl', style="iqp")
sv_latex_from_qc(qc_test_2_plus_3, backend)
qc_test_7 = QuantumCircuit(4, 4)
qc_test_7.x(0)
qc_test_7.x(1)
qc_test_7.x(2)
qc_test_7_plus_8 = qc_test_7.compose(circuit_adder(8))
sv_latex_from_qc(qc_test_7_plus_8, backend)
#qc_test_7_plus_8.draw()
theta = 6.544985
phi = 2.338741
lmbda = 0
alice_1 = 0
alice_2 = 1
bob_1 = 2
qr_alice = QuantumRegister(2, 'Alice')
qr_bob = QuantumRegister(1, 'Bob')
cr = ClassicalRegister(3, 'c')
qc_ej3 = QuantumCircuit(qr_alice, qr_bob, cr)
qc_ej3.barrier(label='1')
qc_ej3.u(theta, phi, lmbda, alice_1);
qc_ej3.barrier(label='2')
qc_ej3.h(alice_2)
qc_ej3.cx(alice_2, bob_1);
qc_ej3.barrier(label='3')
qc_ej3.cx(alice_1, alice_2)
qc_ej3.h(alice_1);
qc_ej3.barrier(label='4')
qc_ej3.measure([alice_1, alice_2], [alice_1, alice_2]);
qc_ej3.barrier(label='5')
qc_ej3.x(bob_1).c_if(alice_2, 1)
qc_ej3.z(bob_1).c_if(alice_1, 1)
qc_ej3.measure(bob_1, bob_1);
qc_ej3.draw(output='mpl', style="iqp")
result = backend.run(qc_ej3, shots=1024).result()
counts = result.get_counts()
plot_histogram(counts)
sv_0 = np.array([1, 0])
sv_1 = np.array([0, 1])
def find_symbolic_representation(value, symbolic_constants={1/np.sqrt(2): '1/√2'}, tolerance=1e-10):
"""
Check if the given numerical value corresponds to a symbolic constant within a specified tolerance.
Parameters:
- value (float): The numerical value to check.
- symbolic_constants (dict): A dictionary mapping numerical values to their symbolic representations.
Defaults to {1/np.sqrt(2): '1/√2'}.
- tolerance (float): Tolerance for comparing values with symbolic constants. Defaults to 1e-10.
Returns:
str or float: If a match is found, returns the symbolic representation as a string
(prefixed with '-' if the value is negative); otherwise, returns the original value.
"""
for constant, symbol in symbolic_constants.items():
if np.isclose(abs(value), constant, atol=tolerance):
return symbol if value >= 0 else '-' + symbol
return value
def array_to_dirac_notation(array, tolerance=1e-10):
"""
Convert a complex-valued array representing a quantum state in superposition
to Dirac notation.
Parameters:
- array (numpy.ndarray): The complex-valued array representing
the quantum state in superposition.
- tolerance (float): Tolerance for considering amplitudes as negligible.
Returns:
str: The Dirac notation representation of the quantum state.
"""
# Ensure the statevector is normalized
array = array / np.linalg.norm(array)
# Get the number of qubits
num_qubits = int(np.log2(len(array)))
# Find indices where amplitude is not negligible
non_zero_indices = np.where(np.abs(array) > tolerance)[0]
# Generate Dirac notation terms
terms = [
(find_symbolic_representation(array[i]), format(i, f"0{num_qubits}b"))
for i in non_zero_indices
]
# Format Dirac notation
dirac_notation = " + ".join([f"{amplitude}|{binary_rep}⟩" for amplitude, binary_rep in terms])
return dirac_notation
def array_to_matrix_representation(array):
"""
Convert a one-dimensional array to a column matrix representation.
Parameters:
- array (numpy.ndarray): The one-dimensional array to be converted.
Returns:
numpy.ndarray: The column matrix representation of the input array.
"""
# Replace symbolic constants with their representations
matrix_representation = np.array([find_symbolic_representation(value) or value for value in array])
# Return the column matrix representation
return matrix_representation.reshape((len(matrix_representation), 1))
def array_to_dirac_and_matrix_latex(array):
"""
Generate LaTeX code for displaying both the matrix representation and Dirac notation
of a quantum state.
Parameters:
- array (numpy.ndarray): The complex-valued array representing the quantum state.
Returns:
Latex: A Latex object containing LaTeX code for displaying both representations.
"""
matrix_representation = array_to_matrix_representation(array)
latex = "Matrix representation\n\\begin{bmatrix}\n" + \
"\\\\\n".join(map(str, matrix_representation.flatten())) + \
"\n\\end{bmatrix}\n"
latex += f'Dirac Notation:\n{array_to_dirac_notation(array)}'
return Latex(latex)
sv_b1 = np.kron(sv_0, sv_0)
array_to_dirac_and_matrix_latex(sv_b1)
sv_b1 = (np.kron(sv_0, sv_0) + np.kron(sv_0, sv_1)) / np.sqrt(2)
array_to_dirac_and_matrix_latex(sv_b1)
sv_b1 = (np.kron(sv_0, sv_0) + np.kron(sv_1, sv_1)) / np.sqrt(2)
array_to_dirac_and_matrix_latex(sv_b1)
sv_b2 = np.kron(sv_0, sv_0)
array_to_dirac_and_matrix_latex(sv_b2)
sv_b2 = (np.kron(sv_0, sv_0) + np.kron(sv_0, sv_1)) / np.sqrt(2)
array_to_dirac_and_matrix_latex(sv_b2)
sv_b2 = (np.kron(sv_0, sv_0) + np.kron(sv_1, sv_1)) / np.sqrt(2)
array_to_dirac_and_matrix_latex(sv_b2)
sv_b2 = (np.kron(sv_0, sv_0) - np.kron(sv_1, sv_1)) / np.sqrt(2)
array_to_dirac_and_matrix_latex(sv_b2)
sv_b3 = np.kron(sv_0, sv_0)
array_to_dirac_and_matrix_latex(sv_b3)
sv_b3 = np.kron(sv_0, sv_1)
array_to_dirac_and_matrix_latex(sv_b3)
sv_b3 = (np.kron(sv_0, sv_0) - np.kron(sv_0, sv_1)) / np.sqrt(2)
array_to_dirac_and_matrix_latex(sv_b3)
sv_b3 = (np.kron(sv_0, sv_1) - np.kron(sv_1, sv_0)) / np.sqrt(2)
array_to_dirac_and_matrix_latex(sv_b3)
sv_b3 = (np.kron(sv_0, sv_1) + np.kron(sv_1, sv_0)) / np.sqrt(2)
array_to_dirac_and_matrix_latex(sv_b3)
sv_b4 = np.kron(sv_0, sv_0)
array_to_dirac_and_matrix_latex(sv_b4)
sv_b4 = np.kron(sv_0, sv_1)
array_to_dirac_and_matrix_latex(sv_b4)
sv_b4 = (np.kron(sv_0, sv_0) - np.kron(sv_0, sv_1)) / np.sqrt(2)
array_to_dirac_and_matrix_latex(sv_b4)
sv_b4 = (np.kron(sv_0, sv_1) - np.kron(sv_1, sv_0)) / np.sqrt(2)
array_to_dirac_and_matrix_latex(sv_b4)
|
https://github.com/Qottmann/Quantum-anomaly-detection
|
Qottmann
|
import sys
from matplotlib import pyplot as plt
import numpy as np
import matplotlib.gridspec as gridspec
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
path = "data/BH_phase-diagrams-and-loss/data_rike/"
x = np.load(path + "x.npy")
y = np.load(path + "y.npy")
es = np.load(path + "es2.npy")
dd = np.load(path + "dd2.npy")
cost0 = np.load(path + "cost_bh_L12_trash2_d48_v5_seed17.npy")
cost1 = np.load(path + "cost_bh_L12_trash4_d25_v47_seed16.npy")
cost2 = np.load(path + "cost_bh_L12_trash6_d10_v10_seed22.npy")
vs = np.logspace(-2,2,50)
ds = np.linspace(-0.95,0.95,50)
fig,aa = plt.subplots(figsize=(16,4),sharey="row",dpi=220)
fs_labels = 17
fs_ticks = 16
ax00 = plt.subplot2grid(shape=(8,25), loc=(0, 0), rowspan=4, colspan=4, xticklabels=[])
ax10 = plt.subplot2grid(shape=(8,25), loc=(4, 0), rowspan=4, colspan=4, xticklabels=[])
ax01 = plt.subplot2grid(shape=(8,25), loc=(1, 7), rowspan=6, colspan=6, xticklabels=[])
ax02 = plt.subplot2grid(shape=(8,25), loc=(1, 13), rowspan=6, colspan=6, xticklabels=[], yticklabels=[])
ax03 = plt.subplot2grid(shape=(8,25), loc=(1, 19), rowspan=6, colspan=6, xticklabels=[], yticklabels=[])
cmap = plt.get_cmap("plasma") #'viridis'
cbar_labelsize = 10
cbar = np.zeros((2,2), dtype="object")
axs=np.array([[ax00,ax10],[ax01,ax02]])
print(axs[0,0])
for ax in axs.flatten():
ax.tick_params(labelsize=fs_ticks)
ax = axs[0,0]
axs[0,0].set_title("DMRG", fontsize=14)
im = ax.pcolormesh(x,y,dd.T, cmap=cmap, shading="auto",rasterized=True) #,rasterized=True necessary for pdf export
#im.ax.tick_params(labelsize=cbar_labelsize)
ax.set_xscale("log")
cbar[0,0] = fig.colorbar(im, ax=ax)
cbar[0,0].ax.tick_params(labelsize=fs_ticks)
cbar[0,0].ax.set_ylabel("$O_{CDW}$",fontsize=fs_labels)
axs[0,0].set_ylabel(r"$\delta J$",fontsize=fs_labels)
ax.set_xticks([], minor=True)
ax.set_xticks([], minor=False)
ax = axs[0,1]
im = ax.pcolormesh(x,y,es.T, cmap=cmap, shading="auto",rasterized=True)
ax.set_xscale("log")
cbar[0,1] = fig.colorbar(im, ax=ax)
cbar[0,1].ax.tick_params(labelsize=fs_ticks)
cbar[0,1].ax.set_ylabel("$D_{ES}$",fontsize=fs_labels)
axs[0,1].set_xlabel(r"$V/J$",fontsize=fs_labels)
axs[0,1].set_ylabel(r"$\delta J$",fontsize=fs_labels)
ax = axs[1,0]
im = ax.pcolormesh(x,y,cost2.T,vmin=0, cmap=cmap, shading="auto",rasterized=True)
ax.set_xscale("log")
cbar[1,0] = fig.colorbar(im, ax=ax)
cbar[1,0].ax.tick_params(labelsize=fs_ticks)
ax.plot(vs[10],ds[10],"X", color="magenta",markersize=15, alpha=0.8)
axs[1,0].set_xlabel(r"$V/J$",fontsize=fs_labels)
axs[1,0].set_ylabel(r"$\delta J$",fontsize=fs_labels)
ax = axs[1,1]
im = ax.pcolormesh(x,y,cost1.T,vmin=0, cmap=cmap, shading="auto",rasterized=True)
ax.set_xscale("log")
cbar[1,1] = fig.colorbar(im, ax=ax)
cbar[1,1].ax.tick_params(labelsize=fs_ticks)
cbar[1,1].ax.set_title("cost", fontsize=fs_labels)
ax.plot(vs[47],ds[25],"X", color="magenta",markersize=15, alpha=0.8)
axs[1,1].set_xlabel(r"$V/J$",fontsize=fs_labels)
ax.set_yticks([])
ax = ax03
ax.tick_params(labelsize=fs_ticks)
im = ax.pcolormesh(x,y,cost0.T,vmin=0, cmap=cmap, shading="auto",rasterized=True)
ax.set_xscale("log")
cbar[1,1] = fig.colorbar(im, ax=ax)
cbar[1,1].ax.tick_params(labelsize=fs_ticks)
cbar[1,1].ax.set_title("cost", fontsize=fs_labels)
ax.plot(vs[5],ds[48],"X", color="magenta",markersize=15, alpha=0.8)
ax03.set_xlabel(r"$V/J$",fontsize=fs_labels)
ax.set_yticks([])
plt.tight_layout()
# has to happen after tight_layout()
axs[0,0].text(-0.21,0.89,"(a)", fontweight="bold", size=fs_labels, transform = axs[0,0].transAxes)
axs[1,0].text(0.5,1.07,"(c)", fontweight="bold", size=fs_labels, transform = axs[1,0].transAxes)
axs[0,1].text(-0.21,0.88,"(b)", fontweight="bold", size=fs_labels, transform = axs[0,1].transAxes)
axs[1,1].text(0.5,1.07,"(d)", fontweight="bold", size=fs_labels, transform = axs[1,1].transAxes)
ax03.text(0.5,1.07,"(e)", fontweight="bold", size=fs_labels, transform = ax03.transAxes)
axs[1,0].text(0.33,0.15,"MI", fontweight="bold", size=fs_labels, transform = axs[1,0].transAxes, color="white")
axs[1,0].text(0.23,0.8,"TMI", fontweight="bold", size=fs_labels, transform = axs[1,0].transAxes)
axs[1,0].text(0.7,0.5,"CDW", fontweight="bold", size=fs_labels, transform = axs[1,0].transAxes)
cbar[1,0].ax.set_title("cost", fontsize=fs_labels)
plt.savefig("plots/BH_paper.pdf", bbox_inches='tight')
cost = np.load("data/ibmq_antiferro-2D_load-AD_10x10_noisy-rome-simu_thetas-loss-cost_run1.npy",allow_pickle=True)
qmag = np.load("data/noisy_rome_simu_VQE_maxiter-500_Ising_L5_anti_-1_10x10.npz", allow_pickle=True)["Qmag"].reshape(10,10)
x,y = np.logspace(-2,2,10), np.logspace(-2,2,10)
x,y = np.meshgrid(x,y)
fig,axs = plt.subplots(ncols=2, nrows=1,figsize=(8,3),sharex=True, sharey=True, dpi=220)
cmap1 = plt.get_cmap("viridis")
cmap2 = plt.get_cmap("plasma")
cbar_labelsize = 18
cbar = np.zeros((2), dtype="object")
ax0 = axs[0]
im = ax0.pcolormesh(x, y, qmag, cmap=cmap1, shading="auto", rasterized=True) #,rasterized=True necessary for pdf export
cbar[0] = fig.colorbar(im, ax=ax0)
cbar[0].ax.tick_params(labelsize=cbar_labelsize)
cbar[0].ax.set_ylabel("$\hat{S}$",fontsize=20)
ax1 = axs[1]
im = ax1.pcolormesh(x, y, cost,vmin=0, cmap=cmap2, shading="auto",rasterized=True)
cbar[1] = fig.colorbar(im, ax=ax1)
cbar[1].ax.tick_params(labelsize=cbar_labelsize)
cbar[1].ax.set_ylabel("cost",fontsize=20)
#ax.plot(vs[47],ds[25],"x", color="magenta")
ax1.plot(1e-2,1e-2,"X", color="magenta",markersize=15, alpha=0.7, clip_on=False)
ax0.set_yscale("log")
ax0.set_yscale("log")
ax1.set_xscale("log")
ax0.set_xscale("log")
ax0.tick_params(labelsize=18)
ax1.tick_params(labelsize=18)
ax1.set_xlabel(r"$g_x$",fontsize=20)
ax0.set_ylabel(r"$g_z$",fontsize=20)
ax0.set_xlabel(r"$g_x$",fontsize=20)
#ax1.set_yticks([], minor=True)
#ax1.set_yticks([], minor=False)
plt.tight_layout()
# has to happen after tight_layout()
axs[0].text(-0.35,0.9,"(a)", fontweight="bold", size=18, transform = axs[0].transAxes)
axs[1].text(-0.2, 0.9,"(b)", fontweight="bold", size=18, transform = axs[1].transAxes)
plt.savefig("plots/antiferro2D_paper.pdf", bbox_inches='tight')
anti = -1
L = 5
num_trash = 2
name = "ibmq_antiferro-1D-load_bogota-optimize-20points" # 01.06.2021 experiment
filename = "data/noisy_VQE_maxiter-500_Ising_L5_anti_-1_20" #"data/noisy_VQE_maxiter-100_Ising_L5_anti_-1_20_recycle"
print("filename: ", filename, "notebook name: ", name)
# where to get the simulated thetas values from? needs to contain a thetas_mitigated array
filename_simulated_thetas = 'data/ibmq_antiferro-1D-load_bogota-optimize_thetas-loss-cost_run2.npz'
# this is noisy simulation data
L = 5
num_trash = 2
anti = -1
VQE_params = np.load(filename + ".npz", allow_pickle=True)
pick = np.arange(0,len(VQE_params['gx_list']))
gx_list = VQE_params['gx_list'][pick]
gz_list = VQE_params['gz_list'][pick]
opt_params = VQE_params['opt_params'][pick]
Qmags = VQE_params["Qmag"][pick]
Qen = VQE_params["Qen"][pick]
Sen = VQE_params["Sen"][pick]
Smags = VQE_params["Smag"][pick]
temp = np.load("data/" + name + "executed_mags-Es.npz",allow_pickle=True)
Qmags_executed = temp["Qmags"]
gx_vals = np.unique(gx_list)
gz_vals = np.unique(gz_list)
fig, axs = plt.subplots(nrows=2,figsize=(5,5),sharex=True,gridspec_kw={'height_ratios': [2, 2]},dpi=220)
ax = axs[0]
cost = np.load("data/" + "ibmq_antiferro-1D-load_simu" + "_thetas-loss-cost_run1.npz", allow_pickle=True)["cost_mitigated"]
ax.plot(gx_list, cost,".:")#, label="loss noisy sim.")
cost = np.load("data/" + name + "_thetas-loss-cost_run1.npz", allow_pickle=True)["cost_mitigated"]
ax.plot(gx_list, cost,".:")#, label="loss ibmq_bogota")
ax.plot(gx_list, Qmags,"x--", label="$\hat{S}$ noisy sim.")
ax.plot(gx_list, Qmags_executed,"x--", label="$\hat{S}$ ibmq$\_$bogota")
ax.set_xscale("log")
ax.plot(gx_list[0],cost[0], "X",markersize=20,alpha=0.4,color="magenta")
ax = axs[1]
cost = np.load("data/" + "ibmq_antiferro-1D-load_simu" + "_thetas-loss-cost_run2.npz", allow_pickle=True)["cost_mitigated"]
ax.plot(gx_list, cost,"x--", label="cost noisy sim.")
cost = np.load("data/" + name + "_thetas-loss-cost_run2.npz", allow_pickle=True)["cost_mitigated"]
ax.plot(gx_list, cost,".:", label="cost ibmq$\_$bogota")
ax.plot(gx_list, abs(Qmags),"x--", label="$\mid \hat{S} \mid$ noisy sim.")
ax.plot(gx_list, abs(Qmags_executed),".:", label="$\mid \hat{S} \mid$ ibmq$\_$bogota")
ax.set_xscale("log")
ax.plot(gx_list[-1],cost[-1],"X",markersize=20,alpha=0.4,color="magenta")
for ax in axs:
ax.legend()
ax.tick_params(labelsize=14)
axs[-1].set_xlabel("$g_x$", fontsize=18)
plt.tight_layout()
axs[0].text(-0.11,0.9,"(a)", fontweight="bold", size=18, transform = axs[0].transAxes)
axs[1].text(-0.11,0.9,"(b)", fontweight="bold", size=18, transform = axs[1].transAxes)
plt.savefig("plots/" + name + "_mainplot.png", bbox_inches='tight')
plt.savefig("plots/" + name + "_mainplot.pdf", bbox_inches='tight')
|
https://github.com/Qottmann/Quantum-anomaly-detection
|
Qottmann
|
"""A quantum auto-encoder (QAE)."""
from typing import Union, Optional, List, Tuple, Callable, Any
import numpy as np
from qiskit import *
from qiskit.circuit.quantumcircuit import QuantumCircuit
from qiskit.circuit.library import TwoLocal
from qiskit.circuit.library.standard_gates import RYGate, CZGate
from qiskit.circuit.gate import Gate
from qiskit.algorithms.optimizers import Optimizer, SPSA
from qiskit.utils import algorithm_globals
from qiskit.providers import BaseBackend, Backend
class QAEAnsatz(TwoLocal):
def __init__(
self,
num_qubits: int,
num_trash_qubits: int,
trash_qubits_idxs: Union[np.ndarray, List] = [1, 2], # TODO
measure_trash: bool = False,
rotation_blocks: Gate = RYGate,
entanglement_blocks: Gate = CZGate,
parameter_prefix: str = 'θ',
insert_barriers: bool = False,
initial_state: Optional[Any] = None,
) -> None:
"""Create a new QAE circuit.
Args:
num_qubits: The number of qubits of the QAE circuit.
num_trash_qubits: The number of trash qubits that should be measured in the end.
trash_qubits_idxs: The explicit indices of the trash qubits, i.e., where the trash
qubits should be placed.
measure_trash: If True, the trash qubits will be measured at the end. If False, no
measurement takes place.
rotation_blocks: The blocks used in the rotation layers. If multiple are passed,
these will be applied one after another (like new sub-layers).
entanglement_blocks: The blocks used in the entanglement layers. If multiple are passed,
these will be applied one after another.
parameter_prefix: The prefix used if default parameters are generated.
insert_barriers: If True, barriers are inserted in between each layer. If False,
no barriers are inserted.
initial_state: A `QuantumCircuit` object which can be used to describe an initial state
prepended to the NLocal circuit.
"""
assert num_trash_qubits < num_qubits
self.num_trash_qubits = num_trash_qubits
self.trash_qubits_idxs = trash_qubits_idxs
self.measure_trash = measure_trash
entanglement = [QAEAnsatz._generate_entangler_map(
num_qubits, num_trash_qubits, i, trash_qubits_idxs) for i in range(num_trash_qubits)]
super().__init__(num_qubits=num_qubits,
rotation_blocks=rotation_blocks,
entanglement_blocks=entanglement_blocks,
entanglement=entanglement,
reps=num_trash_qubits,
skip_final_rotation_layer=True,
parameter_prefix=parameter_prefix,
insert_barriers=insert_barriers,
initial_state=initial_state)
self.add_register(ClassicalRegister(self.num_trash_qubits))
@staticmethod
def _generate_entangler_map(num_qubits: int, num_trash_qubits: int, i_permut: int = 1, trash_qubits_idxs: Union[np.ndarray, List] = [1, 2]) -> List[Tuple[int, int]]:
"""Generates entanglement map for QAE circuit
Entangling gates are only added between trash and non-trash-qubits.
Args:
num_qubits: The number of qubits of the QAE circuit.
num_trash_qubits: The number of trash qubits that should be measured in the end.
i_permut: Permutation index; increases for every layer of the circuit
trash_qubits_idxs: The explicit indices of the trash qubits, i.e., where the trash
qubits should be placed.
Returns:
entanglement map: List of pairs of qubit indices that should be entangled
"""
result = []
nums_compressed = list(range(num_qubits))
for trashqubit in trash_qubits_idxs:
nums_compressed.remove(trashqubit)
if trash_qubits_idxs == None:
nums_compressed = list(range(num_qubits))[:num_qubits-num_trash_qubits]
trash_qubits_idxs = list(range(num_qubits))[-num_trash_qubits:]
# combine all trash qubits with themselves
for i,trash_q in enumerate(trash_qubits_idxs[:-1]):
result.append((trash_qubits_idxs[i+1], trash_qubits_idxs[i]))
# combine each of the trash qubits with every n-th
# repeat the list of trash indices cyclicly
repeated = list(trash_qubits_idxs) * (num_qubits-num_trash_qubits)
for i in range(num_qubits-num_trash_qubits):
result.append((repeated[i_permut + i], nums_compressed[i]))
return result
def _build(self) -> None:
"""Build the circuit."""
if self._data:
return
_ = self._check_configuration()
self._data = []
if self.num_qubits == 0:
return
# use the initial state circuit if it is not None
if self._initial_state:
circuit = self._initial_state.construct_circuit('circuit', register=self.qregs[0])
self.compose(circuit, inplace=True)
param_iter = iter(self.ordered_parameters)
# build the prepended layers
self._build_additional_layers('prepended')
# main loop to build the entanglement and rotation layers
for i in range(self.reps):
# insert barrier if specified and there is a preceding layer
if self._insert_barriers and (i > 0 or len(self._prepended_blocks) > 0):
self.barrier()
# build the rotation layer
self._build_rotation_layer(param_iter, i)
# barrier in between rotation and entanglement layer
if self._insert_barriers and len(self._rotation_blocks) > 0:
self.barrier()
# build the entanglement layer
self._build_entanglement_layer(param_iter, i)
# add the final rotation layer
if self.insert_barriers and self.reps > 0:
self.barrier()
for j, block in enumerate(self.rotation_blocks):
# create a new layer
layer = QuantumCircuit(*self.qregs)
block_indices = [[i] for i in self.trash_qubits_idxs]
# apply the operations in the layer
for indices in block_indices:
parameterized_block = self._parameterize_block(block, param_iter, i, j, indices)
layer.compose(parameterized_block, indices, inplace=True)
# add the layer to the circuit
self.compose(layer, inplace=True)
# add the appended layers
self._build_additional_layers('appended')
# measure trash qubits if set
if self.measure_trash:
for i, j in enumerate(self.trash_qubits_idxs):
self.measure(self.qregs[0][j], self.cregs[0][i])
@property
def num_parameters_settable(self) -> int:
"""The number of total parameters that can be set to distinct values.
Returns:
The number of parameters originally available in the circuit.
"""
return super().num_parameters_settable + self.num_trash_qubits
def hamming_distance(out) -> int:
"""Computes the Hamming distance of a measurement outcome to the
all zero state. For example: A single measurement outcome 101 would
have a Hamming distance of 2.
Args:
out: The measurement outcomes; a dictionary containing all possible measurement strings
as keys and their occurences as values.
Returns:
Hamming distance
"""
return sum(key.count('1') * value for key, value in out.items())
class QAE:
def __init__(
self,
num_qubits: int,
num_trash_qubits: int,
ansatz: Optional[QuantumCircuit] = None,
initial_params: Optional[Union[np.ndarray, List]] = None,
optimizer: Optional[Optimizer] = None,
shots: int = 1000,
num_epochs: int = 100,
save_training_curve: Optional[bool] = False,
seed: int = 123,
backend: Union[BaseBackend, Backend] = Aer.get_backend('qasm_simulator')
) -> None:
"""Quantum auto-encoder.
Args:
num_qubits: The number of qubits of the QAE circuit.
num_trash_qubits: The number of trash qubits that should be measured in the end.
ansatz: A parameterized quantum circuit ansatz to be optimized.
initial_params: The initial list of parameters for the circuit ansatz
optimizer: The optimizer used for training (default is SPSA)
shots: The number of measurement shots when training and evaluating the QAE.
num_epochs: The number of training iterations/epochs.
save_training_curve: If True, the cost after each optimizer step is computed and stored.
seed: Random number seed.
backend: The backend on which the QAE is performed.
"""
algorithm_globals.random_seed = seed
np.random.seed(seed)
self.costs = []
if save_training_curve:
callback = self._store_intermediate_result
else:
callback = None
if optimizer:
self.optimizer = optimizer
else:
self.optimizer = SPSA(num_epochs, callback=callback)
self.backend = backend
if ansatz:
self.ansatz = ansatz
else:
self.ansatz = QAEAnsatz(num_qubits, num_trash_qubits, measure_trash=True)
if initial_params:
self.initial_params = initial_params
else:
self.initial_params = np.random.uniform(0, 2*np.pi, self.ansatz.num_parameters_settable)
self.shots = shots
self.save_training_curve = save_training_curve
def run(self, input_state: Optional[Any] = None, params: Optional[Union[np.ndarray, List]] = None):
"""Execute ansatz circuit and measure trash qubits
Args:
input_state: If provided, circuit is initialized accordingly
params: If provided, list of optimization parameters for circuit
Returns:
measurement outcomes
"""
if params is None:
params = self.initial_params
if input_state is not None:
if type(input_state) == QuantumCircuit:
circ = input_state
elif type(input_state) == list or type(input_state) == np.ndarray:
circ = QuantumCircuit(self.ansatz.num_qubits, self.ansatz.num_trash_qubits)
circ.initialize(input_state)
else:
raise TypeError("input_state has to be an array or a QuantumCircuit.")
circ = circ.compose(self.ansatz)
else:
circ = self.ansatz
circ = circ.assign_parameters(params)
job_sim = execute(circ, self.backend, shots=self.shots)
return job_sim.result().get_counts(circ)
def cost(self, input_state: Optional[Any] = None, params: Optional[Union[np.ndarray, List]] = None) -> float:
""" Cost function
Average Hamming distance of measurement outcomes to zero state.
Args:
input_state: If provided, circuit is initialized accordingly
params: If provided, list of optimization parameters for circuit
Returns:
Cost
"""
out = self.run(input_state, params)
cost = hamming_distance(out)
return cost/self.shots
def _store_intermediate_result(self, eval_count, parameters, mean, std, ac):
"""Callback function to save intermediate costs during training."""
self.costs.append(mean)
def train(self, input_state: Optional[Any] = None):
""" Trains the QAE using optimizer (default SPSA)
Args:
input_state: If provided, circuit is initialized accordingly
Returns:
Result of optimization: optimized parameters, cost, iterations
Training curve: Cost function evaluated after each iteration
"""
result = self.optimizer.optimize(
num_vars=len(self.initial_params),
objective_function=lambda params: self.cost(input_state, params),
initial_point=self.initial_params
)
self.initial_params = result[0]
return result, self.costs
def reset(self):
"""Resets parameters to random values"""
self.costs = []
self.initial_params = np.random.uniform(0, 2*np.pi, self.ansatz.num_parameters_settable)
if __name__ == '__main__':
num_qubits = 5
num_trash_qubits = 2
qae = QAE(num_qubits, num_trash_qubits, save_training_curve=True)
# for demonstration purposes QAE is trained on a random state
input_state = np.random.uniform(size=2**num_qubits)
input_state /= np.linalg.norm(input_state)
result, cost = qae.train(input_state)
|
https://github.com/Qottmann/Quantum-anomaly-detection
|
Qottmann
|
import time
import itertools
import numpy as np
import qiskit
from qiskit import *
from qiskit.quantum_info import Statevector
from qiskit.algorithms.optimizers import COBYLA, L_BFGS_B, SPSA
from qiskit.opflow.state_fns import StateFn, CircuitStateFn
from qiskit.providers.aer import StatevectorSimulator, AerSimulator
from qiskit.opflow import CircuitSampler
from qiskit.ignis.mitigation.measurement import CompleteMeasFitter # you will need to pip install qiskit-ignis
from qiskit.ignis.mitigation.measurement import complete_meas_cal
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
from matplotlib.colors import BoundaryNorm
cmap = plt.get_cmap("plasma") #'viridis'
from modules.utils import *
from qae import *
import datetime
import tenpy
from tenpy.networks.mps import MPS
from tenpy.models.hubbard import BoseHubbardChain
from tenpy.models.tf_ising import TFIChain
from tenpy.algorithms import dmrg
from tenpy.linalg import np_conserved
def DMRG_EBH(L, V, t_list, chi_max=30, bc_MPS='infinite'):
model_params = dict(n_max=1, filling=0.5, bc_MPS=bc_MPS, t=t_list,
L=L, V=V, mu=0, conserve='N')
M = BoseHubbardChain(model_params)
vector=[]
for i in range(M.lat.N_sites):
if i%2:
vector.append(1)
else:
vector.append(0)
psi = MPS.from_product_state(M.lat.mps_sites(), vector, bc=M.lat.bc_MPS)
dmrg_params = {
'mixer': True,
'trunc_params': {
'chi_max': chi_max,
},
'max_E_err': 1.e-16,
#'verbose': 0
}
info = dmrg.run(psi, M, dmrg_params)
return info['E'], psi
def DMRG_Ising(L, J, g, chi_max=30, bc_MPS='finite'):
model_params = dict(bc_MPS=bc_MPS, bc_x="open",
L=L, J=J, g=g, conserve="best")
M = TFIChain(model_params)
product_state = ["up"] * M.lat.N_sites
psi = MPS.from_product_state(M.lat.mps_sites(), product_state, bc=M.lat.bc_MPS)
dmrg_params = {
'mixer': True,
'trunc_params': {
'chi_max': chi_max,
},
'max_E_err': 1e-16,
#'verbose': 0
}
info = dmrg.run(psi, M, dmrg_params)
return info['E'], psi
### Preliminaries
qiskit_chi = 100
L = 8
num_trash = int(np.log(L)/np.log(2))
anti = 1 # 1 for ferromagnetic Ising model, -1 for antiferromagnet
g = 0.05
J=anti
filename = "data/QAEAnsatz_scaling_MPS_script"
backend = qiskit.providers.aer.AerSimulator(method="matrix_product_state",
precision="single",
matrix_product_state_max_bond_dimension = qiskit_chi,
matrix_product_state_truncation_threshold = 1e-10,
#mps_sample_measure_algorithm = "mps_apply_measure", #alt: "mps_probabilities"
)
from qiskit.utils import algorithm_globals
algorithm_globals.random_seed = 333
def qiskit_state(psi0):
# G is only the local tensor (not multiplied by any singular values) - see https://tenpy.readthedocs.io/en/latest/reference/tenpy.networks.mps.html
A_list = [psi0.get_B(i, form="G").to_ndarray().transpose([1,0,2]) for i in range(L)]
for i,A in enumerate(A_list):
A_list[i] = (A[0], A[1])
S_list = [psi0.get_SR(i) for i in range(L-1)] # skip trivial last bond; hast to be of size L-1
return (A_list, S_list)
def prepare_circuit(thetas, L=6, num_trash=2, init_state=None, measurement=True,vqe=False):
# QAE ansatz
QAE_circ = QAEAnsatz(num_qubits = L, num_trash_qubits= num_trash, trash_qubits_idxs = list(range(L//2-1,L//2-1+num_trash)), measure_trash=measurement).assign_parameters(thetas)
# initialize state vector
initcirc = QuantumCircuit(QuantumRegister(L,"q"),ClassicalRegister(num_trash, 'c'))
if init_state != None:
initcirc.set_matrix_product_state(qiskit_state(init_state))
# compose circuits
fullcirc = initcirc.compose(QAE_circ)
return fullcirc
### Execute circuit
count = 0
def run_circuit(thetas, L, num_trash, init_state, vqe=False, shots=100, meas_fitter = None):
#global count
#count += 1
#print(count, "thetas: ", thetas)
#print(L, num_trash)
circ = prepare_circuit(thetas, L, num_trash, init_state, vqe=vqe)
#circ.draw("mpl")
#tcirc = qiskit.transpile(circ, backend)
# Execute the circuit
job_sim = backend.run(circ, shots=shots, seed_simulator=333, seed_transpiler=444) #fix seed to make it reproducible
result = job_sim.result()
# Results without mitigation
counts = result.get_counts()
if meas_fitter != None:
# Get the filter object
meas_filter = meas_fitter.filter
# Results with mitigation
mitigated_results = meas_filter.apply(result)
counts = mitigated_results.get_counts(0)
return counts
def count_ones(string):
return np.sum([int(_) for _ in string])
### Optimize circuit
def cost_function_single(thetas, L, num_trash, init_state, shots=1000, vqe=True, param_encoding=False, x=0, meas_fitter=None):
""" Optimizes circuit """
if param_encoding: thetas = feature_encoding(thetas, x)
out = run_circuit(thetas, L, num_trash, init_state, vqe=vqe, shots=shots, meas_fitter=meas_fitter)
cost = np.sum([out[_]*count_ones(_) for _ in out if _ != "0" * num_trash]) # all measurement results except "000"
return cost/shots
def cost_function(thetas, L, num_trash, init_states, shots=1000, vqe=True, param_encoding=False, x=0, meas_fitter=None):
""" Optimizes circuit """
cost = 0.
for init_state in init_states:
cost += cost_function_single(thetas, L, num_trash, init_state, shots, vqe, param_encoding, meas_fitter=meas_fitter)
return cost/len(init_states)
def optimize(init_states, L=6, num_trash=2, thetas=None, shots=1000, max_iter=400, vqe=True, param_encoding=False, x=0, pick_optimizer = None,
meas_fitter=None):
if thetas is None:
n_params = (num_trash*L+num_trasg)*2 if param_encoding else (num_trash*L+num_trash)
thetas = np.random.uniform(0, 2*np.pi, n_params) # initial parameters without feature encoding
#print("Initial cost: {:.3f}".format(cost_function(thetas, L, num_trash, init_states, shots, vqe, param_encoding, x)))
counts, values, accepted = [], [], []
def store_intermediate_result(eval_count, parameters, mean, std, ac):
# counts.append(eval_count)
values.append(mean)
accepted.append(ac)
# Initialize optimizer
if pick_optimizer == "cobyla":
optimizer = COBYLA(maxiter=max_iter, tol=0.0001)
if pick_optimizer == "adam" or pick_optimizer == "ADAM":
optimizer = qiskit.algorithms.optimizers.ADAM(maxiter=max_iter)
# optimizer = L_BFGS_B(maxfun=300, maxiter=max_iter)#, factr=10, iprint=- 1, epsilon=1e-08)
if pick_optimizer == "spsa" or pick_optimizer == None:
optimizer = SPSA(maxiter=max_iter,
#blocking=True,
callback=store_intermediate_result,
#learning_rate=0.3,
#perturbation=0.1
) # recommended from qiskit (first iteraction takes quite long)
# to reduce time figure out optimal learning rate and perturbation in advance
start_time = time.time()
ret = optimizer.optimize(
num_vars=len(thetas),
objective_function=(lambda thetas: cost_function(thetas, L, num_trash, init_states, shots, vqe, param_encoding, x, meas_fitter=meas_fitter)),
initial_point=thetas
)
print("Time: {:.5f} sec".format(time.time()-start_time))
print(ret)
return ret[0], values, accepted
Ls = [12,12,12,12] # [3,4,8,10,12,14,16,20, 32, 32, 32]
max_iter = [400] * len(Ls)
num_trashs = np.log(Ls)/np.log(2)
num_trashs = np.array(num_trashs, dtype="int")
losses = [None] * len(Ls); accepted = [None] * len(Ls); thetas_opt= [None] * len(Ls)
times = [None] * len(Ls)
for j,L in enumerate(Ls):
L = Ls[j]
num_trash = num_trashs[j]
# BH
V = 1
deltat=-1
chi = 100
g=0.05 # ordered phase to make things a bit more interesting
print(f"bond dimension {chi}, max_iter {max_iter[j]}, L {L}, num_trash {num_trash}")
t_list = np.ones(L-1)
for i in range(len(t_list)):
t_list[i] -= deltat*(-1)**i
#E0, psi0 = DMRG_EBH(L, V, t_list, chi_max=chi, bc_MPS='finite')
E0, psi0 = DMRG_Ising(L, J, g, chi_max=chi, bc_MPS='finite')
SZ = psi0.expectation_value("Sigmaz")
print(f"Psi magnetization: {SZ}") # note that in tenpy Sz and Sx roles are reversed, i.e. in tenpy its -SxSx - g Sz
t0 = time.time()
thetas_opt[j], losses[j], accepted[j] = optimize([psi0], max_iter=max_iter[j], L=L, num_trash=num_trash, meas_fitter=None) #, pick_optimizer="adam")
times[j] = time.time() - t0
loss_final = cost_function_single(thetas_opt[j], L, num_trash, psi0, shots=1000)
print(f"opt loss = {loss_final}, min loss = {np.min(losses[j])}, computation time = {times[j]} sec")
print(f"losses vs steps: ", losses[j]) # to save computation time, dont save intermediate results and just compute the loss in the end
np.savez(filename + "_results", thetas_opt=thetas_opt, losses=losses, accepted=accepted,Ls=Ls, max_iter=max_iter, times=times)
|
https://github.com/Qottmann/Quantum-anomaly-detection
|
Qottmann
|
import time
import itertools
import numpy as np
import qiskit
from qiskit import *
from qiskit.quantum_info import Statevector
from qiskit.algorithms.optimizers import COBYLA, L_BFGS_B, SPSA
from qiskit.opflow.state_fns import StateFn, CircuitStateFn
from qiskit.providers.aer import StatevectorSimulator, AerSimulator
from qiskit.opflow import CircuitSampler
from qiskit.ignis.mitigation.measurement import CompleteMeasFitter # you will need to pip install qiskit-ignis
from qiskit.ignis.mitigation.measurement import complete_meas_cal
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
from matplotlib.colors import BoundaryNorm
cmap = plt.get_cmap("plasma") #'viridis'
from modules.utils import *
from qae import *
import datetime
print(qiskit.__version__, np.__version__)
#plt.rc('text', usetex=True)
plt.rc('font', family='serif',size=16)
#plt.rc('font', family='serif')
temp = np.load("data/QAEAnsatz_scaling_losses.npz", allow_pickle=True)
losses0 = temp["losses"]
Ls = temp["Ls"]
# It took 51 hrs to obtain these results.
# However, you can confirm the validity by just executing the circuit once for the optimized parameters (as demonstrated down below)
temp2 = np.load("data/QAEAnsatz_scaling_MPS_script_L-32_tracked-losses_results.npz",allow_pickle=True)
losses32 = temp2["losses"]
Ls = list(Ls)
Ls += [32]
Ls
fig, ax = plt.subplots(figsize=(6,5))
for i in range(4):
ax.plot(np.array(losses0[i]),label=f"L = {Ls[i]}")
ax.plot(losses32[1],label=f"L = {Ls[-1]}")
ax.legend()
left, bottom, width, height = [0.4,0.61, 0.25, 0.325]
ax2 = fig.add_axes([left, bottom, width, height])
for i in range(4):
ax2.plot(np.array(losses0[i]),label=f"L = {Ls[i]}")
ax2.plot(losses32[1],label=f"L = {Ls[-1]}")
ax2.set_yscale("log")
#ax.set_xlim(0,405)
#ax.set_yscale("log")
ax.set_ylabel("cost", fontsize=16)
ax.set_xlabel("iteration", fontsize=16)
plt.tight_layout()
plt.savefig("plots/QAEAnsatz_Ising-scaling.pdf")
plt.savefig("plots/QAEAnsatz_Ising-scaling.png")
#IBMQ.load_account() # this then automatically loads your saved account
#provider = IBMQ.get_provider(hub='ibm-q-research')
#device = provider.backend.ibmq_rome # 6 bogota ; 4 rome
### Real device execution:
#backend = device
### Simulation with noise profile from real device
#backend = qiskit.providers.aer.AerSimulator.from_backend(device)
### Simulation without noise
backend = qiskit.providers.aer.AerSimulator()
#backend = StatevectorSimulator()
### Preliminaries
L = 5
num_trash = 2
anti = 1 # 1 for ferromagnetic Ising model, -1 for antiferromagnet
filename = "data/QAEAnsatz_scaling"
gz = 0
gx = 0.3
ED_state, ED_E, ham = ising_groundstate(L, anti, np.float32(gx), np.float32(gz))
Sen = ED_E
Smag = ED_state.T.conj()@Mag(L,anti)@ED_state
def prepare_circuit(thetas, L=6, num_trash=2, init_state=None, measurement=True,vqe=False):
# QAE ansatz
QAE_circ = QAEAnsatz(num_qubits = L, num_trash_qubits= num_trash, trash_qubits_idxs = list(range(num_trash)), measure_trash=measurement).assign_parameters(thetas)
# initialize state vector
initcirc = QuantumCircuit(QuantumRegister(L,"q"),ClassicalRegister(num_trash, 'c'))
initcirc.initialize(init_state, initcirc.qubits)
# compose circuits
fullcirc = initcirc.compose(QAE_circ)
return fullcirc
circ = prepare_circuit(thetas = np.random.rand(2*L+2), L = L, init_state = ED_state)
circ.draw("mpl")
### Execute circuit
count = 0
def run_circuit(thetas, L, num_trash, init_state, vqe=False, shots=100, meas_fitter = None):
#global count
#count += 1
#print(count, "thetas: ", thetas)
circ = prepare_circuit(thetas, L, num_trash, init_state, vqe=vqe)
tcirc = qiskit.transpile(circ, backend)
# Execute the circuit
job_sim = backend.run(tcirc, shots=shots,seed_simulator=333, seed_transpiler=444) # , seed_simulator=123, seed_transpiler=234 fix seed to make it reproducible
result = job_sim.result()
# Results without mitigation
counts = result.get_counts()
if meas_fitter != None:
# Get the filter object
meas_filter = meas_fitter.filter
# Results with mitigation
mitigated_results = meas_filter.apply(result)
counts = mitigated_results.get_counts(0)
return counts
res = run_circuit(thetas = np.random.rand(num_trash*L+num_trash), L = L, num_trash = num_trash, init_state = ED_state, shots=1000)
res
def count_ones(string):
return np.sum([int(_) for _ in string])
count_ones("01010111")
[_ for _ in res]
[_ for _ in res if _ != "0" * num_trash]
np.sum([res[_]*count_ones(_) for _ in res if _ != "0" * num_trash]) # all measurement results except "000"
### Optimize circuit
def cost_function_single(thetas, L, num_trash, init_state, shots=1000, vqe=True, param_encoding=False, x=0, meas_fitter=None):
""" Optimizes circuit """
if param_encoding: thetas = feature_encoding(thetas, x)
out = run_circuit(thetas, L, num_trash, init_state, vqe=vqe, shots=shots, meas_fitter=meas_fitter)
cost = np.sum([out[_]*count_ones(_) for _ in out if _ != "0" * num_trash]) # all measurement results except "000"
return cost/shots
def cost_function(thetas, L, num_trash, init_states, shots=1000, vqe=True, param_encoding=False, x=0, meas_fitter=None):
""" Optimizes circuit """
cost = 0.
for init_state in init_states:
cost += cost_function_single(thetas, L, num_trash, init_state, shots, vqe, param_encoding, meas_fitter=meas_fitter)
return cost/len(init_states)
def optimize(init_states, L=6, num_trash=2, thetas=None, shots=1000, max_iter=400, vqe=True, param_encoding=False, x=0, pick_optimizer = None,
meas_fitter=None):
if thetas is None:
n_params = (num_trash*L+num_trasg)*2 if param_encoding else (num_trash*L+num_trash)
thetas = np.random.uniform(0, 2*np.pi, n_params) # initial parameters without feature encoding
#print("Initial cost: {:.3f}".format(cost_function(thetas, L, num_trash, init_states, shots, vqe, param_encoding, x)))
counts, values, accepted = [], [], []
def store_intermediate_result(eval_count, parameters, mean, std, ac):
# counts.append(eval_count)
values.append(mean)
accepted.append(ac)
# Initialize optimizer
if pick_optimizer == "cobyla":
optimizer = COBYLA(maxiter=max_iter, tol=0.0001)
if pick_optimizer == "adam" or pick_optimizer == "ADAM":
optimizer = qiskit.algorithms.optimizers.ADAM(maxiter=max_iter)
# optimizer = L_BFGS_B(maxfun=300, maxiter=max_iter)#, factr=10, iprint=- 1, epsilon=1e-08)
if pick_optimizer == "spsa" or pick_optimizer == None:
optimizer = SPSA(maxiter=max_iter,
#blocking=True,
callback=store_intermediate_result,
#learning_rate=0.3,
#perturbation=0.1
) # recommended from qiskit (first iteraction takes quite long)
# to reduce time figure out optimal learning rate and perturbation in advance
start_time = time.time()
ret = optimizer.optimize(
num_vars=len(thetas),
objective_function=(lambda thetas: cost_function(thetas, L, num_trash, init_states, shots, vqe, param_encoding, x, meas_fitter=meas_fitter)),
initial_point=thetas
)
print("Time: {:.5f} sec".format(time.time()-start_time))
print(ret)
return ret[0], values, accepted
thetas_opt_mitigated, losses, accepted = optimize([ED_state], max_iter=120, L=5, meas_fitter=None) #, pick_optimizer="adam")
plt.plot(losses)
Ls = [3,4,8,16] #note that L=16 may take up few hours, it is faster with MPS further below
max_iter = [400] * len(Ls)
num_trashs = np.log(Ls)/np.log(2)
num_trashs = np.array(num_trashs, dtype="int")
gz = 0
gx = 0.3
losses = [None] * len(Ls); accepted = [None] * len(Ls); thetas_opt= [None] * len(Ls)
# may have to re-run some sizes when it gets stuck in local minima
for j,(L,num_trash) in enumerate(zip(Ls,num_trashs)):
print(L,num_trash)
ED_state, ED_E, ham = ising_groundstate(L, anti, np.float32(gx), np.float32(gz))
thetas_opt[j], losses[j], accepted[j] = optimize([ED_state], max_iter=max_iter[j], L=L, num_trash=num_trash, meas_fitter=None) #, pick_optimizer="adam")
plt.plot(losses[j])
plt.show()
np.savez(filename + "_losses", losses=losses, thetas_opt = thetas_opt, Ls=Ls, max_iter=max_iter, num_trashs=num_trashs)
temp = np.load(filename + "_losses.npz", allow_pickle=True)
losses0 = temp["losses"]
fig, ax = plt.subplots(figsize=(6,5))
for i in range(4):
ax.plot(np.array(losses0[i]) + 1e-4,label=f"L = {Ls[i]}")
ax.legend()
ax.set_xlim(0,400)
ax.set_yscale("log")
ax.set_yticks([1,1e-1,1e-2,1e-3,1e-4])
ax.set_yticklabels(["$10^{}$".format(i) for i in range(4)] + ["0.0"])
ax.set_ylabel("cost", fontsize=16)
ax.set_xlabel("iterations", fontsize=16)
min_losses = [np.min(l) for l in losses0]
min_losses
fig, ax = plt.subplots(figsize=(6,5))
ax.plot(Ls, min_losses,"x--")
#ax.set_yscale("log")
#ax.set_xscale("log")
def DMRG_Ising(L, J, g, chi_max=30, bc_MPS='finite'):
print("finite DMRG, transverse field Ising model")
print("L={L:d}, g={g:.2f}".format(L=L, g=g))
model_params = dict(L=L, J=J, g=g, bc_MPS=bc_MPS, conserve=None)
M = TFIChain(model_params)
product_state = ["up"] * M.lat.N_sites
psi = MPS.from_product_state(M.lat.mps_sites(), product_state, bc=M.lat.bc_MPS)
dmrg_params = {
'mixer': None, # setting this to True helps to escape local minima
'max_E_err': 1.e-10,
'trunc_params': {
'chi_max': chi_max,
'svd_min': 1.e-10
},
'combine': True
}
info = dmrg.run(psi, M, dmrg_params) # the main work...
E = info['E']
print("E = {E:.13f}".format(E=E))
print("final bond dimensions: ", psi.chi)
mag_x = np.sum(psi.expectation_value("Sigmax"))
mag_z = np.sum(psi.expectation_value("Sigmaz"))
print("magnetization in X = {mag_x:.5f}".format(mag_x=mag_x))
print("magnetization in Z = {mag_z:.5f}".format(mag_z=mag_z))
#if L < 20: # compare to exact result
# from tfi_exact import finite_gs_energy
# E_exact = finite_gs_energy(L, 1., g)
# print("Exact diagonalization: E = {E:.13f}".format(E=E_exact))
# print("relative error: ", abs((E - E_exact) / E_exact))
return E, psi #, M
### Preliminaries
qiskit_chi = 100
L = 8
num_trash = int(np.log(L)/np.log(2))
anti = 1 # 1 for ferromagnetic Ising model, -1 for antiferromagnet
g = 0.05
J=anti
filename = "data/QAEAnsatz_scaling_MPS_script_L-32_tracked-losses"
backend = qiskit.providers.aer.AerSimulator(method="matrix_product_state",
precision="single",
matrix_product_state_max_bond_dimension = qiskit_chi,
matrix_product_state_truncation_threshold = 1e-10,
#mps_sample_measure_algorithm = "mps_apply_measure", #alt: "mps_probabilities"
)
from qiskit.utils import algorithm_globals
algorithm_globals.random_seed = 333
def qiskit_state(psi0):
# G is only the local tensor (not multiplied by any singular values) - see https://tenpy.readthedocs.io/en/latest/reference/tenpy.networks.mps.html
A_list = [psi0.get_B(i, form="G").to_ndarray().transpose([1,0,2]) for i in range(L)]
for i,A in enumerate(A_list):
A_list[i] = (A[0], A[1])
S_list = [psi0.get_SR(i) for i in range(L-1)] # skip trivial last bond; hast to be of size L-1
return (A_list, S_list)
def prepare_circuit(thetas, L=6, num_trash=2, init_state=None, measurement=True,vqe=False):
# QAE ansatz
QAE_circ = QAEAnsatz(num_qubits = L, num_trash_qubits= num_trash, trash_qubits_idxs = list(range(L//2-1,L//2-1+num_trash)), measure_trash=measurement).assign_parameters(thetas)
# initialize state vector
initcirc = QuantumCircuit(QuantumRegister(L,"q"),ClassicalRegister(num_trash, 'c'))
if init_state != None:
initcirc.set_matrix_product_state(qiskit_state(init_state))
# compose circuits
fullcirc = initcirc.compose(QAE_circ)
return fullcirc
### Execute circuit
count = 0
def run_circuit(thetas, L, num_trash, init_state, vqe=False, shots=100, meas_fitter = None):
#global count
#count += 1
#print(count, "thetas: ", thetas)
#print(L, num_trash)
circ = prepare_circuit(thetas, L, num_trash, init_state, vqe=vqe)
#circ.draw("mpl")
#tcirc = qiskit.transpile(circ, backend)
# Execute the circuit
job_sim = backend.run(circ, shots=shots, seed_simulator=333, seed_transpiler=444) #fix seed to make it reproducible
result = job_sim.result()
# Results without mitigation
counts = result.get_counts()
if meas_fitter != None:
# Get the filter object
meas_filter = meas_fitter.filter
# Results with mitigation
mitigated_results = meas_filter.apply(result)
counts = mitigated_results.get_counts(0)
return counts
def count_ones(string):
return np.sum([int(_) for _ in string])
### Optimize circuit
def cost_function_single(thetas, L, num_trash, init_state, shots=1000, vqe=True, param_encoding=False, x=0, meas_fitter=None):
""" Optimizes circuit """
if param_encoding: thetas = feature_encoding(thetas, x)
out = run_circuit(thetas, L, num_trash, init_state, vqe=vqe, shots=shots, meas_fitter=meas_fitter)
cost = np.sum([out[_]*count_ones(_) for _ in out if _ != "0" * num_trash]) # all measurement results except "000"
return cost/shots
def cost_function(thetas, L, num_trash, init_states, shots=1000, vqe=True, param_encoding=False, x=0, meas_fitter=None):
""" Optimizes circuit """
cost = 0.
for init_state in init_states:
cost += cost_function_single(thetas, L, num_trash, init_state, shots, vqe, param_encoding, meas_fitter=meas_fitter)
return cost/len(init_states)
def optimize(init_states, L=6, num_trash=2, thetas=None, shots=1000, max_iter=400, vqe=True, param_encoding=False, x=0, pick_optimizer = None,
meas_fitter=None):
if thetas is None:
n_params = (num_trash*L+num_trasg)*2 if param_encoding else (num_trash*L+num_trash)
thetas = np.random.uniform(0, 2*np.pi, n_params) # initial parameters without feature encoding
#print("Initial cost: {:.3f}".format(cost_function(thetas, L, num_trash, init_states, shots, vqe, param_encoding, x)))
counts, values, accepted = [], [], []
def store_intermediate_result(eval_count, parameters, mean, std, ac):
# counts.append(eval_count)
values.append(mean)
accepted.append(ac)
# Initialize optimizer
if pick_optimizer == "cobyla":
optimizer = COBYLA(maxiter=max_iter, tol=0.0001)
if pick_optimizer == "adam" or pick_optimizer == "ADAM":
optimizer = qiskit.algorithms.optimizers.ADAM(maxiter=max_iter)
# optimizer = L_BFGS_B(maxfun=300, maxiter=max_iter)#, factr=10, iprint=- 1, epsilon=1e-08)
if pick_optimizer == "spsa" or pick_optimizer == None:
optimizer = SPSA(maxiter=max_iter,
#blocking=True,
callback=store_intermediate_result,
#learning_rate=0.3,
#perturbation=0.1
) # recommended from qiskit (first iteraction takes quite long)
# to reduce time figure out optimal learning rate and perturbation in advance
start_time = time.time()
ret = optimizer.optimize(
num_vars=len(thetas),
objective_function=(lambda thetas: cost_function(thetas, L, num_trash, init_states, shots, vqe, param_encoding, x, meas_fitter=meas_fitter)),
initial_point=thetas
)
print("Time: {:.5f} sec".format(time.time()-start_time))
print(ret)
return ret[0], values, accepted
temp2 = np.load("data/QAEAnsatz_scaling/QAEAnsatz_scaling_MPS_script_L-32_results.npz",allow_pickle=True) # 500 iterations, gx = 0.3, chi=100 (but resulting bond dimension of state much smaller)
psi0 = temp2["psi0"].item()
thetas_opt = temp2["thetas_opt"][0]
L = psi0.L
loss_final = cost_function_single(thetas_opt, L, int(np.log(L)/np.log(2)), psi0, shots=1000)
print(f"Result for L = {L}: loss_opt = {loss_final} with optimal parameters {thetas_opt}")
temp2 = np.load("data/QAEAnsatz_scaling_MPS_script_L-32_tracked-losses_results.npz",allow_pickle=True) # 500 iterations, gx = 0.3, chi=100 (but resulting bond dimension of state much smaller)
psi0 = temp2["psis"][1]
thetas_opt = temp2["thetas_opt"][1]
L = psi0.L
loss_final = cost_function_single(thetas_opt, L, int(np.log(L)/np.log(2)), psi0, shots=1000)
print(f"Result for L = {L}: loss_opt = {loss_final} with optimal parameters {thetas_opt}")
|
https://github.com/Qottmann/Quantum-anomaly-detection
|
Qottmann
|
import sys
sys.path.insert(1, '..') # sets the import path to the parent folder
import time
import datetime
import numpy as np
from matplotlib import pyplot as plt
import qiskit
from qiskit import *
from qiskit.opflow import X,Z,I
from qiskit.opflow.state_fns import StateFn, CircuitStateFn
from qiskit.providers.aer import StatevectorSimulator, AerSimulator
from qiskit.algorithms import VQE
from qiskit.algorithms.optimizers import COBYLA, SLSQP, SPSA
from qiskit.opflow import CircuitSampler
from qiskit.ignis.mitigation.measurement import CompleteMeasFitter # you will need to pip install qiskit-ignis
from qiskit.ignis.mitigation.measurement import complete_meas_cal
from scipy import sparse
import scipy.sparse.linalg.eigen.arpack as arp
from modules.utils import *
anti = -1
L = 5
num_trash = 2
name = "ibmq_antiferro-1D-load_bogota-no-optimize" # remove test at the end when running on a real device
filename = "data/noisy_VQE_maxiter-500_Ising_L5_anti_-1_20" #"data/noisy_VQE_maxiter-100_Ising_L5_anti_-1_20_recycle"
print("filename: ", filename, "notebook name: ", name)
# where to get the simulated thetas values from? needs to contain a thetas_mitigated array
filename_simulated_thetas = 'data/ibmq_antiferro-1D-load_simu_thetas-loss-cost_run2.npz'
load = False
recompute = False # whether or not to recompute Magnetization, makes sense on device
IBMQ.load_account() # this then automatically loads your saved account
provider = IBMQ.get_provider(hub='ibm-q-research')
device = provider.backend.ibmq_bogota
print(device)
backend = device
#backend = qiskit.providers.aer.AerSimulator.from_backend(device)
coupling_map = device.configuration().coupling_map
noise_model = qiskit.providers.aer.noise.NoiseModel.from_backend(device)
basis_gates = noise_model.basis_gates
#aqua_globals.random_seed = seed
qi = qiskit.utils.QuantumInstance(backend=backend, # , seed_simulator=seed, seed_transpiler=seed
coupling_map=coupling_map, # noise_model=noise_model,
measurement_error_mitigation_cls= CompleteMeasFitter,
cals_matrix_refresh_period=30 #How often to refresh the calibration matrix in measurement mitigation. in minutes
)
basis_gates
# Very important, at the moment poorly coded so it needs to come back to this instance all the time
ansatz = qiskit.circuit.library.TwoLocal(L,rotation_blocks="ry", entanglement_blocks='cz', entanglement="sca", reps=1)
ansatz.draw("mpl")
ansatz = qiskit.transpile(ansatz, backend)
ansatz.draw("mpl")
L = 5
num_trash = 2
anti = -1
VQE_params = np.load(filename + ".npz", allow_pickle=True)
pick = np.arange(0,len(VQE_params['gx_list']),3)
gx_list = VQE_params['gx_list'][pick]
gz_list = VQE_params['gz_list'][pick]
opt_params = VQE_params['opt_params'][pick]
Qmags = VQE_params["Qmag"][pick]
Qen = VQE_params["Qen"][pick]
Sen = VQE_params["Sen"][pick]
Smags = VQE_params["Smag"][pick]
gx_vals = np.unique(gx_list)
gz_vals = np.unique(gz_list)
if load:
temp = np.load("data/" + name + "executed_mags-Es.npz",allow_pickle=True)
Qmags = temp["Qmags"]
Qen = temp["Qen"]
Sen = temp["Sen"]
Smags = temp["Smags"]
verbose=1
if recompute:
mag = QMag(L,anti) #magnetization operator (Qiskit)
Smag = Mag(L,anti) #magnetization operator (numpy)
Qen_executed=np.zeros(len(opt_params), dtype="complex")
Qmags_executed=np.zeros(len(opt_params), dtype="complex")
for j in range(len(opt_params)):
t0 = datetime.datetime.now()
gx = gx_list[j]
gz = gz_list[j]
H = QHIsing(L, anti, np.float32(gx), np.float32(gz)) # build Hamiltonian Op
state = ansatz.assign_parameters(opt_params[j])
meas_outcome = ~StateFn(mag) @ StateFn(state)
Qmags_executed[j] = CircuitSampler(qi).convert(meas_outcome).eval() #https://quantumcomputing.stackexchange.com/questions/12080/evaluating-expectation-values-of-operators-in-qiskit
#e_outcome = ~StateFn(H) @ StateFn(state)
#Qen_executed[j] = CircuitSampler(qi).convert(e_outcome).eval()
init_state, E, ham = ising_groundstate(L, anti, np.float64(gx), np.float64(gz))
Sen[j] = E
Smags[j] = init_state.T.conj()@Smag@init_state #Magnetization with Numpy results
print(f"{j+1} / {len(opt_params)}, gx = {gx:.2f}, gz = {gz:.2f}, time : {(datetime.datetime.now() - t0)}")
np.savez("data/" + name + "executed_mags-Es.npz",Qmags=Qmags_executed, Qen=Qen_executed, Sen=Sen, Smags=Smags) # for large parameter space takes quite a while because of the exact diagonalization
fig, axs = plt.subplots(ncols=2, figsize=(10,5))
ax = axs[0]
ax.plot(gx_list, Qmags,"x--", label="noisy VQE simu")
ax.plot(gx_list, Smags,"x--", label="ED")
ax.set_xscale("log")
if recompute:
ax.plot(gx_list, Qmags_executed,"x--", label="IBMQ")
ax.legend()
##############################################################################
### II - Training ###########################################################
##############################################################################
# linear entangler (as in scales linearly with trash qubits)
def get_entangler_map(L, num_trash, i_permut=1, nums_trash=[1,2]):
result = []
nums_compressed = list(range(L))
for trashqubit in nums_trash:
nums_compressed.remove(trashqubit)
if nums_trash == None: #old way
nums_compressed = list(range(L))[:L-num_trash]
nums_trash = list(range(L))[-num_trash:]
#print(nums, nums_compressed, nums_trash)
# combine all trash qubits with themselves
for trash_q in nums_trash[:-1]:
result.append((trash_q+1,trash_q))
# combine each of the trash qubits with every n-th
repeated = list(nums_trash) * (L-num_trash) # repeat the list of trash indices cyclicly
for i in range(L-num_trash):
result.append((repeated[i_permut + i], nums_compressed[i]))
return result
def QAE_Ansatz(thetas, L, num_trash, insert_barriers=False, parametrized_gate = "ry", entangling_gate = "cz", nums_trash = [1,2]):
entanglement = [get_entangler_map(L,num_trash,i_permut) for i_permut in range(num_trash)]
if nums_trash == None:
nums_trash = list(range(L))[-num_trash:]
circ = qiskit.circuit.library.TwoLocal(L,
parametrized_gate,
entangling_gate,
entanglement,
reps=num_trash,
insert_barriers=insert_barriers,
skip_final_rotation_layer=True
).assign_parameters(thetas[:-num_trash])
if insert_barriers: circ.barrier()
for i in nums_trash:
circ.ry(thetas[i], i)
#circ.ry(circuit.Parameter(f'θ{i}'), L-i-1)
return circ
def prepare_circuit(thetas, L=6, num_trash=2, init_state=None, measurement=True, vqe=True):
qreg = QuantumRegister(L, 'q')
creg = ClassicalRegister(num_trash, 'c')
circ = QuantumCircuit(qreg, creg)
circ += QAE_Ansatz(thetas, L, num_trash, insert_barriers=False)#.assign_parameters(thetas) # difference to bind?
if measurement:
for i in range(num_trash):
circ.measure(qreg[L-i-1], creg[i])
if init_state is not None:
if vqe:
circ = ansatz.assign_parameters(init_state) + circ # needs to have ansatz defined somewhere in the script
else:
circ.initialize(init_state, qreg)
return circ
def feature_encoding(thetas, x):
""" thetas: parameters to be optimized, x: Ising model parameter (eg. field) """
new_thetas = []
thetas = thetas.reshape((-1,2))
for theta in thetas:
new_thetas.append(theta[0] * x + theta[1])
return new_thetas
def calibrate_circuit(L, num_trash,shots=1000):
qreg = QuantumRegister(L, 'q')
# obtain calibration matrix
qubit_list = [L-i-1 for i in range(num_trash)] # only need to calibrate the trash qubits
circlabel = f'mcal_{datetime.datetime.now()}'
meas_calibs, state_labels = complete_meas_cal(qubit_list=qubit_list, qr=qreg, circlabel=circlabel)
cal_job = backend.run(meas_calibs, shots=shots) #, noise_model=noise_model
cal_results = cal_job.result()
meas_fitter = CompleteMeasFitter(cal_results, state_labels, circlabel=circlabel)
print(circlabel, meas_fitter.cal_matrix)
return meas_fitter
def run_circuit(thetas, L, num_trash, init_state, vqe=True, shots=100, meas_fitter = None):
circ = prepare_circuit(thetas, L, num_trash, init_state, vqe=vqe)
tcirc = qiskit.transpile(circ, backend)
# Execute the circuit
job_sim = backend.run(tcirc, shots=shots) # , seed_simulator=123, seed_transpiler=234 fix seed to make it reproducible
result = job_sim.result()
# Results without mitigation
counts = result.get_counts()
if meas_fitter != None:
# Get the filter object
meas_filter = meas_fitter.filter
# Results with mitigation
mitigated_results = meas_filter.apply(result)
counts = mitigated_results.get_counts(0)
return counts
meas_fitter = calibrate_circuit(L, num_trash)
phis = opt_params
# translate to Rikes naming
gxs = gx_list
gzs = gz_list
def cost_function_single(thetas, L, num_trash, p, shots=1000, vqe=True, param_encoding=False, x=0, meas_fitter=None):
""" Optimizes circuit """
if vqe:
init_state = phis[p]
else:
J, gx, gz = p
init_state, _ = ising_groundstate(L, J, gx, gz)
if param_encoding: thetas = feature_encoding(thetas, x)
out = run_circuit(thetas, L, num_trash, init_state, vqe=vqe, shots=shots, meas_fitter=meas_fitter)
cost = out.get('11', 0)*2 + out.get('01', 0) + out.get('10', 0)
return cost/shots
def cost_function(thetas, L, num_trash, ising_params, shots=1000, vqe=True, param_encoding=False, x=0, meas_fitter=None):
""" Optimizes circuit """
cost = 0.
n_samples = len(ising_params)
for i, p in enumerate(ising_params):
if param_encoding:
cost += cost_function_single(thetas, L, num_trash, p, shots, vqe, param_encoding, x[i], meas_fitter=meas_fitter)
else:
cost += cost_function_single(thetas, L, num_trash, p, shots, vqe, param_encoding, meas_fitter=meas_fitter)
return cost/n_samples
def optimize(ising_params, L=6, num_trash=2, thetas=None, shots=1000, max_iter=400, vqe=True, param_encoding=False, x=0, pick_optimizer = None,
meas_fitter=None):
if thetas is None:
n_params = (2*L+2)*2 if param_encoding else (2*L+2)
thetas = np.random.uniform(0, 2*np.pi, n_params) # initial parameters without feature encoding
print("Initial cost: {:.3f}".format(cost_function(thetas, L, num_trash, ising_params, shots, vqe, param_encoding, x)))
counts, values, accepted = [], [], []
def store_intermediate_result(eval_count, parameters, mean, std, ac):
# counts.append(eval_count)
values.append(mean)
accepted.append(ac)
# Initialize optimizer
if pick_optimizer == "cobyla":
optimizer = COBYLA(maxiter=max_iter, tol=0.0001)
if pick_optimizer == "adam" or pick_optimizer == "ADAM":
optimizer = qiskit.algorithms.optimizers.ADAM(maxiter=max_iter)
# optimizer = L_BFGS_B(maxfun=300, maxiter=max_iter)#, factr=10, iprint=- 1, epsilon=1e-08)
if pick_optimizer == "spsa" or pick_optimizer == None:
optimizer = SPSA(maxiter=max_iter,
#blocking=True,
callback=store_intermediate_result,
learning_rate=0.3,
perturbation=0.1
) # recommended from qiskit (first iteraction takes quite long)
# to reduce time figure out optimal learning rate and perturbation in advance
start_time = time.time()
ret = optimizer.optimize(
num_vars=len(thetas),
objective_function=(lambda thetas: cost_function(thetas, L, num_trash, ising_params, shots, vqe, param_encoding, x, meas_fitter=meas_fitter)),
initial_point=thetas
)
print("Time: {:.5f} sec".format(time.time()-start_time))
print(ret)
return ret[0], values, accepted
def run_inference(thetas, shots=1000, L=5, meas_fitter=None):
points = 50
J = -1.0
x,y = np.meshgrid(gx_vals, gz_vals)
cost = np.zeros((len(gx_vals) * len(gz_vals)))
Smags = np.zeros((len(gx_vals) * len(gz_vals)))
shots = 1000
for i,p in enumerate(list(zip(gxs, gzs))):
t0 = datetime.datetime.now()
cost[i] = cost_function_single(thetas, L, num_trash, i, shots=shots, meas_fitter=meas_fitter)
if not i%verbose:
print(f"{i+1} / {len(opt_params)}, gx = {gx:.2f}, gz = {gz:.2f}, time : {verbose*(datetime.datetime.now() - t0)}")
cost = cost.reshape((len(gx_vals), len(gz_vals)))
return cost
phys_params = [-1]
thetas_opt_raw = np.load(filename_simulated_thetas, allow_pickle=True)["thetas_mitigated"]
# Inference
cost_raw = np.zeros((len(gx_vals)))
shots = 1000
for i,p in enumerate(list(zip(gxs, gzs))):
t0 = datetime.datetime.now()
cost_raw[i] = cost_function_single(thetas_opt_raw, L, num_trash, i, shots=shots, meas_fitter=None) # np.random.uniform(0, 2*np.pi, 2*L+2) random parameters to check if training "does" something - result was: with random paremters just get noise, so yes, it "does" something
if not i%verbose:
print(f"{i+1} / {len(opt_params)}, gx = {p[0]:.2f}, gz = {p[1]:.2f}, time : {verbose*(datetime.datetime.now() - t0)}")
thetas_opt_raw = thetas_opt_mitigated.copy()
cost_raw = cost_mitigated.copy()
thetas_opt_mitigated = np.load(filename_simulated_thetas, allow_pickle=True)["thetas_mitigated"]
# Inference
cost_mitigated = np.zeros((len(gx_vals)))
shots = 1000
for i,p in enumerate(list(zip(gxs, gzs))):
t0 = datetime.datetime.now()
cost_mitigated[i] = cost_function_single(thetas_opt_mitigated, L, num_trash, i, shots=shots, meas_fitter=meas_fitter) # np.random.uniform(0, 2*np.pi, 2*L+2) random parameters to check if training "does" something - result was: with random paremters just get noise, so yes, it "does" something
if not i%verbose:
print(f"{i+1} / {len(opt_params)}, gx = {p[0]:.2f}, gz = {p[1]:.2f}, time : {verbose*(datetime.datetime.now() - t0)}")
np.savez("data/" + name + "_thetas-loss-cost_run2",
cost_mitigated=cost_mitigated, thetas_mitigated=thetas_opt_mitigated,
cost_raw=cost_raw, thetas_raw=thetas_opt_raw,
)
fig, axs = plt.subplots(ncols=2,figsize=(12,5))
ax = axs[0]
ax.set_title("Raw results")
cost = np.load("data/" + name + "_thetas-loss-cost_run2.npz", allow_pickle=True)["cost_raw"]
ax.plot(gx_list, cost,".:", label="raw output")
ax.plot(gx_list, Qmags,"x--", color="tab:orange", label="Qmag")
ax.set_xscale("log")
for p in phys_params:
ax.plot(gx_list[p],cost[p],"o",alpha=0.3,color="magenta")
ax = axs[1]
ax.set_title("Mitigated results")
cost = np.load("data/" + name + "_thetas-loss-cost_run2.npz", allow_pickle=True)["cost_mitigated"]
ax.plot(gx_list, cost,".:", label="raw output")
ax.plot(gx_list, Qmags,"x--", color="tab:orange", label="Qmag")
ax.set_xscale("log")
for p in phys_params:
ax.plot(gx_list[p],cost[p],"o",alpha=0.3,color="magenta")
fig, ax = plt.subplots(ncols=1,figsize=(6,5))
ax.set_title("Mitigated results")
cost = np.load("data/" + name + "_thetas-loss-cost_run2.npz", allow_pickle=True)["cost_raw"]
ax.plot(gx_list, cost,".:", label="raw output")
ax.plot(gx_list, abs(Qmags),"x--", color="tab:orange", label="Qmag")
ax.set_xscale("log")
for p in phys_params:
ax.plot(gx_list[p],cost[p],"o",alpha=0.3,color="magenta")
|
https://github.com/Qottmann/Quantum-anomaly-detection
|
Qottmann
|
import time
import datetime
import numpy as np
from matplotlib import pyplot as plt
import qiskit
from qiskit import *
from qiskit.opflow import X,Z,I
from qiskit.opflow.state_fns import StateFn, CircuitStateFn
from qiskit.providers.aer import StatevectorSimulator, AerSimulator
from qiskit.algorithms import VQE
from qiskit.algorithms.optimizers import COBYLA, SLSQP, SPSA
from qiskit.opflow import CircuitSampler
from qiskit.ignis.mitigation.measurement import CompleteMeasFitter # you will need to pip install qiskit-ignis
from qiskit.ignis.mitigation.measurement import complete_meas_cal
from scipy import sparse
import scipy.sparse.linalg.eigen.arpack as arp
from modules.utils import *
anti = -1
L = 5
num_trash = 2
name = "ibmq_antiferro-1D-load_bogota-optimize-20points" # remove test at the end when running on a real device
filename = "data/noisy_VQE_maxiter-500_Ising_L5_anti_-1_20" #"data/noisy_VQE_maxiter-100_Ising_L5_anti_-1_20_recycle"
print("filename: ", filename, "notebook name: ", name)
# where to get the simulated thetas values from? needs to contain a thetas_mitigated array
filename_simulated_thetas = 'data/ibmq_antiferro-1D-load_bogota-optimize_thetas-loss-cost_run2.npz'
load = False
recompute = True # whether or not to recompute Magnetization, makes sense on device
IBMQ.load_account() # this then automatically loads your saved account
provider = IBMQ.get_provider(hub='ibm-q-research')
device = provider.backend.ibmq_bogota
print(device)
backend = device
#backend = qiskit.providers.aer.AerSimulator.from_backend(device)
coupling_map = device.configuration().coupling_map
noise_model = qiskit.providers.aer.noise.NoiseModel.from_backend(device)
basis_gates = noise_model.basis_gates
#aqua_globals.random_seed = seed
qi = qiskit.utils.QuantumInstance(backend=backend, # , seed_simulator=seed, seed_transpiler=seed
coupling_map=coupling_map, #, noise_model=noise_model,
measurement_error_mitigation_cls= CompleteMeasFitter,
cals_matrix_refresh_period=30 #How often to refresh the calibration matrix in measurement mitigation. in minutes
)
# Very important, at the moment poorly coded so it needs to come back to this instance all the time
ansatz = qiskit.circuit.library.TwoLocal(L,rotation_blocks="ry", entanglement_blocks='cz', entanglement="sca", reps=1)
ansatz.draw("mpl")
ansatz = qiskit.transpile(ansatz, backend)
ansatz.draw("mpl")
L = 5
num_trash = 2
anti = -1
VQE_params = np.load(filename + ".npz", allow_pickle=True)
pick = np.arange(0,len(VQE_params['gx_list']))
gx_list = VQE_params['gx_list'][pick]
gz_list = VQE_params['gz_list'][pick]
opt_params = VQE_params['opt_params'][pick]
Qmags = VQE_params["Qmag"][pick]
Qen = VQE_params["Qen"][pick]
Sen = VQE_params["Sen"][pick]
Smags = VQE_params["Smag"][pick]
gx_vals = np.unique(gx_list)
gz_vals = np.unique(gz_list)
if load:
temp = np.load("data/" + name + "executed_mags-Es.npz",allow_pickle=True)
Qmags = temp["Qmags"]
Qen = temp["Qen"]
Sen = temp["Sen"]
Smags = temp["Smags"]
verbose=1
if recompute:
mag = QMag(L,anti) #magnetization operator (Qiskit)
Smag = Mag(L,anti) #magnetization operator (numpy)
Qen_executed=np.zeros(len(opt_params), dtype="complex")
Qmags_executed=np.zeros(len(opt_params), dtype="complex")
for j in range(len(opt_params)):
t0 = datetime.datetime.now()
gx = gx_list[j]
gz = gz_list[j]
H = QHIsing(L, anti, np.float32(gx), np.float32(gz)) # build Hamiltonian Op
state = ansatz.assign_parameters(opt_params[j])
meas_outcome = ~StateFn(mag) @ StateFn(state)
Qmags_executed[j] = CircuitSampler(qi).convert(meas_outcome).eval() #https://quantumcomputing.stackexchange.com/questions/12080/evaluating-expectation-values-of-operators-in-qiskit
#e_outcome = ~StateFn(H) @ StateFn(state)
#Qen_executed[j] = CircuitSampler(qi).convert(e_outcome).eval()
#init_state, E, ham = ising_groundstate(L, anti, np.float64(gx), np.float64(gz))
#Sen[j] = E
#Smags[j] = init_state.T.conj()@Smag@init_state #Magnetization with Numpy results
print(f"{j+1} / {len(opt_params)}, gx = {gx:.2f}, gz = {gz:.2f}, time : {(datetime.datetime.now() - t0)}")
np.savez("data/" + name + "executed_mags-Es.npz",Qmags=Qmags_executed, Qen=Qen_executed, Sen=Sen, Smags=Smags) # for large parameter space takes quite a while because of the exact diagonalization
fig, axs = plt.subplots(ncols=2, figsize=(10,5))
ax = axs[0]
ax.plot(gx_list, Qmags,"x--", label="noisy VQE simu")
ax.plot(gx_list, Smags,"x--", label="ED")
ax.set_xscale("log")
if recompute or load:
ax.plot(gx_list, Qmags_executed,"x--", label="IBMQ")
ax.legend()
##############################################################################
### II - Training ###########################################################
##############################################################################
# linear entangler (as in scales linearly with trash qubits)
def get_entangler_map(L, num_trash, i_permut=1, nums_trash=[1,2]):
result = []
nums_compressed = list(range(L))
for trashqubit in nums_trash:
nums_compressed.remove(trashqubit)
if nums_trash == None: #old way
nums_compressed = list(range(L))[:L-num_trash]
nums_trash = list(range(L))[-num_trash:]
#print(nums, nums_compressed, nums_trash)
# combine all trash qubits with themselves
for trash_q in nums_trash[:-1]:
result.append((trash_q+1,trash_q))
# combine each of the trash qubits with every n-th
repeated = list(nums_trash) * (L-num_trash) # repeat the list of trash indices cyclicly
for i in range(L-num_trash):
result.append((repeated[i_permut + i], nums_compressed[i]))
return result
def QAE_Ansatz(thetas, L, num_trash, insert_barriers=False, parametrized_gate = "ry", entangling_gate = "cz", nums_trash = [1,2]):
entanglement = [get_entangler_map(L,num_trash,i_permut) for i_permut in range(num_trash)]
if nums_trash == None:
nums_trash = list(range(L))[-num_trash:]
circ = qiskit.circuit.library.TwoLocal(L,
parametrized_gate,
entangling_gate,
entanglement,
reps=num_trash,
insert_barriers=insert_barriers,
skip_final_rotation_layer=True
).assign_parameters(thetas[:-num_trash])
if insert_barriers: circ.barrier()
for i in nums_trash:
circ.ry(thetas[i], i)
#circ.ry(circuit.Parameter(f'θ{i}'), L-i-1)
return circ
def prepare_circuit(thetas, L=6, num_trash=2, init_state=None, measurement=True, vqe=True):
qreg = QuantumRegister(L, 'q')
creg = ClassicalRegister(num_trash, 'c')
circ = QuantumCircuit(qreg, creg)
circ += QAE_Ansatz(thetas, L, num_trash, insert_barriers=False)#.assign_parameters(thetas) # difference to bind?
if measurement:
for i in range(num_trash):
circ.measure(qreg[L-i-1], creg[i])
if init_state is not None:
if vqe:
circ = ansatz.assign_parameters(init_state) + circ # needs to have ansatz defined somewhere in the script
else:
circ.initialize(init_state, qreg)
return circ
def feature_encoding(thetas, x):
""" thetas: parameters to be optimized, x: Ising model parameter (eg. field) """
new_thetas = []
thetas = thetas.reshape((-1,2))
for theta in thetas:
new_thetas.append(theta[0] * x + theta[1])
return new_thetas
def calibrate_circuit(L, num_trash,shots=1000):
qreg = QuantumRegister(L, 'q')
# obtain calibration matrix
qubit_list = [L-i-1 for i in range(num_trash)] # only need to calibrate the trash qubits
circlabel = f'mcal_{datetime.datetime.now()}'
meas_calibs, state_labels = complete_meas_cal(qubit_list=qubit_list, qr=qreg, circlabel=circlabel)
cal_job = backend.run(meas_calibs, shots=shots) #, noise_model=noise_model)
cal_results = cal_job.result()
meas_fitter = CompleteMeasFitter(cal_results, state_labels, circlabel=circlabel)
print(circlabel, meas_fitter.cal_matrix)
return meas_fitter
def run_circuit(thetas, L, num_trash, init_state, vqe=True, shots=100, meas_fitter = None):
circ = prepare_circuit(thetas, L, num_trash, init_state, vqe=vqe)
tcirc = qiskit.transpile(circ, backend)
# Execute the circuit
job_sim = backend.run(tcirc, shots=shots) # , seed_simulator=123, seed_transpiler=234 fix seed to make it reproducible
result = job_sim.result()
# Results without mitigation
counts = result.get_counts()
if meas_fitter != None:
# Get the filter object
meas_filter = meas_fitter.filter
# Results with mitigation
mitigated_results = meas_filter.apply(result)
counts = mitigated_results.get_counts(0)
return counts
meas_fitter = calibrate_circuit(L, num_trash)
phis = opt_params
# translate to Rikes naming
gxs = gx_list
gzs = gz_list
def cost_function_single(thetas, L, num_trash, p, shots=1000, vqe=True, param_encoding=False, x=0, meas_fitter=None):
""" Optimizes circuit """
if vqe:
init_state = phis[p]
else:
J, gx, gz = p
init_state, _ = ising_groundstate(L, J, gx, gz)
if param_encoding: thetas = feature_encoding(thetas, x)
out = run_circuit(thetas, L, num_trash, init_state, vqe=vqe, shots=shots, meas_fitter=meas_fitter)
cost = out.get('11', 0)*2 + out.get('01', 0) + out.get('10', 0)
return cost/shots
def cost_function(thetas, L, num_trash, ising_params, shots=1000, vqe=True, param_encoding=False, x=0, meas_fitter=None):
""" Optimizes circuit """
cost = 0.
n_samples = len(ising_params)
for i, p in enumerate(ising_params):
if param_encoding:
cost += cost_function_single(thetas, L, num_trash, p, shots, vqe, param_encoding, x[i], meas_fitter=meas_fitter)
else:
cost += cost_function_single(thetas, L, num_trash, p, shots, vqe, param_encoding, meas_fitter=meas_fitter)
return cost/n_samples
def optimize(ising_params, L=6, num_trash=2, thetas=None, shots=1000, max_iter=400, vqe=True, param_encoding=False, x=0, pick_optimizer = None,
meas_fitter=None):
if thetas is None:
n_params = (2*L+2)*2 if param_encoding else (2*L+2)
thetas = np.random.uniform(0, 2*np.pi, n_params) # initial parameters without feature encoding
print("Initial cost: {:.3f}".format(cost_function(thetas, L, num_trash, ising_params, shots, vqe, param_encoding, x)))
counts, values, accepted = [], [], []
def store_intermediate_result(eval_count, parameters, mean, std, ac):
# counts.append(eval_count)
values.append(mean)
accepted.append(ac)
# Initialize optimizer
if pick_optimizer == "cobyla":
optimizer = COBYLA(maxiter=max_iter, tol=0.0001)
if pick_optimizer == "adam" or pick_optimizer == "ADAM":
optimizer = qiskit.algorithms.optimizers.ADAM(maxiter=max_iter)
# optimizer = L_BFGS_B(maxfun=300, maxiter=max_iter)#, factr=10, iprint=- 1, epsilon=1e-08)
if pick_optimizer == "spsa" or pick_optimizer == None:
optimizer = SPSA(maxiter=max_iter,
#blocking=True,
#callback=store_intermediate_result,
learning_rate=0.3,
perturbation=0.1
) # recommended from qiskit (first iteraction takes quite long)
# to reduce time figure out optimal learning rate and perturbation in advance
start_time = time.time()
ret = optimizer.optimize(
num_vars=len(thetas),
objective_function=(lambda thetas: cost_function(thetas, L, num_trash, ising_params, shots, vqe, param_encoding, x, meas_fitter=meas_fitter)),
initial_point=thetas
)
print("Time: {:.5f} sec".format(time.time()-start_time))
print(ret)
return ret[0], values, accepted
def run_inference(thetas, shots=1000, L=5, meas_fitter=None):
points = 50
J = -1.0
x,y = np.meshgrid(gx_vals, gz_vals)
cost = np.zeros((len(gx_vals) * len(gz_vals)))
Smags = np.zeros((len(gx_vals) * len(gz_vals)))
shots = 1000
for i,p in enumerate(list(zip(gxs, gzs))):
t0 = datetime.datetime.now()
cost[i] = cost_function_single(thetas, L, num_trash, i, shots=shots, meas_fitter=meas_fitter)
if not i%verbose:
print(f"{i+1} / {len(opt_params)}, gx = {gx:.2f}, gz = {gz:.2f}, time : {verbose*(datetime.datetime.now() - t0)}")
cost = cost.reshape((len(gx_vals), len(gz_vals)))
return cost
phys_params = [0]
filename_simulated_thetas = 'data/ibmq_antiferro-1D-load_simu_thetas-loss-cost_run2.npz' #this was actually a mistake, should have been the run1 but seemed to have worked anyways
thetas_guess = np.load(filename_simulated_thetas, allow_pickle=True)["thetas_mitigated"]
# Training
to = datetime.datetime.now()
thetas_opt_mitigated, loss, accepted = optimize(phys_params, thetas = thetas_guess, max_iter=30, L=5,meas_fitter=meas_fitter) #, pick_optimizer="adam")
plt.plot(loss)
print(datetime.datetime.now() - t0)
# Inference
cost_mitigated = np.zeros((len(gx_vals)))
shots = 1000
for i,p in enumerate(list(zip(gxs, gzs))):
t0 = datetime.datetime.now()
cost_mitigated[i] = cost_function_single(thetas_opt_mitigated, L, num_trash, i, shots=shots, meas_fitter=meas_fitter) # np.random.uniform(0, 2*np.pi, 2*L+2) random parameters to check if training "does" something - result was: with random paremters just get noise, so yes, it "does" something
if not i%verbose:
print(f"{i+1} / {len(opt_params)}, gx = {p[0]:.2f}, gz = {p[1]:.2f}, time : {verbose*(datetime.datetime.now() - t0)}")
np.savez("data/" + name + "_thetas-loss-cost_run1",
cost_mitigated=cost_mitigated, thetas_mitigated=thetas_opt_mitigated,
)
fig, ax = plt.subplots(ncols=1,figsize=(6,5))
ax.set_title("Mitigated results")
cost = np.load("data/" + name + "_thetas-loss-cost_run1.npz", allow_pickle=True)["cost_mitigated"]
ax.plot(gx_list, cost,".:", label="raw output")
ax.plot(gx_list, Qmags,"x--", label="$\\langle \\sigma_z \\rangle$ noisy sim")
ax.plot(gx_list, Qmags_executed,"x--", label="$\\langle \\sigma_z \\rangle$ ibmq_bogota")
ax.set_xscale("log")
for p in phys_params:
ax.plot(gx_list[p],cost[p],"o",alpha=0.3,color="magenta")
ax.legend()
np.savez("data/" + name + "_thetas-loss-cost_run2",
cost_mitigated=cost_mitigated, thetas_mitigated=thetas_opt_mitigated,
)
filename_simulated_thetas = 'data/ibmq_antiferro-1D-load_bogota-optimize_thetas-loss-cost_run2.npz'
fig, ax = plt.subplots(ncols=1,figsize=(6,5))
ax.set_title("Mitigated results")
cost = np.load("data/" + name + "_thetas-loss-cost_run2.npz", allow_pickle=True)["cost_mitigated"]
ax.plot(gx_list, cost,".:", label="raw output")
ax.plot(gx_list, Qmags,"x--", color="tab:orange", label="Qmag")
ax.set_xscale("log")
for p in phys_params:
ax.plot(gx_list[p],cost[p],"o",alpha=0.3,color="magenta")
|
https://github.com/Qottmann/Quantum-anomaly-detection
|
Qottmann
|
import sys
sys.path.insert(1, '..') # sets the path to the parent folder, where the notebook was originally executed
import time
import datetime
import numpy as np
from matplotlib import pyplot as plt
import qiskit
from qiskit import *
from qiskit.opflow import X,Z,I
from qiskit.opflow.state_fns import StateFn, CircuitStateFn
from qiskit.providers.aer import StatevectorSimulator, AerSimulator
from qiskit.algorithms import VQE
from qiskit.algorithms.optimizers import COBYLA, SLSQP, SPSA
from qiskit.opflow import CircuitSampler
from qiskit.ignis.mitigation.measurement import CompleteMeasFitter # you will need to pip install qiskit-ignis
from qiskit.ignis.mitigation.measurement import complete_meas_cal
from scipy import sparse
import scipy.sparse.linalg.eigen.arpack as arp
from modules.utils import *
anti = -1
L = 5
num_trash = 2
name = "ibmq_antiferro-1D-load_bogota-optimize" # remove test at the end when running on a real device
filename = "data/noisy_VQE_maxiter-500_Ising_L5_anti_-1_20" #"data/noisy_VQE_maxiter-100_Ising_L5_anti_-1_20_recycle"
print("filename: ", filename, "notebook name: ", name)
# where to get the simulated thetas values from? needs to contain a thetas_mitigated array
filename_simulated_thetas = 'data/ibmq_antiferro-1D-load_simu_thetas-loss-cost_run2.npz'
load = False
recompute = False # whether or not to recompute Magnetization, makes sense on device
IBMQ.load_account() # this then automatically loads your saved account
provider = IBMQ.get_provider(hub='ibm-q-research')
device = provider.backend.ibmq_bogota
print(device)
backend = device
#backend = qiskit.providers.aer.AerSimulator.from_backend(device)
coupling_map = device.configuration().coupling_map
noise_model = qiskit.providers.aer.noise.NoiseModel.from_backend(device)
basis_gates = noise_model.basis_gates
#aqua_globals.random_seed = seed
qi = qiskit.utils.QuantumInstance(backend=backend, # , seed_simulator=seed, seed_transpiler=seed
coupling_map=coupling_map, #, noise_model=noise_model,
measurement_error_mitigation_cls= CompleteMeasFitter,
cals_matrix_refresh_period=30 #How often to refresh the calibration matrix in measurement mitigation. in minutes
)
# Very important, at the moment poorly coded so it needs to come back to this instance all the time
ansatz = qiskit.circuit.library.TwoLocal(L,rotation_blocks="ry", entanglement_blocks='cz', entanglement="sca", reps=1)
ansatz.draw("mpl")
ansatz = qiskit.transpile(ansatz, backend)
ansatz.draw("mpl")
L = 5
num_trash = 2
anti = -1
VQE_params = np.load(filename + ".npz", allow_pickle=True)
pick = np.arange(0,len(VQE_params['gx_list']),3)
gx_list = VQE_params['gx_list'][pick]
gz_list = VQE_params['gz_list'][pick]
opt_params = VQE_params['opt_params'][pick]
Qmags = VQE_params["Qmag"][pick]
Qen = VQE_params["Qen"][pick]
Sen = VQE_params["Sen"][pick]
Smags = VQE_params["Smag"][pick]
gx_vals = np.unique(gx_list)
gz_vals = np.unique(gz_list)
if load:
temp = np.load("data/" + name + "executed_mags-Es.npz",allow_pickle=True)
Qmags = temp["Qmags"]
Qen = temp["Qen"]
Sen = temp["Sen"]
Smags = temp["Smags"]
verbose=1
if recompute:
mag = QMag(L,anti) #magnetization operator (Qiskit)
Smag = Mag(L,anti) #magnetization operator (numpy)
Qen_executed=np.zeros(len(opt_params), dtype="complex")
Qmags_executed=np.zeros(len(opt_params), dtype="complex")
for j in range(len(opt_params)):
t0 = datetime.datetime.now()
gx = gx_list[j]
gz = gz_list[j]
H = QHIsing(L, anti, np.float32(gx), np.float32(gz)) # build Hamiltonian Op
state = ansatz.assign_parameters(opt_params[j])
meas_outcome = ~StateFn(mag) @ StateFn(state)
Qmags_executed[j] = CircuitSampler(qi).convert(meas_outcome).eval() #https://quantumcomputing.stackexchange.com/questions/12080/evaluating-expectation-values-of-operators-in-qiskit
#e_outcome = ~StateFn(H) @ StateFn(state)
#Qen_executed[j] = CircuitSampler(qi).convert(e_outcome).eval()
init_state, E, ham = ising_groundstate(L, anti, np.float64(gx), np.float64(gz))
Sen[j] = E
Smags[j] = init_state.T.conj()@Smag@init_state #Magnetization with Numpy results
print(f"{j+1} / {len(opt_params)}, gx = {gx:.2f}, gz = {gz:.2f}, time : {(datetime.datetime.now() - t0)}")
np.savez("data/" + name + "executed_mags-Es.npz",Qmags=Qmags_executed, Qen=Qen_executed, Sen=Sen, Smags=Smags) # for large parameter space takes quite a while because of the exact diagonalization
fig, axs = plt.subplots(ncols=2, figsize=(10,5))
ax = axs[0]
ax.plot(gx_list, Qmags,"x--", label="noisy VQE simu")
ax.plot(gx_list, Smags,"x--", label="ED")
ax.set_xscale("log")
if recompute:
ax.plot(gx_list, Qmags_executed,"x--", label="IBMQ")
ax.legend()
##############################################################################
### II - Training ###########################################################
##############################################################################
# linear entangler (as in scales linearly with trash qubits)
def get_entangler_map(L, num_trash, i_permut=1, nums_trash=[1,2]):
result = []
nums_compressed = list(range(L))
for trashqubit in nums_trash:
nums_compressed.remove(trashqubit)
if nums_trash == None: #old way
nums_compressed = list(range(L))[:L-num_trash]
nums_trash = list(range(L))[-num_trash:]
#print(nums, nums_compressed, nums_trash)
# combine all trash qubits with themselves
for trash_q in nums_trash[:-1]:
result.append((trash_q+1,trash_q))
# combine each of the trash qubits with every n-th
repeated = list(nums_trash) * (L-num_trash) # repeat the list of trash indices cyclicly
for i in range(L-num_trash):
result.append((repeated[i_permut + i], nums_compressed[i]))
return result
def QAE_Ansatz(thetas, L, num_trash, insert_barriers=False, parametrized_gate = "ry", entangling_gate = "cz", nums_trash = [1,2]):
entanglement = [get_entangler_map(L,num_trash,i_permut) for i_permut in range(num_trash)]
if nums_trash == None:
nums_trash = list(range(L))[-num_trash:]
circ = qiskit.circuit.library.TwoLocal(L,
parametrized_gate,
entangling_gate,
entanglement,
reps=num_trash,
insert_barriers=insert_barriers,
skip_final_rotation_layer=True
).assign_parameters(thetas[:-num_trash])
if insert_barriers: circ.barrier()
for i in nums_trash:
circ.ry(thetas[i], i)
#circ.ry(circuit.Parameter(f'θ{i}'), L-i-1)
return circ
def prepare_circuit(thetas, L=6, num_trash=2, init_state=None, measurement=True, vqe=True):
qreg = QuantumRegister(L, 'q')
creg = ClassicalRegister(num_trash, 'c')
circ = QuantumCircuit(qreg, creg)
circ += QAE_Ansatz(thetas, L, num_trash, insert_barriers=False)#.assign_parameters(thetas) # difference to bind?
if measurement:
for i in range(num_trash):
circ.measure(qreg[L-i-1], creg[i])
if init_state is not None:
if vqe:
circ = ansatz.assign_parameters(init_state) + circ # needs to have ansatz defined somewhere in the script
else:
circ.initialize(init_state, qreg)
return circ
def feature_encoding(thetas, x):
""" thetas: parameters to be optimized, x: Ising model parameter (eg. field) """
new_thetas = []
thetas = thetas.reshape((-1,2))
for theta in thetas:
new_thetas.append(theta[0] * x + theta[1])
return new_thetas
def calibrate_circuit(L, num_trash,shots=1000):
qreg = QuantumRegister(L, 'q')
# obtain calibration matrix
qubit_list = [L-i-1 for i in range(num_trash)] # only need to calibrate the trash qubits
circlabel = f'mcal_{datetime.datetime.now()}'
meas_calibs, state_labels = complete_meas_cal(qubit_list=qubit_list, qr=qreg, circlabel=circlabel)
cal_job = backend.run(meas_calibs, shots=shots) #, noise_model=noise_model)
cal_results = cal_job.result()
meas_fitter = CompleteMeasFitter(cal_results, state_labels, circlabel=circlabel)
print(circlabel, meas_fitter.cal_matrix)
return meas_fitter
def run_circuit(thetas, L, num_trash, init_state, vqe=True, shots=100, meas_fitter = None):
circ = prepare_circuit(thetas, L, num_trash, init_state, vqe=vqe)
tcirc = qiskit.transpile(circ, backend)
# Execute the circuit
job_sim = backend.run(tcirc, shots=shots) # , seed_simulator=123, seed_transpiler=234 fix seed to make it reproducible
result = job_sim.result()
# Results without mitigation
counts = result.get_counts()
if meas_fitter != None:
# Get the filter object
meas_filter = meas_fitter.filter
# Results with mitigation
mitigated_results = meas_filter.apply(result)
counts = mitigated_results.get_counts(0)
return counts
meas_fitter = calibrate_circuit(L, num_trash)
phis = opt_params
# translate to Rikes naming
gxs = gx_list
gzs = gz_list
def cost_function_single(thetas, L, num_trash, p, shots=1000, vqe=True, param_encoding=False, x=0, meas_fitter=None):
""" Optimizes circuit """
if vqe:
init_state = phis[p]
else:
J, gx, gz = p
init_state, _ = ising_groundstate(L, J, gx, gz)
if param_encoding: thetas = feature_encoding(thetas, x)
out = run_circuit(thetas, L, num_trash, init_state, vqe=vqe, shots=shots, meas_fitter=meas_fitter)
cost = out.get('11', 0)*2 + out.get('01', 0) + out.get('10', 0)
return cost/shots
def cost_function(thetas, L, num_trash, ising_params, shots=1000, vqe=True, param_encoding=False, x=0, meas_fitter=None):
""" Optimizes circuit """
cost = 0.
n_samples = len(ising_params)
for i, p in enumerate(ising_params):
if param_encoding:
cost += cost_function_single(thetas, L, num_trash, p, shots, vqe, param_encoding, x[i], meas_fitter=meas_fitter)
else:
cost += cost_function_single(thetas, L, num_trash, p, shots, vqe, param_encoding, meas_fitter=meas_fitter)
return cost/n_samples
def optimize(ising_params, L=6, num_trash=2, thetas=None, shots=1000, max_iter=400, vqe=True, param_encoding=False, x=0, pick_optimizer = None,
meas_fitter=None):
if thetas is None:
n_params = (2*L+2)*2 if param_encoding else (2*L+2)
thetas = np.random.uniform(0, 2*np.pi, n_params) # initial parameters without feature encoding
print("Initial cost: {:.3f}".format(cost_function(thetas, L, num_trash, ising_params, shots, vqe, param_encoding, x)))
counts, values, accepted = [], [], []
def store_intermediate_result(eval_count, parameters, mean, std, ac):
# counts.append(eval_count)
values.append(mean)
accepted.append(ac)
# Initialize optimizer
if pick_optimizer == "cobyla":
optimizer = COBYLA(maxiter=max_iter, tol=0.0001)
if pick_optimizer == "adam" or pick_optimizer == "ADAM":
optimizer = qiskit.algorithms.optimizers.ADAM(maxiter=max_iter)
# optimizer = L_BFGS_B(maxfun=300, maxiter=max_iter)#, factr=10, iprint=- 1, epsilon=1e-08)
if pick_optimizer == "spsa" or pick_optimizer == None:
optimizer = SPSA(maxiter=max_iter,
#blocking=True,
callback=store_intermediate_result,
learning_rate=0.3,
perturbation=0.1
) # recommended from qiskit (first iteraction takes quite long)
# to reduce time figure out optimal learning rate and perturbation in advance
start_time = time.time()
ret = optimizer.optimize(
num_vars=len(thetas),
objective_function=(lambda thetas: cost_function(thetas, L, num_trash, ising_params, shots, vqe, param_encoding, x, meas_fitter=meas_fitter)),
initial_point=thetas
)
print("Time: {:.5f} sec".format(time.time()-start_time))
print(ret)
return ret[0], values, accepted
def run_inference(thetas, shots=1000, L=5, meas_fitter=None):
points = 50
J = -1.0
x,y = np.meshgrid(gx_vals, gz_vals)
cost = np.zeros((len(gx_vals) * len(gz_vals)))
Smags = np.zeros((len(gx_vals) * len(gz_vals)))
shots = 1000
for i,p in enumerate(list(zip(gxs, gzs))):
t0 = datetime.datetime.now()
cost[i] = cost_function_single(thetas, L, num_trash, i, shots=shots, meas_fitter=meas_fitter)
if not i%verbose:
print(f"{i+1} / {len(opt_params)}, gx = {gx:.2f}, gz = {gz:.2f}, time : {verbose*(datetime.datetime.now() - t0)}")
cost = cost.reshape((len(gx_vals), len(gz_vals)))
return cost
phys_params = [-1]
thetas_guess = np.load(filename_simulated_thetas, allow_pickle=True)["thetas_mitigated"]
# Training
thetas_opt_mitigated, loss, accepted = optimize(phys_params, thetas = thetas_guess, max_iter=30, L=5,meas_fitter=meas_fitter) #, pick_optimizer="adam")
plt.plot(loss)
# Inference
cost_mitigated = np.zeros((len(gx_vals)))
shots = 1000
for i,p in enumerate(list(zip(gxs, gzs))):
t0 = datetime.datetime.now()
cost_mitigated[i] = cost_function_single(thetas_opt_mitigated, L, num_trash, i, shots=shots, meas_fitter=meas_fitter) # np.random.uniform(0, 2*np.pi, 2*L+2) random parameters to check if training "does" something - result was: with random paremters just get noise, so yes, it "does" something
if not i%verbose:
print(f"{i+1} / {len(opt_params)}, gx = {p[0]:.2f}, gz = {p[1]:.2f}, time : {verbose*(datetime.datetime.now() - t0)}")
"data/" + name + "_thetas-loss-cost_run2"
np.savez("data/" + name + "_thetas-loss-cost_run2",
cost_mitigated=cost_mitigated, thetas_mitigated=thetas_opt_mitigated,
)
fig, ax = plt.subplots(ncols=1,figsize=(6,5))
ax.set_title("Mitigated results")
cost = np.load("data/" + name + "_thetas-loss-cost_run2.npz", allow_pickle=True)["cost_mitigated"]
ax.plot(gx_list, cost,".:", label="raw output")
ax.plot(gx_list, abs(Qmags),"x--", color="tab:orange", label="Qmag")
ax.set_xscale("log")
for p in phys_params:
ax.plot(gx_list[p],cost[p],"o",alpha=0.3,color="magenta")
|
https://github.com/Qottmann/Quantum-anomaly-detection
|
Qottmann
|
import time
import datetime
import numpy as np
from matplotlib import pyplot as plt
import qiskit
from qiskit import *
from qiskit.opflow import X,Z,I
from qiskit.opflow.state_fns import StateFn, CircuitStateFn
from qiskit.providers.aer import StatevectorSimulator, AerSimulator
from qiskit.algorithms import VQE
from qiskit.algorithms.optimizers import COBYLA, SLSQP, SPSA
from qiskit.opflow import CircuitSampler
from qiskit.ignis.mitigation.measurement import CompleteMeasFitter # you will need to pip install qiskit-ignis
from qiskit.ignis.mitigation.measurement import complete_meas_cal
from scipy import sparse
import scipy.sparse.linalg.eigen.arpack as arp
from modules.utils import *
anti = -1
L = 5
num_trash = 2
name = "ibmq_antiferro-1D-load_bogota-test"
filename = "data/noisy_VQE_maxiter-500_Ising_L5_anti_-1_20" #"data/noisy_VQE_maxiter-100_Ising_L5_anti_-1_20_recycle"
filename_simualted_thetas = 'data/ibmq_antiferro-1D-load_simu_thetas-loss-cost_run2.npz' # where to get the simulated thetas values from?
print("filename: ", filename, "notebook name: ", name)
load = False
recompute = False # whether or not to recompute Magnetization, makes sense on device
IBMQ.load_account() # this then automatically loads your saved account
provider = IBMQ.get_provider(hub='ibm-q-research')
device = provider.backend.ibmq_bogota
print(device)
#backend = device
backend = qiskit.providers.aer.AerSimulator.from_backend(device)
coupling_map = device.configuration().coupling_map
noise_model = qiskit.providers.aer.noise.NoiseModel.from_backend(device)
basis_gates = noise_model.basis_gates
#aqua_globals.random_seed = seed
qi = qiskit.utils.QuantumInstance(backend=backend, # , seed_simulator=seed, seed_transpiler=seed
coupling_map=coupling_map, noise_model=noise_model,
measurement_error_mitigation_cls= CompleteMeasFitter,
cals_matrix_refresh_period=30 #How often to refresh the calibration matrix in measurement mitigation. in minutes
)
backend._cached_basis_gates
# Very important, at the moment poorly coded so it needs to come back to this instance all the time
ansatz = qiskit.circuit.library.TwoLocal(L,rotation_blocks="ry", entanglement_blocks='cz', entanglement="sca", reps=1)
ansatz.draw("mpl")
ansatz = qiskit.transpile(ansatz, backend)
ansatz.draw("mpl")
L = 5
num_trash = 2
anti = -1
VQE_params = np.load(filename + ".npz", allow_pickle=True)
gx_list = VQE_params['gx_list']
gz_list = VQE_params['gz_list']
opt_params = VQE_params['opt_params']
Qmags = VQE_params["Qmag"]
Qen = VQE_params["Qen"]
Sen = VQE_params["Sen"]
Smags = VQE_params["Smag"]
gx_vals = np.unique(gx_list)
gz_vals = np.unique(gz_list)
if load:
temp = np.load("data/" + name + "executed_mags-Es.npz",allow_pickle=True)
Qmags = temp["Qmags"]
Qen = temp["Qen"]
Sen = temp["Sen"]
Smags = temp["Smags"]
verbose=1
if recompute:
mag = QMag(L,anti) #magnetization operator (Qiskit)
Smag = Mag(L,anti) #magnetization operator (numpy)
Qen_executed=np.zeros(len(opt_params), dtype="complex")
Qmags_executed=np.zeros(len(opt_params), dtype="complex")
for j in range(len(opt_params)):
t0 = datetime.datetime.now()
gx = gx_list[j]
gz = gz_list[j]
H = QHIsing(L, anti, np.float32(gx), np.float32(gz)) # build Hamiltonian Op
state = ansatz.assign_parameters(opt_params[j])
meas_outcome = ~StateFn(mag) @ StateFn(state)
Qmags_executed[j] = CircuitSampler(qi).convert(meas_outcome).eval() #https://quantumcomputing.stackexchange.com/questions/12080/evaluating-expectation-values-of-operators-in-qiskit
#e_outcome = ~StateFn(H) @ StateFn(state)
#Qen_executed[j] = CircuitSampler(qi).convert(e_outcome).eval()
init_state, E, ham = ising_groundstate(L, anti, np.float64(gx), np.float64(gz))
Sen[j] = E
Smags[j] = init_state.T.conj()@Smag@init_state #Magnetization with Numpy results
print(f"{j+1} / {len(opt_params)}, gx = {gx:.2f}, gz = {gz:.2f}, time : {(datetime.datetime.now() - t0)}")
np.savez("data/" + name + "executed_mags-Es.npz",Qmags=Qmags_executed, Qen=Qen_executed, Sen=Sen, Smags=Smags) # for large parameter space takes quite a while because of the exact diagonalization
fig, axs = plt.subplots(ncols=2, figsize=(10,5))
ax = axs[0]
ax.plot(gx_list, Qmags,"x--", label="noisy VQE simu")
ax.plot(gx_list, Smags,"x--", label="ED")
ax.set_xscale("log")
if recompute:
ax.plot(gx_list, Qmags_executed,"x--", label="IBMQ")
ax.legend()
##############################################################################
### II - Training ###########################################################
##############################################################################
# linear entangler (as in scales linearly with trash qubits)
def get_entangler_map(L, num_trash, i_permut=1, nums_trash=[1,2]):
result = []
nums_compressed = list(range(L))
for trashqubit in nums_trash:
nums_compressed.remove(trashqubit)
if nums_trash == None: #old way
nums_compressed = list(range(L))[:L-num_trash]
nums_trash = list(range(L))[-num_trash:]
#print(nums, nums_compressed, nums_trash)
# combine all trash qubits with themselves
for trash_q in nums_trash[:-1]:
result.append((trash_q+1,trash_q))
# combine each of the trash qubits with every n-th
repeated = list(nums_trash) * (L-num_trash) # repeat the list of trash indices cyclicly
for i in range(L-num_trash):
result.append((repeated[i_permut + i], nums_compressed[i]))
return result
def QAE_Ansatz(thetas, L, num_trash, insert_barriers=False, parametrized_gate = "ry", entangling_gate = "cz", nums_trash = [1,2]):
entanglement = [get_entangler_map(L,num_trash,i_permut) for i_permut in range(num_trash)]
if nums_trash == None:
nums_trash = list(range(L))[-num_trash:]
circ = qiskit.circuit.library.TwoLocal(L,
parametrized_gate,
entangling_gate,
entanglement,
reps=num_trash,
insert_barriers=insert_barriers,
skip_final_rotation_layer=True
).assign_parameters(thetas[:-num_trash])
if insert_barriers: circ.barrier()
for i in nums_trash:
circ.ry(thetas[i], i)
#circ.ry(circuit.Parameter(f'θ{i}'), L-i-1)
return circ
def prepare_circuit(thetas, L=6, num_trash=2, init_state=None, measurement=True, vqe=True):
qreg = QuantumRegister(L, 'q')
creg = ClassicalRegister(num_trash, 'c')
circ = QuantumCircuit(qreg, creg)
circ += QAE_Ansatz(thetas, L, num_trash, insert_barriers=False)#.assign_parameters(thetas) # difference to bind?
if measurement:
for i in range(num_trash):
circ.measure(qreg[L-i-1], creg[i])
if init_state is not None:
if vqe:
circ = ansatz.assign_parameters(init_state) + circ # needs to have ansatz defined somewhere in the script
else:
circ.initialize(init_state, qreg)
return circ
def feature_encoding(thetas, x):
""" thetas: parameters to be optimized, x: Ising model parameter (eg. field) """
new_thetas = []
thetas = thetas.reshape((-1,2))
for theta in thetas:
new_thetas.append(theta[0] * x + theta[1])
return new_thetas
def calibrate_circuit(L, num_trash,shots=1000):
qreg = QuantumRegister(L, 'q')
# obtain calibration matrix
qubit_list = [L-i-1 for i in range(num_trash)] # only need to calibrate the trash qubits
circlabel = f'mcal_{datetime.datetime.now()}'
meas_calibs, state_labels = complete_meas_cal(qubit_list=qubit_list, qr=qreg, circlabel=circlabel)
cal_job = backend.run(meas_calibs, shots=shots, noise_model=noise_model)
cal_results = cal_job.result()
meas_fitter = CompleteMeasFitter(cal_results, state_labels, circlabel=circlabel)
print(circlabel, meas_fitter.cal_matrix)
return meas_fitter
def run_circuit(thetas, L, num_trash, init_state, vqe=True, shots=100, meas_fitter = None):
circ = prepare_circuit(thetas, L, num_trash, init_state, vqe=vqe)
tcirc = qiskit.transpile(circ, backend)
# Execute the circuit
job_sim = backend.run(tcirc, shots=shots) # , seed_simulator=123, seed_transpiler=234 fix seed to make it reproducible
result = job_sim.result()
# Results without mitigation
counts = result.get_counts()
if meas_fitter != None:
# Get the filter object
meas_filter = meas_fitter.filter
# Results with mitigation
mitigated_results = meas_filter.apply(result)
counts = mitigated_results.get_counts(0)
return counts
meas_fitter = calibrate_circuit(L, num_trash)
phis = opt_params
# translate to Rikes naming
gxs = gx_list
gzs = gz_list
def cost_function_single(thetas, L, num_trash, p, shots=1000, vqe=True, param_encoding=False, x=0, meas_fitter=None):
""" Optimizes circuit """
if vqe:
init_state = phis[p]
else:
J, gx, gz = p
init_state, _ = ising_groundstate(L, J, gx, gz)
if param_encoding: thetas = feature_encoding(thetas, x)
out = run_circuit(thetas, L, num_trash, init_state, vqe=vqe, shots=shots, meas_fitter=meas_fitter)
cost = out.get('11', 0)*2 + out.get('01', 0) + out.get('10', 0)
return cost/shots
def cost_function(thetas, L, num_trash, ising_params, shots=1000, vqe=True, param_encoding=False, x=0, meas_fitter=None):
""" Optimizes circuit """
cost = 0.
n_samples = len(ising_params)
for i, p in enumerate(ising_params):
if param_encoding:
cost += cost_function_single(thetas, L, num_trash, p, shots, vqe, param_encoding, x[i], meas_fitter=meas_fitter)
else:
cost += cost_function_single(thetas, L, num_trash, p, shots, vqe, param_encoding, meas_fitter=meas_fitter)
return cost/n_samples
def optimize(ising_params, L=6, num_trash=2, thetas=None, shots=1000, max_iter=400, vqe=True, param_encoding=False, x=0, pick_optimizer = None,
meas_fitter=None):
if thetas is None:
n_params = (2*L+2)*2 if param_encoding else (2*L+2)
thetas = np.random.uniform(0, 2*np.pi, n_params) # initial parameters without feature encoding
print("Initial cost: {:.3f}".format(cost_function(thetas, L, num_trash, ising_params, shots, vqe, param_encoding, x)))
counts, values, accepted = [], [], []
def store_intermediate_result(eval_count, parameters, mean, std, ac):
# counts.append(eval_count)
values.append(mean)
accepted.append(ac)
# Initialize optimizer
if pick_optimizer == "cobyla":
optimizer = COBYLA(maxiter=max_iter, tol=0.0001)
if pick_optimizer == "adam" or pick_optimizer == "ADAM":
optimizer = qiskit.algorithms.optimizers.ADAM(maxiter=max_iter)
# optimizer = L_BFGS_B(maxfun=300, maxiter=max_iter)#, factr=10, iprint=- 1, epsilon=1e-08)
if pick_optimizer == "spsa" or pick_optimizer == None:
optimizer = SPSA(maxiter=max_iter,
#blocking=True,
callback=store_intermediate_result,
learning_rate=0.3,
perturbation=0.1
) # recommended from qiskit (first iteraction takes quite long)
# to reduce time figure out optimal learning rate and perturbation in advance
start_time = time.time()
ret = optimizer.optimize(
num_vars=len(thetas),
objective_function=(lambda thetas: cost_function(thetas, L, num_trash, ising_params, shots, vqe, param_encoding, x, meas_fitter=meas_fitter)),
initial_point=thetas
)
print("Time: {:.5f} sec".format(time.time()-start_time))
print(ret)
return ret[0], values, accepted
def run_inference(thetas, shots=1000, L=5, meas_fitter=None):
points = 50
J = -1.0
x,y = np.meshgrid(gx_vals, gz_vals)
cost = np.zeros((len(gx_vals) * len(gz_vals)))
Smags = np.zeros((len(gx_vals) * len(gz_vals)))
shots = 1000
for i,p in enumerate(list(zip(gxs, gzs))):
t0 = datetime.datetime.now()
cost[i] = cost_function_single(thetas, L, num_trash, i, shots=shots, meas_fitter=meas_fitter)
if not i%verbose:
print(f"{i+1} / {len(opt_params)}, gx = {gx:.2f}, gz = {gz:.2f}, time : {verbose*(datetime.datetime.now() - t0)}")
cost = cost.reshape((len(gx_vals), len(gz_vals)))
return cost
thetas_guess = np.load(filename_simualted_thetas, allow_pickle=True)["thetas_mitigated"]
# Training
phys_params = [-1]
thetas_opt_mitigated, loss, accepted = optimize(phys_params, thetas = thetas_guess, max_iter=30, L=5,meas_fitter=meas_fitter) #, pick_optimizer="adam")
plt.plot(loss)
# Inference
cost_mitigated = np.zeros((len(gx_vals)))
shots = 1000
for i,p in enumerate(list(zip(gxs, gzs))):
t0 = datetime.datetime.now()
cost_mitigated[i] = cost_function_single(thetas_opt_mitigated, L, num_trash, i, shots=shots, meas_fitter=meas_fitter) # np.random.uniform(0, 2*np.pi, 2*L+2) random parameters to check if training "does" something - result was: with random paremters just get noise, so yes, it "does" something
if not i%verbose:
print(f"{i+1} / {len(opt_params)}, gx = {p[0]:.2f}, gz = {p[1]:.2f}, time : {verbose*(datetime.datetime.now() - t0)}")
np.savez("data/" + name + "_thetas-loss-cost_run2",
cost_mitigated=cost_mitigated, thetas_mitigated=thetas_opt_mitigated,
)
fig, ax = plt.subplots(ncols=1,figsize=(6,5))
ax.set_title("Mitigated results")
cost = np.load("data/" + name + "_thetas-loss-cost_run2.npz", allow_pickle=True)["cost_mitigated"]
ax.plot(gx_list, cost,".:", label="raw output")
ax.plot(gx_list, Qmags,"x--", color="tab:orange", label="Qmag")
ax.set_xscale("log")
for p in phys_params:
ax.plot(gx_list[p],cost[p],"o",alpha=0.3,color="magenta")
|
https://github.com/Qottmann/Quantum-anomaly-detection
|
Qottmann
|
import sys
sys.path.insert(1, '..') # sets the path to the parent folder, where the notebook was originally executed
import time
import datetime
import numpy as np
from matplotlib import pyplot as plt
import qiskit
from qiskit import *
from qiskit.opflow import X,Z,I
from qiskit.opflow.state_fns import StateFn, CircuitStateFn
from qiskit.providers.aer import StatevectorSimulator, AerSimulator
from qiskit.algorithms import VQE
from qiskit.algorithms.optimizers import COBYLA, SLSQP, SPSA
from qiskit.opflow import CircuitSampler
from qiskit.ignis.mitigation.measurement import CompleteMeasFitter # you will need to pip install qiskit-ignis
from qiskit.ignis.mitigation.measurement import complete_meas_cal
from scipy import sparse
import scipy.sparse.linalg.eigen.arpack as arp
from modules.utils import *
anti = -1
L = 5
num_trash = 2
name = "ibmq_antiferro-1D-load_bogota-optimize" # remove test at the end when running on a real device
filename = "data/noisy_VQE_maxiter-500_Ising_L5_anti_-1_20" #"data/noisy_VQE_maxiter-100_Ising_L5_anti_-1_20_recycle"
print("filename: ", filename, "notebook name: ", name)
# where to get the simulated thetas values from? needs to contain a thetas_mitigated array
filename_simulated_thetas = 'data/ibmq_antiferro-1D-load_simu_thetas-loss-cost_run2.npz'
load = False
recompute = True # whether or not to recompute Magnetization, makes sense on device
IBMQ.load_account() # this then automatically loads your saved account
provider = IBMQ.get_provider(hub='ibm-q-research')
device = provider.backend.ibmq_rome
print(device)
backend = device
#backend = qiskit.providers.aer.AerSimulator.from_backend(device)
coupling_map = device.configuration().coupling_map
noise_model = qiskit.providers.aer.noise.NoiseModel.from_backend(device)
basis_gates = noise_model.basis_gates
#aqua_globals.random_seed = seed
qi = qiskit.utils.QuantumInstance(backend=backend, # , seed_simulator=seed, seed_transpiler=seed
coupling_map=coupling_map, #, noise_model=noise_model,
measurement_error_mitigation_cls= CompleteMeasFitter,
cals_matrix_refresh_period=30 #How often to refresh the calibration matrix in measurement mitigation. in minutes
)
# Very important, at the moment poorly coded so it needs to come back to this instance all the time
ansatz = qiskit.circuit.library.TwoLocal(L,rotation_blocks="ry", entanglement_blocks='cz', entanglement="sca", reps=1)
#ansatz.draw("mpl")
ansatz = qiskit.transpile(ansatz, backend)
#ansatz.draw("mpl")
L = 5
num_trash = 2
anti = -1
VQE_params = np.load(filename + ".npz", allow_pickle=True)
pick = np.arange(0,len(VQE_params['gx_list']),3)
gx_list = VQE_params['gx_list'][pick]
gz_list = VQE_params['gz_list'][pick]
opt_params = VQE_params['opt_params'][pick]
Qmags = VQE_params["Qmag"][pick]
Qen = VQE_params["Qen"][pick]
Sen = VQE_params["Sen"][pick]
Smags = VQE_params["Smag"][pick]
gx_vals = np.unique(gx_list)
gz_vals = np.unique(gz_list)
if load:
temp = np.load("data/" + name + "executed_mags-Es.npz",allow_pickle=True)
Qmags = temp["Qmags"]
Qen = temp["Qen"]
Sen = temp["Sen"]
Smags = temp["Smags"]
verbose=1
if recompute:
mag = QMag(L,anti) #magnetization operator (Qiskit)
Smag = Mag(L,anti) #magnetization operator (numpy)
Qen_executed=np.zeros(len(opt_params), dtype="complex")
Qmags_executed=np.zeros(len(opt_params), dtype="complex")
for j in range(len(opt_params)):
t0 = datetime.datetime.now()
gx = gx_list[j]
gz = gz_list[j]
H = QHIsing(L, anti, np.float32(gx), np.float32(gz)) # build Hamiltonian Op
state = ansatz.assign_parameters(opt_params[j])
meas_outcome = ~StateFn(mag) @ StateFn(state)
Qmags_executed[j] = CircuitSampler(qi).convert(meas_outcome).eval() #https://quantumcomputing.stackexchange.com/questions/12080/evaluating-expectation-values-of-operators-in-qiskit
#e_outcome = ~StateFn(H) @ StateFn(state)
#Qen_executed[j] = CircuitSampler(qi).convert(e_outcome).eval()
init_state, E, ham = ising_groundstate(L, anti, np.float64(gx), np.float64(gz))
Sen[j] = E
Smags[j] = init_state.T.conj()@Smag@init_state #Magnetization with Numpy results
print(f"{j+1} / {len(opt_params)}, gx = {gx:.2f}, gz = {gz:.2f}, time : {(datetime.datetime.now() - t0)}")
np.savez("data/" + name + "executed_mags-Es.npz",Qmags=Qmags_executed, Qen=Qen_executed, Sen=Sen, Smags=Smags) # for large parameter space takes quite a while because of the exact diagonalization
temp = np.load("data/ibmq_antiferro-1D-load_bogota-optimize-20pointsexecuted_mags-Es.npz",allow_pickle=True)
Qmags_executed_bogota = temp["Qmags"]
fig, ax = plt.subplots(ncols=1, figsize=(6,5))
ax.plot(gx_list, Qmags,"x--", label="noisy rome simu")
ax.plot(gx_list, Smags,"x--", label="ED")
ax.plot(gx_list, Qmags_executed_bogota[::3],"x--", label="ibmq_bogota")
if recompute:
ax.plot(gx_list, Qmags_executed,"x--", label="ibmq_rome")
ax.legend()
ax.set_xscale("log")
plt.tight_layout()
plt.savefig("plots/temp_compare-bogota-rome_vqe-vals-from-noisy-rome-simulation.png")
##############################################################################
### II - Training ###########################################################
##############################################################################
# linear entangler (as in scales linearly with trash qubits)
def get_entangler_map(L, num_trash, i_permut=1, nums_trash=[1,2]):
result = []
nums_compressed = list(range(L))
for trashqubit in nums_trash:
nums_compressed.remove(trashqubit)
if nums_trash == None: #old way
nums_compressed = list(range(L))[:L-num_trash]
nums_trash = list(range(L))[-num_trash:]
#print(nums, nums_compressed, nums_trash)
# combine all trash qubits with themselves
for trash_q in nums_trash[:-1]:
result.append((trash_q+1,trash_q))
# combine each of the trash qubits with every n-th
repeated = list(nums_trash) * (L-num_trash) # repeat the list of trash indices cyclicly
for i in range(L-num_trash):
result.append((repeated[i_permut + i], nums_compressed[i]))
return result
def QAE_Ansatz(thetas, L, num_trash, insert_barriers=False, parametrized_gate = "ry", entangling_gate = "cz", nums_trash = [1,2]):
entanglement = [get_entangler_map(L,num_trash,i_permut) for i_permut in range(num_trash)]
if nums_trash == None:
nums_trash = list(range(L))[-num_trash:]
circ = qiskit.circuit.library.TwoLocal(L,
parametrized_gate,
entangling_gate,
entanglement,
reps=num_trash,
insert_barriers=insert_barriers,
skip_final_rotation_layer=True
).assign_parameters(thetas[:-num_trash])
if insert_barriers: circ.barrier()
for i in nums_trash:
circ.ry(thetas[i], i)
#circ.ry(circuit.Parameter(f'θ{i}'), L-i-1)
return circ
def prepare_circuit(thetas, L=6, num_trash=2, init_state=None, measurement=True, vqe=True):
qreg = QuantumRegister(L, 'q')
creg = ClassicalRegister(num_trash, 'c')
circ = QuantumCircuit(qreg, creg)
circ += QAE_Ansatz(thetas, L, num_trash, insert_barriers=False)#.assign_parameters(thetas) # difference to bind?
if measurement:
for i in range(num_trash):
circ.measure(qreg[L-i-1], creg[i])
if init_state is not None:
if vqe:
circ = ansatz.assign_parameters(init_state) + circ # needs to have ansatz defined somewhere in the script
else:
circ.initialize(init_state, qreg)
return circ
def feature_encoding(thetas, x):
""" thetas: parameters to be optimized, x: Ising model parameter (eg. field) """
new_thetas = []
thetas = thetas.reshape((-1,2))
for theta in thetas:
new_thetas.append(theta[0] * x + theta[1])
return new_thetas
def calibrate_circuit(L, num_trash,shots=1000):
qreg = QuantumRegister(L, 'q')
# obtain calibration matrix
qubit_list = [L-i-1 for i in range(num_trash)] # only need to calibrate the trash qubits
circlabel = f'mcal_{datetime.datetime.now()}'
meas_calibs, state_labels = complete_meas_cal(qubit_list=qubit_list, qr=qreg, circlabel=circlabel)
cal_job = backend.run(meas_calibs, shots=shots) #, noise_model=noise_model)
cal_results = cal_job.result()
meas_fitter = CompleteMeasFitter(cal_results, state_labels, circlabel=circlabel)
print(circlabel, meas_fitter.cal_matrix)
return meas_fitter
def run_circuit(thetas, L, num_trash, init_state, vqe=True, shots=100, meas_fitter = None):
circ = prepare_circuit(thetas, L, num_trash, init_state, vqe=vqe)
tcirc = qiskit.transpile(circ, backend)
# Execute the circuit
job_sim = backend.run(tcirc, shots=shots) # , seed_simulator=123, seed_transpiler=234 fix seed to make it reproducible
result = job_sim.result()
# Results without mitigation
counts = result.get_counts()
if meas_fitter != None:
# Get the filter object
meas_filter = meas_fitter.filter
# Results with mitigation
mitigated_results = meas_filter.apply(result)
counts = mitigated_results.get_counts(0)
return counts
meas_fitter = calibrate_circuit(L, num_trash)
phis = opt_params
# translate to Rikes naming
gxs = gx_list
gzs = gz_list
def cost_function_single(thetas, L, num_trash, p, shots=1000, vqe=True, param_encoding=False, x=0, meas_fitter=None):
""" Optimizes circuit """
if vqe:
init_state = phis[p]
else:
J, gx, gz = p
init_state, _ = ising_groundstate(L, J, gx, gz)
if param_encoding: thetas = feature_encoding(thetas, x)
out = run_circuit(thetas, L, num_trash, init_state, vqe=vqe, shots=shots, meas_fitter=meas_fitter)
cost = out.get('11', 0)*2 + out.get('01', 0) + out.get('10', 0)
return cost/shots
def cost_function(thetas, L, num_trash, ising_params, shots=1000, vqe=True, param_encoding=False, x=0, meas_fitter=None):
""" Optimizes circuit """
cost = 0.
n_samples = len(ising_params)
for i, p in enumerate(ising_params):
if param_encoding:
cost += cost_function_single(thetas, L, num_trash, p, shots, vqe, param_encoding, x[i], meas_fitter=meas_fitter)
else:
cost += cost_function_single(thetas, L, num_trash, p, shots, vqe, param_encoding, meas_fitter=meas_fitter)
return cost/n_samples
def optimize(ising_params, L=6, num_trash=2, thetas=None, shots=1000, max_iter=400, vqe=True, param_encoding=False, x=0, pick_optimizer = None,
meas_fitter=None):
if thetas is None:
n_params = (2*L+2)*2 if param_encoding else (2*L+2)
thetas = np.random.uniform(0, 2*np.pi, n_params) # initial parameters without feature encoding
print("Initial cost: {:.3f}".format(cost_function(thetas, L, num_trash, ising_params, shots, vqe, param_encoding, x)))
counts, values, accepted = [], [], []
def store_intermediate_result(eval_count, parameters, mean, std, ac):
# counts.append(eval_count)
values.append(mean)
accepted.append(ac)
# Initialize optimizer
if pick_optimizer == "cobyla":
optimizer = COBYLA(maxiter=max_iter, tol=0.0001)
if pick_optimizer == "adam" or pick_optimizer == "ADAM":
optimizer = qiskit.algorithms.optimizers.ADAM(maxiter=max_iter)
# optimizer = L_BFGS_B(maxfun=300, maxiter=max_iter)#, factr=10, iprint=- 1, epsilon=1e-08)
if pick_optimizer == "spsa" or pick_optimizer == None:
optimizer = SPSA(maxiter=max_iter,
#blocking=True,
callback=store_intermediate_result,
learning_rate=0.3,
perturbation=0.1
) # recommended from qiskit (first iteraction takes quite long)
# to reduce time figure out optimal learning rate and perturbation in advance
start_time = time.time()
ret = optimizer.optimize(
num_vars=len(thetas),
objective_function=(lambda thetas: cost_function(thetas, L, num_trash, ising_params, shots, vqe, param_encoding, x, meas_fitter=meas_fitter)),
initial_point=thetas
)
print("Time: {:.5f} sec".format(time.time()-start_time))
print(ret)
return ret[0], values, accepted
def run_inference(thetas, shots=1000, L=5, meas_fitter=None):
points = 50
J = -1.0
x,y = np.meshgrid(gx_vals, gz_vals)
cost = np.zeros((len(gx_vals) * len(gz_vals)))
Smags = np.zeros((len(gx_vals) * len(gz_vals)))
shots = 1000
for i,p in enumerate(list(zip(gxs, gzs))):
t0 = datetime.datetime.now()
cost[i] = cost_function_single(thetas, L, num_trash, i, shots=shots, meas_fitter=meas_fitter)
if not i%verbose:
print(f"{i+1} / {len(opt_params)}, gx = {gx:.2f}, gz = {gz:.2f}, time : {verbose*(datetime.datetime.now() - t0)}")
cost = cost.reshape((len(gx_vals), len(gz_vals)))
return cost
phys_params = [-1]
thetas_guess = np.load(filename_simulated_thetas, allow_pickle=True)["thetas_mitigated"]
# Training
thetas_opt_mitigated, loss, accepted = optimize(phys_params, thetas = thetas_guess, max_iter=30, L=5,meas_fitter=meas_fitter) #, pick_optimizer="adam")
plt.plot(loss)
# Inference
cost_mitigated = np.zeros((len(gx_vals)))
shots = 1000
for i,p in enumerate(list(zip(gxs, gzs))):
t0 = datetime.datetime.now()
cost_mitigated[i] = cost_function_single(thetas_opt_mitigated, L, num_trash, i, shots=shots, meas_fitter=meas_fitter) # np.random.uniform(0, 2*np.pi, 2*L+2) random parameters to check if training "does" something - result was: with random paremters just get noise, so yes, it "does" something
if not i%verbose:
print(f"{i+1} / {len(opt_params)}, gx = {p[0]:.2f}, gz = {p[1]:.2f}, time : {verbose*(datetime.datetime.now() - t0)}")
"data/" + name + "_thetas-loss-cost_run2"
np.savez("data/" + name + "_thetas-loss-cost_run2",
cost_mitigated=cost_mitigated, thetas_mitigated=thetas_opt_mitigated,
)
fig, ax = plt.subplots(ncols=1,figsize=(6,5))
ax.set_title("Mitigated results")
cost = np.load("data/" + name + "_thetas-loss-cost_run2.npz", allow_pickle=True)["cost_mitigated"]
ax.plot(gx_list, cost,".:", label="raw output")
ax.plot(gx_list, abs(Qmags),"x--", color="tab:orange", label="Qmag")
ax.set_xscale("log")
for p in phys_params:
ax.plot(gx_list[p],cost[p],"o",alpha=0.3,color="magenta")
|
https://github.com/Qottmann/Quantum-anomaly-detection
|
Qottmann
|
# -*- coding: utf-8 -*-
# This code is part of Qiskit.
#
# (C) Copyright IBM 2017, 2018.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""Utils for using with Qiskit unit tests."""
import logging
import os
import unittest
from enum import Enum
from qiskit import __path__ as qiskit_path
class Path(Enum):
"""Helper with paths commonly used during the tests."""
# Main SDK path: qiskit/
SDK = qiskit_path[0]
# test.python path: qiskit/test/python/
TEST = os.path.normpath(os.path.join(SDK, '..', 'test', 'python'))
# Examples path: examples/
EXAMPLES = os.path.normpath(os.path.join(SDK, '..', 'examples'))
# Schemas path: qiskit/schemas
SCHEMAS = os.path.normpath(os.path.join(SDK, 'schemas'))
# VCR cassettes path: qiskit/test/cassettes/
CASSETTES = os.path.normpath(os.path.join(TEST, '..', 'cassettes'))
# Sample QASMs path: qiskit/test/python/qasm
QASMS = os.path.normpath(os.path.join(TEST, 'qasm'))
def setup_test_logging(logger, log_level, filename):
"""Set logging to file and stdout for a logger.
Args:
logger (Logger): logger object to be updated.
log_level (str): logging level.
filename (str): name of the output file.
"""
# Set up formatter.
log_fmt = ('{}.%(funcName)s:%(levelname)s:%(asctime)s:'
' %(message)s'.format(logger.name))
formatter = logging.Formatter(log_fmt)
# Set up the file handler.
file_handler = logging.FileHandler(filename)
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
# Set the logging level from the environment variable, defaulting
# to INFO if it is not a valid level.
level = logging._nameToLevel.get(log_level, logging.INFO)
logger.setLevel(level)
class _AssertNoLogsContext(unittest.case._AssertLogsContext):
"""A context manager used to implement TestCase.assertNoLogs()."""
# pylint: disable=inconsistent-return-statements
def __exit__(self, exc_type, exc_value, tb):
"""
This is a modified version of TestCase._AssertLogsContext.__exit__(...)
"""
self.logger.handlers = self.old_handlers
self.logger.propagate = self.old_propagate
self.logger.setLevel(self.old_level)
if exc_type is not None:
# let unexpected exceptions pass through
return False
if self.watcher.records:
msg = 'logs of level {} or higher triggered on {}:\n'.format(
logging.getLevelName(self.level), self.logger.name)
for record in self.watcher.records:
msg += 'logger %s %s:%i: %s\n' % (record.name, record.pathname,
record.lineno,
record.getMessage())
self._raiseFailure(msg)
|
https://github.com/Qottmann/Quantum-anomaly-detection
|
Qottmann
|
import time
import datetime
import numpy as np
from matplotlib import pyplot as plt
import qiskit
from qiskit import *
from qiskit.opflow import X,Z,I
from qiskit.opflow.state_fns import StateFn, CircuitStateFn
from qiskit.providers.aer import StatevectorSimulator, AerSimulator
from qiskit.algorithms import VQE
from qiskit.algorithms.optimizers import COBYLA, SLSQP, SPSA
from scipy import sparse
import scipy.sparse.linalg.eigen.arpack as arp
from modules.utils import *
gz = 0
anti = 1
gx = 1e-1
L = 5
num_trash = 2
name = f"qsim_params_VQE_Ising_L{L:.0f}_anti_{anti:.0f}_single-jobs"
filename = 'data/' + name
print(filename)
# more in-depth noise models https://qiskit.org/documentation/tutorials/simulators/2_device_noise_simulation.html
#backend = qiskit.Aer.get_backend('qasm_simulator') # apparently outdated (legacy)
IBMQ.load_account() # this then automatically loads your saved account
provider = IBMQ.get_provider(hub='ibm-q-research')
from qiskit.providers.ibmq import least_busy
small_devices = provider.backends(filters=lambda x: x.configuration().n_qubits == 5
and not x.configuration().simulator)
least_busy(small_devices)
#provider.backends(simulator=False, operational=True)
backend = least_busy(small_devices)
backend
backend.status().pending_jobs
backend_sim = qiskit.providers.aer.AerSimulator.from_backend(backend)
ansatz = qiskit.circuit.library.EfficientSU2(L, reps=3)
ansatz = qiskit.transpile(ansatz, backend)
#optimizer = SLSQP(maxiter=1000)
#optimizer = COBYLA(maxiter=1000)
optimizer = SPSA(maxiter=1000)
vqe = VQE(ansatz, optimizer, quantum_instance=backend)
from qiskit.tools.monitor import job_monitor
# Number of shots to run the program (experiment);
# maximum is 8192 shots.
shots = 1024
# Maximum number of credits to spend on executions.
max_credits = 3
job_exp = execute(qc, backend, shots=shots, max_credits=max_credits)
job_monitor(job_exp)
t0 = datetime.datetime.now()
H = QHIsing(L,anti,np.float32(gx),np.float32(gz))
result = vqe.compute_minimum_eigenvalue(H, aux_operators=[QMag(L,anti)]) #ED with Qiskit VQE
print(f"elapsed time {datetime.datetime.now()-t0}")
# ED
ED_state, E, ham = ising_groundstate(L, anti, np.float32(gx), np.float32(gz))
print(f"ED energy: {E} ;; VQE energy: {result.eigenvalue}")
print(f"ED mag: {ED_state.T.conj()@Mag(L,anti)@ED_state} ;; VQE mag: {result.aux_operator_eigenvalues}")
phis = [sort_params(result.optimal_parameters)] # needs to be called phis for later
vqe2 = VQE(qiskit.circuit.library.EfficientSU2(L, reps=3), optimizer, quantum_instance=StatevectorSimulator())
t0 = datetime.datetime.now()
result2 = vqe2.compute_minimum_eigenvalue(H, aux_operators=[QMag(L,anti)]) #ED with Qiskit VQE
print(f"elapsed time {datetime.datetime.now()-t0}")
phis = []
phis.append(sort_params(result2.optimal_parameters))
state = init_vqe(phis[-1], L=L)
state = qiskit.transpile(state, backend)
meas_outcome = ~StateFn(QMag(L,anti)) @ StateFn(state)
Qmag2 = meas_outcome.eval()
e_outcome = ~StateFn(H) @ StateFn(state)
Qen2 = e_outcome.eval()
print(f"ED energy: {E} ;; VQE energy: {result.eigenvalue} ;; VQE energy from simulated: {result2.eigenvalue} ;; VQE simualted but real execution: {Qen2}")
print(f"ED mag: {ED_state.T.conj()@Mag(L,anti)@ED_state} ;; VQE mag: {result.aux_operator_eigenvalues} ;; VQE magfrom simulated: {result2.aux_operator_eigenvalues} ;; VQE simualted but real execution: {Qmag2}")
##############################################################################
### II - Training ###########################################################
##############################################################################
thetas = np.random.uniform(0, 2*np.pi, 2*L+2) # initial parameters without feature encoding
# thetas = np.random.uniform(0, 2*np.pi, (2*L+2, 2)) # initial parameters with feature encoding
# linear entangler (as in scales linearly with trash qubits)
def get_entangler_map(L, num_trash, i_permut=1):
result = []
nums = list(range(L)) # here was the problem, it doesnt like when list elements are taken from numpy
nums_compressed = nums.copy()[:L-num_trash]
nums_trash = nums.copy()[-num_trash:]
#print(nums, nums_compressed, nums_trash)
# combine all trash qubits with themselves
for trash_q in nums_trash[:-1]:
result.append((trash_q+1,trash_q))
# combine each of the trash qubits with every n-th
repeated = list(nums_trash) * (L-num_trash) # repeat the list of trash indices cyclicly
for i in range(L-num_trash):
result.append((repeated[i_permut + i], nums_compressed[i]))
return result
def QAE_Ansatz(thetas, L, num_trash, insert_barriers=False, parametrized_gate = "ry", entangling_gate = "cz"):
entanglement = [get_entangler_map(L,num_trash,i_permut) for i_permut in range(num_trash)]
circ = qiskit.circuit.library.TwoLocal(L,
parametrized_gate,
entangling_gate,
entanglement,
reps=num_trash,
insert_barriers=insert_barriers,
skip_final_rotation_layer=True
).assign_parameters(thetas[:-num_trash])
if insert_barriers: circ.barrier()
for i in range(num_trash):
circ.ry(thetas[L-i-1], L-i-1)
#circ.ry(circuit.Parameter(f'θ{i}'), L-i-1)
return circ
def prepare_circuit(thetas, L=6, num_trash=2, init_state=None, measurement=True, vqe=True):
qreg = QuantumRegister(L, 'q')
creg = ClassicalRegister(num_trash, 'c')
circ = QuantumCircuit(qreg, creg)
circ += QAE_Ansatz(thetas, L, num_trash, insert_barriers=True)#.assign_parameters(thetas) # difference to bind?
if measurement:
for i in range(num_trash):
circ.measure(qreg[L-i-1], creg[i])
if init_state is not None:
if vqe:
circ = init_vqe(init_state,L=L) + circ
else:
circ.initialize(init_state, qreg)
return circ
def run_circuit(thetas, L, num_trash, init_state, vqe=True, shots=1000, backend=backend):
circ = prepare_circuit(thetas, L, num_trash, init_state, vqe=vqe)
circ = qiskit.transpile(circ, backend)
# Execute the circuit on the qasm simulator.
job_sim = execute(circ, backend, shots=shots, seed_simulator=123, seed_transpiler=234) # fix seed to make it reproducible
return job_sim
phi = phis[0]
circ = prepare_circuit(thetas, L, num_trash, phi)
circ = qiskit.transpile(circ, backend)
# Execute the circuit on the qasm simulator.
job = execute(circ, backend, shots=shots) # fix seed to make it reproducible
from qiskit.tools.monitor import job_monitor
job_monitor(job)
jobID = job.job_id()
print('JOB ID: {}'.format(jobID))
#job_get=backend.retrieve_job(jobID)
job_get=backend.retrieve_job(jobID)
job_get
job_get.result().get_counts()
counts
def cost_function_single(thetas, L, num_trash, p, shots=1000, vqe=True, param_encoding=False, x=0):
""" Optimizes circuit """
if vqe:
init_state = phis[p]
else:
J, gx, gz = p
init_state, _ = ising_groundstate(L, J, gx, gz)
if param_encoding: thetas = feature_encoding(thetas, x)
out = run_circuit(thetas, L, num_trash, init_state, vqe=vqe, shots=shots).result().get_counts()
cost = out.get('11', 0)*2 + out.get('01', 0) + out.get('10', 0)
return cost/shots
def cost_function(thetas, L, num_trash, ising_params, shots=1000, vqe=True, param_encoding=False, x=0):
""" Optimizes circuit """
cost = 0.
n_samples = len(ising_params)
for i, p in enumerate(ising_params):
if param_encoding:
cost += cost_function_single(thetas, L, num_trash, p, shots, vqe, param_encoding, x[i])
else:
cost += cost_function_single(thetas, L, num_trash, p, shots, vqe, param_encoding)
return cost/n_samples
def optimize(ising_params, L=6, num_trash=2, thetas=None, shots=1000, max_iter=400, vqe=True, param_encoding=False, x=0, pick_optimizer = None):
if thetas is None:
n_params = (2*L+2)*2 if param_encoding else (2*L+2)
thetas = np.random.uniform(0, 2*np.pi, n_params) # initial parameters without feature encoding
print("Initial cost: {:.3f}".format(cost_function(thetas, L, num_trash, ising_params, shots, vqe, param_encoding, x)))
counts, values, accepted = [], [], []
def store_intermediate_result(eval_count, parameters, mean, std, ac):
# counts.append(eval_count)
values.append(mean)
accepted.append(ac)
# Initialize optimizer
if pick_optimizer == "cobyla":
optimizer = COBYLA(maxiter=max_iter, tol=0.0001)
if pick_optimizer == "adam" or pick_optimizer == "ADAM":
optimizer = qiskit.algorithms.optimizers.ADAM(maxiter=max_iter)
# optimizer = L_BFGS_B(maxfun=300, maxiter=max_iter)#, factr=10, iprint=- 1, epsilon=1e-08)
if pick_optimizer == "spsa" or pick_optimizer == None:
optimizer = SPSA(maxiter=max_iter,
#blocking=True,
callback=store_intermediate_result,
#learning_rate=1e-1,
#perturbation=0.4
) # recommended from qiskit (first iteraction takes quite long)
# to reduce time figure out optimal learning rate and perturbation in advance
start_time = time.time()
ret = optimizer.optimize(
num_vars=len(thetas),
objective_function=(lambda thetas: cost_function(thetas, L, num_trash, ising_params, shots, vqe, param_encoding, x)),
initial_point=thetas
)
print("Time: {:.5f} sec".format(time.time()-start_time))
print(ret)
return ret[0], values, accepted
def run_inference(thetas, shots=1000, L=5):
cost = np.zeros((len(gx_vals)))
shots = 1000
for i,p in enumerate(list(zip(gxs, gzs))):
cost[i] = cost_function_single(thetas, L, num_trash, i, shots=shots)
return cost
# from the original vqe calculated state
thetas, loss, accepted = optimize([0], max_iter=100, L=5) #, pick_optimizer="adam")
plt.plot(loss)
# using the VQE parameters from simulation
thetas, loss, accepted = optimize([1], max_iter=100, L=5) #, pick_optimizer="adam")
plt.plot(loss)
|
https://github.com/Qottmann/Quantum-anomaly-detection
|
Qottmann
|
import time
import datetime
import numpy as np
from matplotlib import pyplot as plt
import qiskit
from qiskit import *
from qiskit.opflow import X,Z,I
from qiskit.opflow.state_fns import StateFn, CircuitStateFn
from qiskit.providers.aer import StatevectorSimulator, AerSimulator
from qiskit.algorithms import VQE
from qiskit.algorithms.optimizers import COBYLA, SLSQP, SPSA
from scipy import sparse
import scipy.sparse.linalg.eigen.arpack as arp
from modules.utils import *
gz = 0
anti = 1
gx = 1e-1
L = 5
num_trash = 2
name = f"qsim_params_VQE_Ising_L{L:.0f}_anti_{anti:.0f}_single-jobs"
filename = 'data/' + name
print(filename)
gx_vals = np.logspace(-2,2,logspace_size)
# more in-depth noise models https://qiskit.org/documentation/tutorials/simulators/2_device_noise_simulation.html
#backend = qiskit.Aer.get_backend('qasm_simulator') # apparently outdated (legacy)
IBMQ.load_account() # this then automatically loads your saved account
provider = IBMQ.get_provider(hub='ibm-q-research')
real_backend = provider.backends(simulator=False, operational=True)[6]
backend = qiskit.providers.aer.AerSimulator.from_backend(real_backend)
backend_sim = backend
ansatz = qiskit.circuit.library.EfficientSU2(L, reps=3)
ansatz = qiskit.transpile(ansatz, backend)
#optimizer = SLSQP(maxiter=1000)
#optimizer = COBYLA(maxiter=1000)
optimizer = SPSA(maxiter=1000)
vqe = VQE(ansatz, optimizer, quantum_instance=backend)
t0 = datetime.datetime.now()
H = QHIsing(L,anti,np.float32(gx),np.float32(gz))
result = vqe.compute_minimum_eigenvalue(H, aux_operators=[QMag(L,anti)]) #ED with Qiskit VQE
print(f"elapsed time {datetime.datetime.now()-t0}")
# ED
ED_state, E, ham = ising_groundstate(L, anti, np.float32(gx), np.float32(gz))
print(f"ED energy: {E} ;; VQE energy: {result.eigenvalue}")
print(f"ED mag: {ED_state.T.conj()@Mag(L,anti)@ED_state} ;; VQE mag: {result.aux_operator_eigenvalues}")
phis = [sort_params(result.optimal_parameters)] # needs to be called phis for later
vqe2 = VQE(qiskit.circuit.library.EfficientSU2(L, reps=3), optimizer, quantum_instance=StatevectorSimulator())
t0 = datetime.datetime.now()
result2 = vqe2.compute_minimum_eigenvalue(H, aux_operators=[QMag(L,anti)]) #ED with Qiskit VQE
print(f"elapsed time {datetime.datetime.now()-t0}")
phis.append(sort_params(result2.optimal_parameters))
state = init_vqe(phis[-1], L=L)
state
state = init_vqe(phis[-1], L=L)
state = qiskit.transpile(state, backend)
meas_outcome = ~StateFn(QMag(L,anti)) @ StateFn(state)
Qmag2 = meas_outcome.eval()
e_outcome = ~StateFn(H) @ StateFn(state)
Qen2 = e_outcome.eval()
print(f"ED energy: {E} ;; VQE energy: {result.eigenvalue} ;; VQE energy from simulated: {result2.eigenvalue} ;; VQE simualted but real execution: {Qen2}")
print(f"ED mag: {ED_state.T.conj()@Mag(L,anti)@ED_state} ;; VQE mag: {result.aux_operator_eigenvalues} ;; VQE magfrom simulated: {result2.aux_operator_eigenvalues} ;; VQE simualted but real execution: {Qmag2}")
##############################################################################
### II - Training ###########################################################
##############################################################################
thetas = np.random.uniform(0, 2*np.pi, 2*L+2) # initial parameters without feature encoding
# thetas = np.random.uniform(0, 2*np.pi, (2*L+2, 2)) # initial parameters with feature encoding
# linear entangler (as in scales linearly with trash qubits)
def get_entangler_map(L, num_trash, i_permut=1):
result = []
nums = list(range(L)) # here was the problem, it doesnt like when list elements are taken from numpy
nums_compressed = nums.copy()[:L-num_trash]
nums_trash = nums.copy()[-num_trash:]
#print(nums, nums_compressed, nums_trash)
# combine all trash qubits with themselves
for trash_q in nums_trash[:-1]:
result.append((trash_q+1,trash_q))
# combine each of the trash qubits with every n-th
repeated = list(nums_trash) * (L-num_trash) # repeat the list of trash indices cyclicly
for i in range(L-num_trash):
result.append((repeated[i_permut + i], nums_compressed[i]))
return result
def QAE_Ansatz(thetas, L, num_trash, insert_barriers=False, parametrized_gate = "ry", entangling_gate = "cz"):
entanglement = [get_entangler_map(L,num_trash,i_permut) for i_permut in range(num_trash)]
circ = qiskit.circuit.library.TwoLocal(L,
parametrized_gate,
entangling_gate,
entanglement,
reps=num_trash,
insert_barriers=insert_barriers,
skip_final_rotation_layer=True
).assign_parameters(thetas[:-num_trash])
if insert_barriers: circ.barrier()
for i in range(num_trash):
circ.ry(thetas[L-i-1], L-i-1)
#circ.ry(circuit.Parameter(f'θ{i}'), L-i-1)
return circ
def prepare_circuit(thetas, L=6, num_trash=2, init_state=None, measurement=True, vqe=True):
qreg = QuantumRegister(L, 'q')
creg = ClassicalRegister(num_trash, 'c')
circ = QuantumCircuit(qreg, creg)
circ += QAE_Ansatz(thetas, L, num_trash, insert_barriers=True)#.assign_parameters(thetas) # difference to bind?
if measurement:
for i in range(num_trash):
circ.measure(qreg[L-i-1], creg[i])
if init_state is not None:
if vqe:
circ = init_vqe(init_state,L=L) + circ
else:
circ.initialize(init_state, qreg)
return circ
def run_circuit(thetas, L, num_trash, init_state, vqe=True, shots=1000, backend=backend):
circ = prepare_circuit(thetas, L, num_trash, init_state, vqe=vqe)
circ = qiskit.transpile(circ, backend)
# Execute the circuit on the qasm simulator.
job_sim = execute(circ, backend_sim, shots=shots, seed_simulator=123, seed_transpiler=234) # fix seed to make it reproducible
return job_sim
phi = phis[0]
job = run_circuit(thetas, L, num_trash, phi)
counts = job.result().get_counts()
counts
def cost_function_single(thetas, L, num_trash, p, shots=1000, vqe=True, param_encoding=False, x=0):
""" Optimizes circuit """
if vqe:
init_state = phis[p]
else:
J, gx, gz = p
init_state, _ = ising_groundstate(L, J, gx, gz)
if param_encoding: thetas = feature_encoding(thetas, x)
out = run_circuit(thetas, L, num_trash, init_state, vqe=vqe, shots=shots).result().get_counts()
cost = out.get('11', 0)*2 + out.get('01', 0) + out.get('10', 0)
return cost/shots
def cost_function(thetas, L, num_trash, ising_params, shots=1000, vqe=True, param_encoding=False, x=0):
""" Optimizes circuit """
cost = 0.
n_samples = len(ising_params)
for i, p in enumerate(ising_params):
if param_encoding:
cost += cost_function_single(thetas, L, num_trash, p, shots, vqe, param_encoding, x[i])
else:
cost += cost_function_single(thetas, L, num_trash, p, shots, vqe, param_encoding)
return cost/n_samples
def optimize(ising_params, L=6, num_trash=2, thetas=None, shots=1000, max_iter=400, vqe=True, param_encoding=False, x=0, pick_optimizer = None):
if thetas is None:
n_params = (2*L+2)*2 if param_encoding else (2*L+2)
thetas = np.random.uniform(0, 2*np.pi, n_params) # initial parameters without feature encoding
print("Initial cost: {:.3f}".format(cost_function(thetas, L, num_trash, ising_params, shots, vqe, param_encoding, x)))
counts, values, accepted = [], [], []
def store_intermediate_result(eval_count, parameters, mean, std, ac):
# counts.append(eval_count)
values.append(mean)
accepted.append(ac)
# Initialize optimizer
if pick_optimizer == "cobyla":
optimizer = COBYLA(maxiter=max_iter, tol=0.0001)
if pick_optimizer == "adam" or pick_optimizer == "ADAM":
optimizer = qiskit.algorithms.optimizers.ADAM(maxiter=max_iter)
# optimizer = L_BFGS_B(maxfun=300, maxiter=max_iter)#, factr=10, iprint=- 1, epsilon=1e-08)
if pick_optimizer == "spsa" or pick_optimizer == None:
optimizer = SPSA(maxiter=max_iter,
#blocking=True,
callback=store_intermediate_result,
#learning_rate=1e-1,
#perturbation=0.4
) # recommended from qiskit (first iteraction takes quite long)
# to reduce time figure out optimal learning rate and perturbation in advance
start_time = time.time()
ret = optimizer.optimize(
num_vars=len(thetas),
objective_function=(lambda thetas: cost_function(thetas, L, num_trash, ising_params, shots, vqe, param_encoding, x)),
initial_point=thetas
)
print("Time: {:.5f} sec".format(time.time()-start_time))
print(ret)
return ret[0], values, accepted
def run_inference(thetas, shots=1000, L=5):
cost = np.zeros((len(gx_vals)))
shots = 1000
for i,p in enumerate(list(zip(gxs, gzs))):
cost[i] = cost_function_single(thetas, L, num_trash, i, shots=shots)
return cost
# from the original vqe calculated state
thetas, loss, accepted = optimize([0], max_iter=100, L=5) #, pick_optimizer="adam")
plt.plot(loss)
# using the VQE parameters from simulation
thetas, loss, accepted = optimize([1], max_iter=100, L=5) #, pick_optimizer="adam")
plt.plot(loss)
|
https://github.com/Qottmann/Quantum-anomaly-detection
|
Qottmann
|
import time
import datetime
import numpy as np
from matplotlib import pyplot as plt
import qiskit
from qiskit import *
from qiskit.opflow import X,Z,I
from qiskit.opflow.state_fns import StateFn, CircuitStateFn
from qiskit.providers.aer import StatevectorSimulator, AerSimulator
from qiskit.algorithms import VQE
from qiskit.algorithms.optimizers import COBYLA, SLSQP, SPSA
from qiskit.opflow import CircuitSampler
from qiskit.ignis.mitigation.measurement import CompleteMeasFitter # you will need to pip install qiskit-ignis
from scipy import sparse
import scipy.sparse.linalg.eigen.arpack as arp
from modules.utils import *
IBMQ.load_account() # this then automatically loads your saved account
provider = IBMQ.get_provider(hub='ibm-q-research')
device = provider.backend.ibmq_rome
print(device)
#backend = device
backend = qiskit.providers.aer.AerSimulator.from_backend(device)
coupling_map = device.configuration().coupling_map
noise_model = qiskit.providers.aer.noise.NoiseModel.from_backend(device)
basis_gates = noise_model.basis_gates
#aqua_globals.random_seed = seed
qi = qiskit.utils.QuantumInstance(backend=backend, # , seed_simulator=seed, seed_transpiler=seed
coupling_map=coupling_map, noise_model=noise_model,
measurement_error_mitigation_cls= CompleteMeasFitter,
cals_matrix_refresh_period=30 #How often to refresh the calibration matrix in measurement mitigation. in minutes
)
# linear entangler (as in scales linearly with trash qubits)
def get_entangler_map(L, num_trash, i_permut=1):
result = []
nums = list(range(L)) # here was the problem, it doesnt like when list elements are taken from numpy
nums_compressed = nums.copy()[:L-num_trash]
nums_trash = nums.copy()[-num_trash:]
#print(nums, nums_compressed, nums_trash)
# combine all trash qubits with themselves
for trash_q in nums_trash[:-1]:
result.append((trash_q+1,trash_q))
# combine each of the trash qubits with every n-th
repeated = list(nums_trash) * (L-num_trash) # repeat the list of trash indices cyclicly
for i in range(L-num_trash):
result.append((repeated[i_permut + i], nums_compressed[i]))
return result
def QAE_Ansatz(thetas, L, num_trash, insert_barriers=False, parametrized_gate = "ry", entangling_gate = "cz"):
entanglement = [get_entangler_map(L,num_trash,i_permut) for i_permut in range(num_trash)]
circ = qiskit.circuit.library.TwoLocal(L,
parametrized_gate,
entangling_gate,
entanglement,
reps=num_trash,
insert_barriers=insert_barriers,
skip_final_rotation_layer=True
).assign_parameters(thetas[:-num_trash])
if insert_barriers: circ.barrier()
for i in range(num_trash):
circ.ry(thetas[L-i-1], L-i-1)
#circ.ry(circuit.Parameter(f'θ{i}'), L-i-1)
return circ
def prepare_circuit(thetas, L=6, num_trash=2, init_state=None, measurement=True, vqe=True):
qreg = QuantumRegister(L, 'q')
creg = ClassicalRegister(num_trash, 'c')
circ = QuantumCircuit(qreg, creg)
circ += QAE_Ansatz(thetas, L, num_trash, insert_barriers=True)#.assign_parameters(thetas) # difference to bind?
if measurement:
for i in range(num_trash):
circ.measure(qreg[L-i-1], creg[i])
if init_state is not None:
if vqe:
circ = init_vqe(init_state,L=L) + circ
else:
circ.initialize(init_state, qreg)
return circ, qreg
def feature_encoding(thetas, x):
""" thetas: parameters to be optimized, x: Ising model parameter (eg. field) """
new_thetas = []
thetas = thetas.reshape((-1,2))
for theta in thetas:
new_thetas.append(theta[0] * x + theta[1])
return new_thetas
backend_sim = backend
def run_circuit(thetas, L, num_trash, init_state, vqe=True, shots=100):
circ, qreg = prepare_circuit(thetas, L, num_trash, init_state, vqe=vqe)
# Execute the circuit on the qasm simulator.
job_sim = execute(circ, backend_sim, shots=shots, seed_simulator=123, seed_transpiler=234) # fix seed to make it reproducible
# Grab the results from the job.
result_sim = job_sim.result()
counts = result_sim.get_counts(circ)
# print(counts)
# mems = result_sim.get_memory(circ)
# print(mems)
return counts
L = 5
num_trash = 2
phi = np.random.rand(40)
thetas = np.random.uniform(0, 2*np.pi, 2*L+2)
run_circuit(thetas, L, num_trash, phi)['11']
from qiskit.ignis.mitigation.measurement import complete_meas_cal
circ, qreg = prepare_circuit(thetas, L, num_trash, phi)
qubit_list = [L-i-1 for i in range(num_trash)] # only need to calibrate the trash qubits
meas_calibs, state_labels = complete_meas_cal(qubit_list=qubit_list, qr=qreg, circlabel='mcal')
job = qiskit.execute(meas_calibs, backend=backend, shots=1000, noise_model=noise_model)
cal_results = job.result()
meas_fitter = CompleteMeasFitter(cal_results, state_labels, circlabel='mcal')
print(meas_fitter.cal_matrix)
meas_fitter.plot_calibration()
job_sim = execute(circ, backend, shots=1000) # fix seed to make it reproducible
result = job_sim.result()
# Results without mitigation
raw_counts = result.get_counts()
# Get the filter object
meas_filter = meas_fitter.filter
# Results with mitigation
mitigated_results = meas_filter.apply(result)
mitigated_counts = mitigated_results.get_counts(0)
from qiskit.tools.visualization import *
plot_histogram([raw_counts, mitigated_counts], legend=['raw', 'mitigated'])
|
https://github.com/Qottmann/Quantum-anomaly-detection
|
Qottmann
|
import time
import itertools
import numpy as np
from scipy.optimize import minimize, basinhopping
from qiskit import *
from qiskit.quantum_info import Statevector
from qiskit.algorithms.optimizers import COBYLA, L_BFGS_B, SPSA
from qiskit.opflow.state_fns import StateFn, CircuitStateFn
from qiskit.providers.aer import StatevectorSimulator, AerSimulator
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
from matplotlib.colors import BoundaryNorm
from modules.utils import *
%matplotlib inline
cmap = plt.get_cmap("plasma") #'viridis'
import qiskit
#%matplotlib inline
print(qiskit.__version__)
### Preliminaries
L = 5
num_trash = 3
logspace_size=50
name = f'VQE_Ising_L5_anti_-1_50x50' # _1e-3 # name of the data produced by this notebook
filename = "data/params_" + name # name of the data file that is used
L = 5
anti = -1.
VQE_params = np.load(filename + ".npz", allow_pickle=True)
gx_list = VQE_params['gx_list']
gz_list = VQE_params['gz_list']
opt_params = VQE_params['opt_params']
gx_vals = np.unique(gx_list)
gz_vals = np.unique(gz_list)
mag = QMag(L,anti) #magnetization operator (Qiskit)
Smag = Mag(L,anti) #magnetization operator (numpy)
# the ~ is the adjoint, but also it turns the is_measurement attribute to True
~StateFn(mag)
# state is technically a circuit, that prepares the ground state via VQE circuit
#state.draw() # uncomment to see, but is very long
Qen=np.zeros(len(opt_params)); Sen=np.zeros(len(opt_params)) #energies
Qmags=np.zeros(len(opt_params)); Smags=np.zeros(len(opt_params)) #magnetizations
load = True
if load:
temp = np.load(filename + "_mags-Es.npz",allow_pickle=True)
Qmags = temp["Qmags"]
Qen = temp["Qen"]
Sen = temp["Sen"]
Smags = temp["Smags"]
if not load:
for j in range(len(opt_params)):
gx = gx_list[j]
gz = gz_list[j]
H = QHIsing(L, anti, np.float32(gx), np.float32(gz)) # build Hamiltonian Op
state = init_vqe(opt_params[j], L=L)
StateFn(state)
meas_outcome = ~StateFn(mag) @ StateFn(state)
Qmags[j] = meas_outcome.eval()
e_outcome = ~StateFn(H) @ StateFn(state)
Qen[j] = e_outcome.eval()
init_state, E, ham = ising_groundstate(L, anti, np.float(gx), np.float(gz))
Sen[j] = E
Smags[j] = init_state.T.conj()@Smag@init_state #Magnetization with Numpy results
np.savez(filename + "_mags-Es",Qmags=Qmags, Qen=Qen, Sen=Sen, Smags=Smags) # for large parameter space takes quite a while because of the exact diagonalization
fig,ax = plt.subplots(figsize=(8,5))
cmap = plt.get_cmap("plasma") #'viridis'
im = ax.pcolormesh(gz_vals, gx_vals, abs(Smags).reshape(len(gz_vals),len(gx_vals)), cmap=cmap)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
plt.yscale("log")
ax.set_title("Magnetization ED L %.0f anti %.0f" %(L, anti),fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
fig,ax = plt.subplots(figsize=(8,5))
cmap = plt.get_cmap("plasma") #'viridis'
im = ax.pcolormesh(gz_vals, gx_vals, abs(Qmags).reshape(len(gz_vals),len(gx_vals)), cmap=cmap)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
plt.yscale("log")
ax.set_title("Magnetization VQE L %.0f anti %.0f" %(L, anti),fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
fig,ax = plt.subplots(figsize=(8,5))
cmap = plt.get_cmap("plasma") #'viridis'
im = ax.pcolormesh(gz_vals, gx_vals, Sen.reshape(len(gz_vals),len(gx_vals)) - Qen.reshape(len(gz_vals),len(gx_vals)), cmap=cmap)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
plt.yscale("log")
ax.set_title("Difference in energy L %.0f anti %.0f" %(L, anti),fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
##############################################################################
### II - Training ###########################################################
##############################################################################
# Use Aer's qasm_simulator
backend_sim = Aer.get_backend('qasm_simulator')
thetas = np.random.uniform(0, 2*np.pi, num_trash*L+num_trash) # initial parameters without feature encoding
# thetas = np.random.uniform(0, 2*np.pi, (2*L+2, 2)) # initial parameters with feature encoding
# linear entangler (as in scales linearly with trash qubits)
def get_entangler_map(L, num_trash, i_permut=1):
result = []
nums = list(range(L)) # here was the problem, it doesnt like when list elements are taken from numpy
nums_compressed = nums.copy()[:L-num_trash]
nums_trash = nums.copy()[-num_trash:]
#print(nums, nums_compressed, nums_trash)
# combine all trash qubits with themselves
for trash_q in nums_trash[:-1]:
result.append((trash_q+1,trash_q))
# combine each of the trash qubits with every n-th
repeated = list(nums_trash) * (L-num_trash) # repeat the list of trash indices cyclicly
for i in range(L-num_trash):
result.append((repeated[i_permut + i], nums_compressed[i]))
return result
def QAE_Ansatz(thetas, L, num_trash, insert_barriers=False, parametrized_gate = "ry", entangling_gate = "cz"):
entanglement = [get_entangler_map(L,num_trash,i_permut) for i_permut in range(num_trash)]
circ = qiskit.circuit.library.TwoLocal(L,
parametrized_gate,
entangling_gate,
entanglement,
reps=num_trash,
insert_barriers=insert_barriers,
skip_final_rotation_layer=True
).assign_parameters(thetas[:-num_trash])
if insert_barriers: circ.barrier()
for i in range(num_trash):
circ.ry(thetas[L-i-1], L-i-1)
#circ.ry(circuit.Parameter(f'θ{i}'), L-i-1)
return circ
def prepare_circuit(thetas, L=5, num_trash=3, init_state=None, measurement=True, vqe=True):
qreg = QuantumRegister(L, 'q')
creg = ClassicalRegister(num_trash, 'c')
circ = QuantumCircuit(qreg, creg)
circ += QAE_Ansatz(thetas, L, num_trash, insert_barriers=True)#.assign_parameters(thetas) # difference to bind?
if measurement:
for i in range(num_trash):
circ.measure(qreg[L-i-1], creg[i])
if init_state is not None:
if vqe:
circ = init_vqe(init_state,L=L) + circ
else:
circ.initialize(init_state, qreg)
return circ
def feature_encoding(thetas, x):
""" thetas: parameters to be optimized, x: Ising model parameter (eg. field) """
new_thetas = []
thetas = thetas.reshape((-1,2))
for theta in thetas:
new_thetas.append(theta[0] * x + theta[1])
return new_thetas
thetas.shape
idx = 30
num_trash = 3
J, gx, gz = -1., gx_list[idx], gz_list[idx] # Ising parameters for which ground state should be compressed
phi = opt_params[idx] # train on smallest lambda ;; this may grammatically be confusing, init_state = dictionary with unsorted parameters
def run_circuit(thetas, L, num_trash, init_state, vqe=True, shots=100):
circ = prepare_circuit(thetas, L, num_trash, init_state, vqe=vqe)
# Execute the circuit on the qasm simulator.
job_sim = execute(circ, backend_sim, shots=shots, seed_simulator=123, seed_transpiler=234) # fix seed to make it reproducible
# Grab the results from the job.
result_sim = job_sim.result()
counts = result_sim.get_counts(circ)
# print(counts)
# mems = result_sim.get_memory(circ)
# print(mems)
return counts
run_circuit(thetas, L, num_trash, phi)['11']
# translate to Rikes naming
phis = opt_params
gxs = gx_list
gzs = gz_list
def cost_function_single(thetas, L, num_trash, p, shots=1000, vqe=True, param_encoding=False, x=0):
""" Optimizes circuit """
if vqe:
init_state = phis[p]
else:
J, gx, gz = p
init_state, _ = ising_groundstate(L, J, gx, gz)
if param_encoding: thetas = feature_encoding(thetas, x)
out = run_circuit(thetas, L, num_trash, init_state, vqe=vqe, shots=shots)
cost = out.get('11', 0)*2 + out.get('01', 0) + out.get('10', 0)
return cost/shots
def cost_function(thetas, L, num_trash, ising_params, shots=1000, vqe=True, param_encoding=False, x=0):
""" Optimizes circuit """
cost = 0.
n_samples = len(ising_params)
for i, p in enumerate(ising_params):
if param_encoding:
cost += cost_function_single(thetas, L, num_trash, p, shots, vqe, param_encoding, x[i])
else:
cost += cost_function_single(thetas, L, num_trash, p, shots, vqe, param_encoding)
return cost/n_samples
def optimize(ising_params, L=6, num_trash=3, thetas=None, shots=1000, max_iter=400, vqe=True, param_encoding=False, x=0, pick_optimizer = None):
if thetas is None:
n_params = (2*L+2)*2 if param_encoding else (num_trash*L+num_trash)
thetas = np.random.uniform(0, 2*np.pi, n_params) # initial parameters without feature encoding
print(f"initial thetas: {thetas}")
print("Initial cost: {:.3f}".format(cost_function(thetas, L, num_trash, ising_params, shots, vqe, param_encoding, x)))
counts, values, accepted = [], [], []
def store_intermediate_result(eval_count, parameters, mean, std, ac):
# counts.append(eval_count)
values.append(mean)
accepted.append(ac)
# Initialize optimizer
if pick_optimizer == "cobyla":
optimizer = COBYLA(maxiter=max_iter, tol=0.0001)
if pick_optimizer == "adam" or pick_optimizer == "ADAM":
optimizer = qiskit.algorithms.optimizers.ADAM(maxiter=max_iter)
# optimizer = L_BFGS_B(maxfun=300, maxiter=max_iter)#, factr=10, iprint=- 1, epsilon=1e-08)
if pick_optimizer == "spsa" or pick_optimizer == None:
optimizer = SPSA(maxiter=max_iter,
#blocking=True,
callback=store_intermediate_result,
#learning_rate=1e-1,
#perturbation=0.4
) # recommended from qiskit (first iteraction takes quite long)
# to reduce time figure out optimal learning rate and perturbation in advance
start_time = time.time()
ret = optimizer.optimize(
num_vars=len(thetas),
objective_function=(lambda thetas: cost_function(thetas, L, num_trash, ising_params, shots, vqe, param_encoding, x)),
initial_point=thetas
)
print("Time: {:.5f} sec".format(time.time()-start_time))
print(ret)
return ret[0], values, accepted
x,y = np.meshgrid(gx_vals, gz_vals)
def run_inference(thetas, shots=1000, L=5):
points = 50
J = -1.0
x,y = np.meshgrid(gx_vals, gz_vals)
cost = np.zeros((len(gx_vals) * len(gz_vals)))
Smags = np.zeros((len(gx_vals) * len(gz_vals)))
shots = 1000
for i,p in enumerate(list(zip(gxs, gzs))):
cost[i] = cost_function_single(thetas, L, num_trash, i, shots=shots)
cost = cost.reshape((len(gx_vals), len(gz_vals)))
return cost
cmap = plt.get_cmap("plasma")
def plot_result(cost):
fig,axs = plt.subplots(ncols=2,figsize=(15,5))
nbins=100
ax = axs[0]
im = ax.pcolormesh(x, y, cost, cmap=cmap, shading="auto")
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
ax.set_xscale("log")
ax.set_yscale("log")
ax.set_title(f"Loss",fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
for p in params:
gz = gz_list[p]
gx = gx_list[p]
ax.plot(gz,gx,"o",label="training",color="cyan")
ax = axs[1]
im = ax.pcolormesh(gz_vals, gx_vals, abs(Qmags).reshape(len(gz_vals),len(gx_vals)), cmap=cmap, shading="auto")
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
ax.set_xscale("log")
ax.set_yscale("log")
ax.set_title("Magnetization VQE L %.0f anti %.0f" %(L, anti),fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
#reset random seed
np.random.seed(123)
from qiskit.utils import algorithm_globals
algorithm_globals.random_seed = 123
np.intersect1d(
np.intersect1d(np.where(gx_list > 5e-2),np.where(1e-1 > gx_list)),
np.intersect1d(np.where(gz_list > 5e-2),np.where(1e-1 > gz_list))
)
paramss =[
[0], # train on bottom left point
[0],
]
for i,params in enumerate(paramss):
print(f"{i+1} / {len(paramss)} - params (gz, gx) in {[(gzs[p], gxs[p]) for p in params]}")
thetass = [None] * len(paramss)
losss = [None] * len(paramss)
costs = [None] * len(paramss)
thetas.shape
run_circuit(thetas, L=5, num_trash=3, init_state = phis[0])
optimize(paramss[0], num_trash=3,L=5,max_iter=100)
for i,params in enumerate(paramss):
print(f"{i+1} / {len(paramss)} - params (gz, gx) in {[(gzs[p], gxs[p]) for p in params]}")
thetas, loss, accepted = optimize(params, num_trash=3, max_iter=100, L=5) #, pick_optimizer="adam")
thetass[i], losss[i] = thetas, loss
plt.plot(loss)
cost = run_inference(thetas)
costs[i] = cost
filename2 = "data/" + name + "_thetas-loss-cost_vary-training-states_num_trash-3"
np.savez(filename2, losss = losss, costs = costs, thetass = thetass)
for i,params in enumerate(paramss):
print(f"{i+1} / {len(paramss)} - params (gz, gx) in {[(gzs[p], gxs[p]) for p in params]}")
plot_result(np.load(filename2 + ".npz",allow_pickle=True)["costs"][i])
plt.show()
|
https://github.com/Qottmann/Quantum-anomaly-detection
|
Qottmann
|
import time
import itertools
import numpy as np
from scipy.optimize import minimize, basinhopping
from qiskit import *
from qiskit.quantum_info import Statevector
from qiskit.algorithms.optimizers import COBYLA, L_BFGS_B, SPSA
from qiskit.opflow.state_fns import StateFn, CircuitStateFn
from qiskit.providers.aer import StatevectorSimulator, AerSimulator
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
from matplotlib.colors import BoundaryNorm
from modules.utils import *
%matplotlib inline
cmap = plt.get_cmap("plasma") #'viridis'
import qiskit
#%matplotlib inline
print(qiskit.__version__)
### Preliminaries
L = 5
num_trash = 2
logspace_size=50
name = f'VQE_Ising_L5_anti_-1_50x50_1e-3' # _1e-3 # name of the data produced by this notebook
filename = "data/params_" + name # name of the data file that is used
L = 5
anti = -1.
VQE_params = np.load(filename + ".npz", allow_pickle=True)
gx_list = VQE_params['gx_list']
gz_list = VQE_params['gz_list']
opt_params = VQE_params['opt_params']
gx_vals = np.unique(gx_list)
gz_vals = np.unique(gz_list)
mag = QMag(L,anti) #magnetization operator (Qiskit)
Smag = Mag(L,anti) #magnetization operator (numpy)
# the ~ is the adjoint, but also it turns the is_measurement attribute to True
~StateFn(mag)
# state is technically a circuit, that prepares the ground state via VQE circuit
#state.draw() # uncomment to see, but is very long
Qen=np.zeros(len(opt_params)); Sen=np.zeros(len(opt_params)) #energies
Qmags=np.zeros(len(opt_params)); Smags=np.zeros(len(opt_params)) #magnetizations
load = True
if load:
temp = np.load(filename + "_mags-Es.npz",allow_pickle=True)
Qmags = temp["Qmags"]
Qen = temp["Qen"]
Sen = temp["Sen"]
Smags = temp["Smags"]
if not load:
for j in range(len(opt_params)):
gx = gx_list[j]
gz = gz_list[j]
H = QHIsing(L, anti, np.float32(gx), np.float32(gz)) # build Hamiltonian Op
state = init_vqe(opt_params[j], L=L)
StateFn(state)
meas_outcome = ~StateFn(mag) @ StateFn(state)
Qmags[j] = meas_outcome.eval()
e_outcome = ~StateFn(H) @ StateFn(state)
Qen[j] = e_outcome.eval()
init_state, E, ham = ising_groundstate(L, anti, np.float(gx), np.float(gz))
Sen[j] = E
Smags[j] = init_state.T.conj()@Smag@init_state #Magnetization with Numpy results
np.savez(filename + "_mags-Es",Qmags=Qmags, Qen=Qen, Sen=Sen, Smags=Smags) # for large parameter space takes quite a while because of the exact diagonalization
fig,ax = plt.subplots(figsize=(8,5))
cmap = plt.get_cmap("plasma") #'viridis'
im = ax.pcolormesh(gz_vals, gx_vals, abs(Smags).reshape(len(gz_vals),len(gx_vals)), cmap=cmap)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
plt.yscale("log")
ax.set_title("Magnetization ED L %.0f anti %.0f" %(L, anti),fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
fig,ax = plt.subplots(figsize=(8,5))
cmap = plt.get_cmap("plasma") #'viridis'
im = ax.pcolormesh(gz_vals, gx_vals, abs(Qmags).reshape(len(gz_vals),len(gx_vals)), cmap=cmap)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
plt.yscale("log")
ax.set_title("Magnetization VQE L %.0f anti %.0f" %(L, anti),fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
fig,ax = plt.subplots(figsize=(8,5))
cmap = plt.get_cmap("plasma") #'viridis'
im = ax.pcolormesh(gz_vals, gx_vals, Sen.reshape(len(gz_vals),len(gx_vals)) - Qen.reshape(len(gz_vals),len(gx_vals)), cmap=cmap)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
plt.yscale("log")
ax.set_title("Difference in energy L %.0f anti %.0f" %(L, anti),fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
##############################################################################
### II - Training ###########################################################
##############################################################################
# Use Aer's qasm_simulator
backend_sim = Aer.get_backend('qasm_simulator')
thetas = np.random.uniform(0, 2*np.pi, 2*L+2) # initial parameters without feature encoding
# thetas = np.random.uniform(0, 2*np.pi, (2*L+2, 2)) # initial parameters with feature encoding
# linear entangler (as in scales linearly with trash qubits)
def get_entangler_map(L, num_trash, i_permut=1):
result = []
nums = list(range(L)) # here was the problem, it doesnt like when list elements are taken from numpy
nums_compressed = nums.copy()[:L-num_trash]
nums_trash = nums.copy()[-num_trash:]
#print(nums, nums_compressed, nums_trash)
# combine all trash qubits with themselves
for trash_q in nums_trash[:-1]:
result.append((trash_q+1,trash_q))
# combine each of the trash qubits with every n-th
repeated = list(nums_trash) * (L-num_trash) # repeat the list of trash indices cyclicly
for i in range(L-num_trash):
result.append((repeated[i_permut + i], nums_compressed[i]))
return result
def QAE_Ansatz(thetas, L, num_trash, insert_barriers=False, parametrized_gate = "ry", entangling_gate = "cz"):
entanglement = [get_entangler_map(L,num_trash,i_permut) for i_permut in range(num_trash)]
circ = qiskit.circuit.library.TwoLocal(L,
parametrized_gate,
entangling_gate,
entanglement,
reps=num_trash,
insert_barriers=insert_barriers,
skip_final_rotation_layer=True
).assign_parameters(thetas[:-num_trash])
if insert_barriers: circ.barrier()
for i in range(num_trash):
circ.ry(thetas[L-i-1], L-i-1)
#circ.ry(circuit.Parameter(f'θ{i}'), L-i-1)
return circ
def prepare_circuit(thetas, L=6, num_trash=2, init_state=None, measurement=True, vqe=True):
qreg = QuantumRegister(L, 'q')
creg = ClassicalRegister(num_trash, 'c')
circ = QuantumCircuit(qreg, creg)
circ += QAE_Ansatz(thetas, L, num_trash, insert_barriers=True)#.assign_parameters(thetas) # difference to bind?
if measurement:
for i in range(num_trash):
circ.measure(qreg[L-i-1], creg[i])
if init_state is not None:
if vqe:
circ = init_vqe(init_state,L=L) + circ
else:
circ.initialize(init_state, qreg)
return circ
def feature_encoding(thetas, x):
""" thetas: parameters to be optimized, x: Ising model parameter (eg. field) """
new_thetas = []
thetas = thetas.reshape((-1,2))
for theta in thetas:
new_thetas.append(theta[0] * x + theta[1])
return new_thetas
idx = 30
num_trash = 2
J, gx, gz = -1., gx_list[idx], gz_list[idx] # Ising parameters for which ground state should be compressed
phi = opt_params[idx] # train on smallest lambda ;; this may grammatically be confusing, init_state = dictionary with unsorted parameters
def run_circuit(thetas, L, num_trash, init_state, vqe=True, shots=100):
circ = prepare_circuit(thetas, L, num_trash, init_state, vqe=vqe)
# Execute the circuit on the qasm simulator.
job_sim = execute(circ, backend_sim, shots=shots, seed_simulator=123, seed_transpiler=234) # fix seed to make it reproducible
# Grab the results from the job.
result_sim = job_sim.result()
counts = result_sim.get_counts(circ)
# print(counts)
# mems = result_sim.get_memory(circ)
# print(mems)
return counts
run_circuit(thetas, L, num_trash, phi)['11']
# translate to Rikes naming
phis = opt_params
gxs = gx_list
gzs = gz_list
def cost_function_single(thetas, L, num_trash, p, shots=1000, vqe=True, param_encoding=False, x=0):
""" Optimizes circuit """
if vqe:
init_state = phis[p]
else:
J, gx, gz = p
init_state, _ = ising_groundstate(L, J, gx, gz)
if param_encoding: thetas = feature_encoding(thetas, x)
out = run_circuit(thetas, L, num_trash, init_state, vqe=vqe, shots=shots)
cost = out.get('11', 0)*2 + out.get('01', 0) + out.get('10', 0)
return cost/shots
def cost_function(thetas, L, num_trash, ising_params, shots=1000, vqe=True, param_encoding=False, x=0):
""" Optimizes circuit """
cost = 0.
n_samples = len(ising_params)
for i, p in enumerate(ising_params):
if param_encoding:
cost += cost_function_single(thetas, L, num_trash, p, shots, vqe, param_encoding, x[i])
else:
cost += cost_function_single(thetas, L, num_trash, p, shots, vqe, param_encoding)
return cost/n_samples
def optimize(ising_params, L=6, num_trash=2, thetas=None, shots=1000, max_iter=400, vqe=True, param_encoding=False, x=0, pick_optimizer = None):
if thetas is None:
n_params = (2*L+2)*2 if param_encoding else (2*L+2)
thetas = np.random.uniform(0, 2*np.pi, n_params) # initial parameters without feature encoding
print("Initial cost: {:.3f}".format(cost_function(thetas, L, num_trash, ising_params, shots, vqe, param_encoding, x)))
counts, values, accepted = [], [], []
def store_intermediate_result(eval_count, parameters, mean, std, ac):
# counts.append(eval_count)
values.append(mean)
accepted.append(ac)
# Initialize optimizer
if pick_optimizer == "cobyla":
optimizer = COBYLA(maxiter=max_iter, tol=0.0001)
if pick_optimizer == "adam" or pick_optimizer == "ADAM":
optimizer = qiskit.algorithms.optimizers.ADAM(maxiter=max_iter)
# optimizer = L_BFGS_B(maxfun=300, maxiter=max_iter)#, factr=10, iprint=- 1, epsilon=1e-08)
if pick_optimizer == "spsa" or pick_optimizer == None:
optimizer = SPSA(maxiter=max_iter,
#blocking=True,
callback=store_intermediate_result,
#learning_rate=1e-1,
#perturbation=0.4
) # recommended from qiskit (first iteraction takes quite long)
# to reduce time figure out optimal learning rate and perturbation in advance
start_time = time.time()
ret = optimizer.optimize(
num_vars=len(thetas),
objective_function=(lambda thetas: cost_function(thetas, L, num_trash, ising_params, shots, vqe, param_encoding, x)),
initial_point=thetas
)
print("Time: {:.5f} sec".format(time.time()-start_time))
print(ret)
return ret[0], values, accepted
x,y = np.meshgrid(gx_vals, gz_vals)
def run_inference(thetas, shots=1000, L=5):
points = 50
J = -1.0
x,y = np.meshgrid(gx_vals, gz_vals)
cost = np.zeros((len(gx_vals) * len(gz_vals)))
Smags = np.zeros((len(gx_vals) * len(gz_vals)))
shots = 1000
for i,p in enumerate(list(zip(gxs, gzs))):
cost[i] = cost_function_single(thetas, L, num_trash, i, shots=shots)
cost = cost.reshape((len(gx_vals), len(gz_vals)))
return cost
cmap = plt.get_cmap("plasma")
def plot_result(cost):
fig,axs = plt.subplots(ncols=2,figsize=(15,5))
nbins=100
ax = axs[0]
im = ax.pcolormesh(x, y, cost, cmap=cmap, shading="auto")
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
ax.set_xscale("log")
ax.set_yscale("log")
ax.set_title(f"Loss",fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
for p in params:
gz = gz_list[p]
gx = gx_list[p]
ax.plot(gz,gx,"o",label="training",color="cyan")
ax = axs[1]
im = ax.pcolormesh(gz_vals, gx_vals, Qmags.reshape(len(gz_vals),len(gx_vals)), cmap=cmap, shading="auto")
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
ax.set_xscale("log")
ax.set_yscale("log")
ax.set_title("Magnetization VQE L %.0f anti %.0f" %(L, anti),fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
#reset random seed
#np.random.seed(123)
#from qiskit.utils import algorithm_globals
#algorithm_globals.random_seed = 123
np.intersect1d(
np.intersect1d(np.where(gx_list > 5e-2),np.where(1e-1 > gx_list)),
np.intersect1d(np.where(gz_list > 5e-2),np.where(1e-1 > gz_list))
)
paramss =[
[0], # train on bottom left point
]
for i,params in enumerate(paramss):
print(f"{i} / {len(paramss)} - params (gz, gx) in {[(gzs[p], gxs[p]) for p in params]}")
thetass = [None] * len(paramss)
losss = [None] * len(paramss)
costs = [None] * len(paramss)
for i,params in enumerate(paramss):
print(f"{i+1} / {len(paramss)} - params (gz, gx) in {[(gzs[p], gxs[p]) for p in params]}")
thetas, loss, accepted = optimize(params, max_iter=50, L=5) #, pick_optimizer="adam")
thetass[i], losss[i] = thetas, loss
plt.plot(loss)
cost = run_inference(thetas)
costs[i] = cost
filename2 = "data/" + name + "_thetas-loss-cost_vary-training-states-01"
np.savez(filename2, losss = losss, costs = costs, thetass = thetass)
for i,params in enumerate(paramss):
print(f"{i+1} / {len(paramss)} - params (gz, gx) in {[(gzs[p], gxs[p]) for p in params]}")
plot_result(np.load(filename2 + ".npz",allow_pickle=True)["costs"][i])
plt.show()
for i,params in enumerate(paramss):
print(f"{i+1} / {len(paramss)} - params (gz, gx) in {[(gzs[p], gxs[p]) for p in params]}")
thetas, loss, accepted = optimize(params, max_iter=50, L=5) #, pick_optimizer="adam")
thetass[i], losss[i] = thetas, loss
plt.plot(loss)
cost = run_inference(thetas)
costs[i] = cost
filename2 = "data/" + name + "_thetas-loss-cost_vary-training-states-02"
np.savez(filename2, losss = losss, costs = costs, thetass = thetass)
for i,params in enumerate(paramss):
print(f"{i+1} / {len(paramss)} - params (gz, gx) in {[(gzs[p], gxs[p]) for p in params]}")
plot_result(np.load(filename2 + ".npz",allow_pickle=True)["costs"][i])
plt.show()
|
https://github.com/Qottmann/Quantum-anomaly-detection
|
Qottmann
|
import time
import itertools
import numpy as np
from scipy.optimize import minimize, basinhopping
from qiskit import *
from qiskit.quantum_info import Statevector
from qiskit.algorithms.optimizers import COBYLA, L_BFGS_B, SPSA
from qiskit.opflow.state_fns import StateFn, CircuitStateFn
from qiskit.providers.aer import StatevectorSimulator, AerSimulator
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
from matplotlib.colors import BoundaryNorm
from modules.utils import *
%matplotlib inline
cmap = plt.get_cmap("plasma") #'viridis'
import qiskit
#%matplotlib inline
print(qiskit.__version__)
### Preliminaries
L = 5
num_trash = 2
logspace_size=50
name = f'VQE_Ising_L5_anti_-1_50x50' # _1e-3 # name of the data produced by this notebook
filename = "data/params_" + name # name of the data file that is used
L = 5
anti = -1.
VQE_params = np.load(filename + ".npz", allow_pickle=True)
gx_list = VQE_params['gx_list']
gz_list = VQE_params['gz_list']
opt_params = VQE_params['opt_params']
gx_vals = np.unique(gx_list)
gz_vals = np.unique(gz_list)
mag = QMag(L,anti) #magnetization operator (Qiskit)
Smag = Mag(L,anti) #magnetization operator (numpy)
# the ~ is the adjoint, but also it turns the is_measurement attribute to True
~StateFn(mag)
# state is technically a circuit, that prepares the ground state via VQE circuit
#state.draw() # uncomment to see, but is very long
Qen=np.zeros(len(opt_params)); Sen=np.zeros(len(opt_params)) #energies
Qmags=np.zeros(len(opt_params)); Smags=np.zeros(len(opt_params)) #magnetizations
load = True
if load:
temp = np.load(filename + "_mags-Es.npz",allow_pickle=True)
Qmags = temp["Qmags"]
Qen = temp["Qen"]
Sen = temp["Sen"]
Smags = temp["Smags"]
if not load:
for j in range(len(opt_params)):
gx = gx_list[j]
gz = gz_list[j]
H = QHIsing(L, anti, np.float32(gx), np.float32(gz)) # build Hamiltonian Op
state = init_vqe(opt_params[j], L=L)
StateFn(state)
meas_outcome = ~StateFn(mag) @ StateFn(state)
Qmags[j] = meas_outcome.eval()
e_outcome = ~StateFn(H) @ StateFn(state)
Qen[j] = e_outcome.eval()
init_state, E, ham = ising_groundstate(L, anti, np.float(gx), np.float(gz))
Sen[j] = E
Smags[j] = init_state.T.conj()@Smag@init_state #Magnetization with Numpy results
np.savez(filename + "_mags-Es",Qmags=Qmags, Qen=Qen, Sen=Sen, Smags=Smags) # for large parameter space takes quite a while because of the exact diagonalization
fig,ax = plt.subplots(figsize=(8,5))
cmap = plt.get_cmap("plasma") #'viridis'
im = ax.pcolormesh(gz_vals, gx_vals, abs(Smags).reshape(len(gz_vals),len(gx_vals)), cmap=cmap)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
plt.yscale("log")
ax.set_title("Magnetization ED L %.0f anti %.0f" %(L, anti),fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
fig,ax = plt.subplots(figsize=(8,5))
cmap = plt.get_cmap("plasma") #'viridis'
im = ax.pcolormesh(gz_vals, gx_vals, abs(Qmags).reshape(len(gz_vals),len(gx_vals)), cmap=cmap)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
plt.yscale("log")
ax.set_title("Magnetization VQE L %.0f anti %.0f" %(L, anti),fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
fig,ax = plt.subplots(figsize=(8,5))
cmap = plt.get_cmap("plasma") #'viridis'
im = ax.pcolormesh(gz_vals, gx_vals, Sen.reshape(len(gz_vals),len(gx_vals)) - Qen.reshape(len(gz_vals),len(gx_vals)), cmap=cmap)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
plt.yscale("log")
ax.set_title("Difference in energy L %.0f anti %.0f" %(L, anti),fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
##############################################################################
### II - Training ###########################################################
##############################################################################
# Use Aer's qasm_simulator
backend_sim = Aer.get_backend('qasm_simulator')
thetas = np.random.uniform(0, 2*np.pi, 2*L+2) # initial parameters without feature encoding
# thetas = np.random.uniform(0, 2*np.pi, (2*L+2, 2)) # initial parameters with feature encoding
# linear entangler (as in scales linearly with trash qubits)
def get_entangler_map(L, num_trash, i_permut=1):
result = []
nums = list(range(L)) # here was the problem, it doesnt like when list elements are taken from numpy
nums_compressed = nums.copy()[:L-num_trash]
nums_trash = nums.copy()[-num_trash:]
#print(nums, nums_compressed, nums_trash)
# combine all trash qubits with themselves
for trash_q in nums_trash[:-1]:
result.append((trash_q+1,trash_q))
# combine each of the trash qubits with every n-th
repeated = list(nums_trash) * (L-num_trash) # repeat the list of trash indices cyclicly
for i in range(L-num_trash):
result.append((repeated[i_permut + i], nums_compressed[i]))
return result
def QAE_Ansatz(thetas, L, num_trash, insert_barriers=False, parametrized_gate = "ry", entangling_gate = "cz"):
entanglement = [get_entangler_map(L,num_trash,i_permut) for i_permut in range(num_trash)]
circ = qiskit.circuit.library.TwoLocal(L,
parametrized_gate,
entangling_gate,
entanglement,
reps=num_trash,
insert_barriers=insert_barriers,
skip_final_rotation_layer=True
).assign_parameters(thetas[:-num_trash])
if insert_barriers: circ.barrier()
for i in range(num_trash):
circ.ry(thetas[L-i-1], L-i-1)
#circ.ry(circuit.Parameter(f'θ{i}'), L-i-1)
return circ
def prepare_circuit(thetas, L=6, num_trash=2, init_state=None, measurement=True, vqe=True):
qreg = QuantumRegister(L, 'q')
creg = ClassicalRegister(num_trash, 'c')
circ = QuantumCircuit(qreg, creg)
circ += QAE_Ansatz(thetas, L, num_trash, insert_barriers=True)#.assign_parameters(thetas) # difference to bind?
if measurement:
for i in range(num_trash):
circ.measure(qreg[L-i-1], creg[i])
if init_state is not None:
if vqe:
circ = init_vqe(init_state,L=L) + circ
else:
circ.initialize(init_state, qreg)
return circ
def feature_encoding(thetas, x):
""" thetas: parameters to be optimized, x: Ising model parameter (eg. field) """
new_thetas = []
thetas = thetas.reshape((-1,2))
for theta in thetas:
new_thetas.append(theta[0] * x + theta[1])
return new_thetas
idx = 30
num_trash = 2
J, gx, gz = -1., gx_list[idx], gz_list[idx] # Ising parameters for which ground state should be compressed
phi = opt_params[idx] # train on smallest lambda ;; this may grammatically be confusing, init_state = dictionary with unsorted parameters
def run_circuit(thetas, L, num_trash, init_state, vqe=True, shots=100):
circ = prepare_circuit(thetas, L, num_trash, init_state, vqe=vqe)
# Execute the circuit on the qasm simulator.
job_sim = execute(circ, backend_sim, shots=shots, seed_simulator=123, seed_transpiler=234) # fix seed to make it reproducible
# Grab the results from the job.
result_sim = job_sim.result()
counts = result_sim.get_counts(circ)
# print(counts)
# mems = result_sim.get_memory(circ)
# print(mems)
return counts
run_circuit(thetas, L, num_trash, phi)['11']
# translate to Rikes naming
phis = opt_params
gxs = gx_list
gzs = gz_list
def cost_function_single(thetas, L, num_trash, p, shots=1000, vqe=True, param_encoding=False, x=0):
""" Optimizes circuit """
if vqe:
init_state = phis[p]
else:
J, gx, gz = p
init_state, _ = ising_groundstate(L, J, gx, gz)
if param_encoding: thetas = feature_encoding(thetas, x)
out = run_circuit(thetas, L, num_trash, init_state, vqe=vqe, shots=shots)
cost = out.get('11', 0)*2 + out.get('01', 0) + out.get('10', 0)
return cost/shots
def cost_function(thetas, L, num_trash, ising_params, shots=1000, vqe=True, param_encoding=False, x=0):
""" Optimizes circuit """
cost = 0.
n_samples = len(ising_params)
for i, p in enumerate(ising_params):
if param_encoding:
cost += cost_function_single(thetas, L, num_trash, p, shots, vqe, param_encoding, x[i])
else:
cost += cost_function_single(thetas, L, num_trash, p, shots, vqe, param_encoding)
return cost/n_samples
def optimize(ising_params, L=6, num_trash=2, thetas=None, shots=1000, max_iter=400, vqe=True, param_encoding=False, x=0, pick_optimizer = None):
if thetas is None:
n_params = (2*L+2)*2 if param_encoding else (2*L+2)
thetas = np.random.uniform(0, 2*np.pi, n_params) # initial parameters without feature encoding
print("Initial cost: {:.3f}".format(cost_function(thetas, L, num_trash, ising_params, shots, vqe, param_encoding, x)))
counts, values, accepted = [], [], []
def store_intermediate_result(eval_count, parameters, mean, std, ac):
# counts.append(eval_count)
values.append(mean)
accepted.append(ac)
# Initialize optimizer
if pick_optimizer == "cobyla":
optimizer = COBYLA(maxiter=max_iter, tol=0.0001)
if pick_optimizer == "adam" or pick_optimizer == "ADAM":
optimizer = qiskit.algorithms.optimizers.ADAM(maxiter=max_iter)
# optimizer = L_BFGS_B(maxfun=300, maxiter=max_iter)#, factr=10, iprint=- 1, epsilon=1e-08)
if pick_optimizer == "spsa" or pick_optimizer == None:
optimizer = SPSA(maxiter=max_iter,
#blocking=True,
callback=store_intermediate_result,
#learning_rate=1e-1,
#perturbation=0.4
) # recommended from qiskit (first iteraction takes quite long)
# to reduce time figure out optimal learning rate and perturbation in advance
start_time = time.time()
ret = optimizer.optimize(
num_vars=len(thetas),
objective_function=(lambda thetas: cost_function(thetas, L, num_trash, ising_params, shots, vqe, param_encoding, x)),
initial_point=thetas
)
print("Time: {:.5f} sec".format(time.time()-start_time))
print(ret)
return ret[0], values, accepted
x,y = np.meshgrid(gx_vals, gz_vals)
def run_inference(thetas, shots=1000, L=5):
points = 50
J = -1.0
x,y = np.meshgrid(gx_vals, gz_vals)
cost = np.zeros((len(gx_vals) * len(gz_vals)))
Smags = np.zeros((len(gx_vals) * len(gz_vals)))
shots = 1000
for i,p in enumerate(list(zip(gxs, gzs))):
cost[i] = cost_function_single(thetas, L, num_trash, i, shots=shots)
cost = cost.reshape((len(gx_vals), len(gz_vals)))
return cost
cmap = plt.get_cmap("plasma")
def plot_result(cost):
fig,axs = plt.subplots(ncols=2,figsize=(15,5))
nbins=100
ax = axs[0]
im = ax.pcolormesh(x, y, cost, cmap=cmap, shading="auto")
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
ax.set_xscale("log")
ax.set_yscale("log")
ax.set_title(f"Loss",fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
for p in params:
gz = gz_list[p]
gx = gx_list[p]
ax.plot(gz,gx,"o",label="training",color="cyan")
ax = axs[1]
im = ax.pcolormesh(gz_vals, gx_vals, abs(Qmags).reshape(len(gz_vals),len(gx_vals)), cmap=cmap, shading="auto")
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
ax.set_xscale("log")
ax.set_yscale("log")
ax.set_title("Magnetization VQE L %.0f anti %.0f" %(L, anti),fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
#reset random seed
np.random.seed(123)
from qiskit.utils import algorithm_globals
algorithm_globals.random_seed = 123
np.intersect1d(
np.intersect1d(np.where(gx_list > 5e-2),np.where(1e-1 > gx_list)),
np.intersect1d(np.where(gz_list > 5e-2),np.where(1e-1 > gz_list))
)
paramss =[
[459], # train on bottom left point
[460],
[509],
[560]
]
for i,params in enumerate(paramss):
print(f"{i} / {len(paramss)} - params (gz, gx) in {[(gzs[p], gxs[p]) for p in params]}")
thetass = [None] * len(paramss)
losss = [None] * len(paramss)
costs = [None] * len(paramss)
for i,params in enumerate(paramss):
print(f"{i+1} / {len(paramss)} - params (gz, gx) in {[(gzs[p], gxs[p]) for p in params]}")
thetas, loss, accepted = optimize(params, max_iter=100, L=5) #, pick_optimizer="adam")
thetass[i], losss[i] = thetas, loss
plt.plot(loss)
cost = run_inference(thetas)
costs[i] = cost
filename2 = "data/" + name + "_thetas-loss-cost_vary-training-states"
np.savez(filename2, losss = losss, costs = costs, thetass = thetass)
for i,params in enumerate(paramss):
print(f"{i+1} / {len(paramss)} - params (gz, gx) in {[(gzs[p], gxs[p]) for p in params]}")
plot_result(np.load(filename2 + ".npz",allow_pickle=True)["costs"][i])
plt.show()
for i,params in enumerate(paramss):
print(f"{i+1} / {len(paramss)} - params (gz, gx) in {[(gzs[p], gxs[p]) for p in params]}")
thetas, loss, accepted = optimize(params, max_iter=100, L=5, pick_optimizer="cobyla") #, pick_optimizer="adam")
thetass[i], losss[i] = thetas, loss
cost = run_inference(thetas)
costs[i] = cost
filename2 = "data/" + name + "_thetas-loss-cost_vary-training-states_cobyla"
np.savez(filename2, losss = losss, costs = costs, thetass = thetass)
for i,params in enumerate(paramss):
print(f"{i+1} / {len(paramss)} - params (gz, gx) in {[(gzs[p], gxs[p]) for p in params]}")
plot_result(np.load(filename2 + ".npz",allow_pickle=True)["costs"][i])
plt.show()
|
https://github.com/Qottmann/Quantum-anomaly-detection
|
Qottmann
|
import sys
sys.path.insert(1, '..') # sets the import path to the parent folder
import time
import itertools
import numpy as np
import qiskit
from qiskit import *
from qiskit.quantum_info import Statevector
from qiskit.algorithms.optimizers import COBYLA, L_BFGS_B, SPSA
from qiskit.opflow.state_fns import StateFn, CircuitStateFn
from qiskit.providers.aer import StatevectorSimulator, AerSimulator
from qiskit.opflow import CircuitSampler
from qiskit.ignis.mitigation.measurement import CompleteMeasFitter # you will need to pip install qiskit-ignis
from qiskit.ignis.mitigation.measurement import complete_meas_cal
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
from matplotlib.colors import BoundaryNorm
cmap = plt.get_cmap("plasma") #'viridis'
from modules.utils import *
from qae import *
import datetime
from tenpy.networks.mps import MPS
from tenpy.models.hubbard import BoseHubbardChain
from tenpy.algorithms import dmrg
from tenpy.linalg import np_conserved
def DMRG_EBH(L, V, t_list, chi_max=30, bc_MPS='infinite'):
model_params = dict(n_max=1, filling=0.5, bc_MPS=bc_MPS, t=t_list,
L=L, V=V, mu=0, conserve='N')
M = BoseHubbardChain(model_params)
vector=[]
for i in range(M.lat.N_sites):
if i%2:
vector.append(1)
else:
vector.append(0)
psi = MPS.from_product_state(M.lat.mps_sites(), vector, bc=M.lat.bc_MPS)
dmrg_params = {
'mixer': True,
'trunc_params': {
'chi_max': chi_max,
},
'max_E_err': 1.e-16,
#'verbose': 0
}
info = dmrg.run(psi, M, dmrg_params)
return info['E'], psi
print(qiskit.__version__, np.__version__)
#IBMQ.load_account() # this then automatically loads your saved account
#provider = IBMQ.get_provider(hub='ibm-q-research')
#device = provider.backend.ibmq_rome # 6 bogota ; 4 rome
### Real device execution:
#backend = device
### Simulation with noise profile from real device
#backend = qiskit.providers.aer.AerSimulator.from_backend(device)
### Simulation without noise
backend = qiskit.providers.aer.AerSimulator(method="matrix_product_state")
#backend = StatevectorSimulator()
### Preliminaries
L = 10
num_trash = 2
anti = 1 # 1 for ferromagnetic Ising model, -1 for antiferromagnet
filename = "data/QAEAnsatz_scaling_MPS"
V = 1
deltat=1
chi = 2**3
print("bond dimension : ", chi)
t_list = np.ones(L-1)
for i in range(len(t_list)):
t_list[i] -= deltat*(-1)**i
E0, psi0 = DMRG_EBH(L, V, t_list, chi_max=chi, bc_MPS='finite')
thetas = np.random.rand(num_trash*(L+1))
QAE_circ = QAEAnsatz(num_qubits = L, num_trash_qubits= num_trash, trash_qubits_idxs = list(range(num_trash)), measure_trash=False).assign_parameters(thetas)
QAE_circ.save_matrix_product_state(label="my_mps")
result = backend.run(QAE_circ).result()
#result.data(0)["my_mps"]
[(_[0].shape, _[1].shape) for _ in result.data(0)["my_mps"][0]]
[_.shape for _ in result.data(0)["my_mps"][1]]
# G is only the local tensor (not multiplied by any singular values) - see https://tenpy.readthedocs.io/en/latest/reference/tenpy.networks.mps.html
A_list = [psi0.get_B(i, form="G").to_ndarray().transpose([1,0,2]) for i in range(L)]
for i,A in enumerate(A_list):
A_list[i] = (A[0], A[1])
[(_[0].shape, _[1].shape) for _ in A_list]
S_list = [psi0.get_SR(i) for i in range(L-1)] # skip trivial last bond; hast to be of size L-1
state = (A_list, S_list)
initcirc = QuantumCircuit(QuantumRegister(L,"q")) #,ClassicalRegister(num_trash, 'c')
initcirc.set_matrix_product_state(state)
initcirc.draw()
plt.plot(psi0.expectation_value("N"))
i=0
qi = qiskit.utils.QuantumInstance(backend=backend, # , seed_simulator=seed, seed_transpiler=seed
shots = 1000,
#coupling_map=coupling_map,
#noise_model=noise_model, # comment out on real device execution
#measurement_error_mitigation_cls= CompleteMeasFitter,
#cals_matrix_refresh_period=30 #How often to refresh the calibration matrix in measurement mitigation. in minutes
)
QZ = np.zeros(L)
for i in range(L):
meas_outcome = ~StateFn(QNKron(L,Z,I,i)) @ StateFn(initcirc)
QZ[i] = CircuitSampler(qi).convert(meas_outcome).eval()
plt.plot(QZ)
|
https://github.com/Qottmann/Quantum-anomaly-detection
|
Qottmann
|
from qiskit import IBMQ
# token is obtained from IBMQ expierence website, just set up an account at https://quantum-computing.ibm.com/ and once run
# IBMQ.enable_account(token)
# IBMQ.save_account(token)
IBMQ.load_account() # this then automatically loads your saved account
IBMQ.providers() # list of providers, in our case just the standard open one since we dont have any special access (yet!)
provider = IBMQ.get_provider(hub='ibm-q-research')
provider.backends(simulator=False, operational=True)
provider.backends(filters=lambda x: x.configuration().n_qubits >= 10
and not x.configuration().simulator
and x.status().operational==True)
from qiskit.providers.ibmq import least_busy
small_devices = provider.backends(filters=lambda x: x.configuration().n_qubits == 5
and not x.configuration().simulator)
least_busy(small_devices)
backend = least_busy(small_devices)
#backend = provider.backends(simulator=False, operational=True)[7]
backend
status = backend.status()
print("pending jobs: ", status.pending_jobs)
print("maximum jobs to submit: ", backend.job_limit().maximum_jobs)
print("operational: ", status.operational)
ran_job = None
for ran_job in backend.jobs(limit=5):
print(str(ran_job.job_id()) + " " + str(ran_job.status()))
import qiskit
qr = qiskit.QuantumRegister(3)
cr = qiskit.ClassicalRegister(3)
circuit = qiskit.QuantumCircuit(qr, cr)
circuit.x(qr[0])
circuit.x(qr[1])
circuit.ccx(qr[0], qr[1], qr[2])
circuit.cx(qr[0], qr[1])
circuit.measure(qr, cr)
mapped_circuit = qiskit.compiler.transpile(circuit, backend=backend)
#qobj = qiskit.compiler.assemble(mapped_circuit, backend=backend, shots=1024) # apparently deprecated and or unnecessary
job = backend.run(mapped_circuit)
print(status.pending_jobs)
job.status()
job.job_id()
retrieved_job = backend.retrieve_job(job.job_id())
# once job is done this should yield result
#result = retrieved_job.result()
#counts = result.get_counts()
#print(counts)
from qiskit.providers.ibmq.managed import IBMQJobManager
sim_backend = provider.get_backend('ibmq_qasm_simulator')
circs = qiskit.compiler.transpile([circuit]*20, backend=sim_backend)
# Submit them all to the backend
job_manager = IBMQJobManager()
job_set = job_manager.run(circs, backend=sim_backend, name='foo')
print(job_set.report())
|
https://github.com/Qottmann/Quantum-anomaly-detection
|
Qottmann
|
import numpy as np
from scipy.optimize import minimize
import qiskit
from qiskit import Aer, QuantumCircuit
from qiskit.utils import QuantumInstance
from matplotlib import pyplot as plt
from qiskit_machine_learning.neural_networks import CircuitQNN
# linear entangler (as in scales linearly with trash qubits)
def get_entangler_map(L, num_trash, i_permut=1):
result = []
nums = list(range(L)) # here was the problem, it doesnt like when list elements are taken from numpy
nums_compressed = nums.copy()[:L-num_trash]
nums_trash = nums.copy()[-num_trash:]
#print(nums, nums_compressed, nums_trash)
# combine all trash qubits with themselves
for trash_q in nums_trash[:-1]:
result.append((trash_q+1,trash_q))
# combine each of the trash qubits with every n-th
repeated = list(nums_trash) * (L-num_trash) # repeat the list of trash indices cyclicly
for i in range(L-num_trash):
result.append((repeated[i_permut + i], nums_compressed[i]))
return result
def QAE_Ansatz(L,num_trash, insert_barriers=False, parametrized_gate = "ry", entangling_gate = "cz"):
entanglement = [get_entangler_map(L,num_trash,i_permut) for i_permut in range(num_trash)]
return qiskit.circuit.library.TwoLocal(L,
parametrized_gate,
entangling_gate,
entanglement,
reps=num_trash,
insert_barriers=insert_barriers,
)
QAE_Ansatz(6,2,insert_barriers=True).draw("mpl")
QAE_Ansatz(5,2,insert_barriers=True).draw("mpl")
QAE_Ansatz(7,2,insert_barriers=True).draw("mpl")
QAE_Ansatz(6,3,insert_barriers=True).draw("mpl")
# more complicated and as far as i can tell exponential
def get_entangler_map_exp(L, num_trash, i_permut=0):
result = []
nums = list(range(L))
nums_compressed = nums.copy()[:L-num_trash]
nums_trash = nums.copy()[-num_trash:]
#print(nums, nums_compressed, nums_trash)
# combine all trash qubits with themselves
for trash_q in nums_trash[:-1]:
result.append((trash_q+1,trash_q))
# combine each of the trash qubits with every n-th
permutations = list(itertools.permutations(nums_trash))
for _,trash_q in enumerate(permutations[i_permut]):
for comp_q in nums_compressed[_%num_trash::num_trash]: # combine each trash_q with even or odd site
result.append((trash_q, comp_q))
return result
# Use Qiskit layer generator thingy because now it has everything it needs to get a gradient!
# Note that it has redundant Ry rotations at the end on the compression qubits, didnt find a clever way how to get rid of them, but this shouldnt change too much
# variational ansatz sub-circuit
ansatz = QAE_Ansatz(6,2)
# measurement circuit
qreg = qiskit.QuantumRegister(L, 'q')
creg = qiskit.ClassicalRegister(2, 'c')
measurement_circ = qiskit.QuantumCircuit(qreg, creg)
measurement_circ.measure(qreg[4], creg[0])
measurement_circ.measure(qreg[5], creg[1])
circ2 = ansatz + measurement_circ
circ2.decompose()
#print(circ2)
#measurement_circ.draw("mpl")
circ2.draw("mpl")
qi_qasm = QuantumInstance(Aer.get_backend('qasm_simulator'), shots=10)
qi_sv = QuantumInstance(Aer.get_backend('statevector_simulator'))
qnn4 = CircuitQNN(circ2, [], circ2.parameters, sparse=False, quantum_instance=qi_qasm)
input4 = np.random.rand(qnn4.num_inputs)
weights4 = np.random.rand(qnn4.num_weights)
qnn4.forward(input4, weights4)
qnn4.backward(input4, weights4)
from qiskit.algorithms.variational_algorithm import VariationalAlgorithm
VariationalAlgorithm(ansatz = circ2, optimizer = None)
qiskit.__version__
import qiskit
import numpy as np
L = 6
def create_map(L):
return [(i,i+1) for i in np.arange(L-1)]
map1 = create_map(L)
print(map1)
map_by_hand1 = [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5)]
print(map_by_hand1)
map1 == map_by_hand1
ansatz = qiskit.circuit.library.TwoLocal(L,"ry","cz",[map_by_hand1],reps=2, insert_barriers=True)
ansatz.draw("mpl")
ansatz = qiskit.circuit.library.TwoLocal(L,"ry","cz",[map1],reps=2, insert_barriers=True)
ansatz.draw("mpl")
map1 = get_entangler_map(6,2)
map2 = get_entangler_map(6,2,1)
print(map1)
print(map2)
ansatz = qiskit.circuit.library.TwoLocal(L,"ry","cz",[map1,map2],reps=2, insert_barriers=True)
ansatz.draw("mpl")
ansatz = qiskit.circuit.library.TwoLocal(L,"ry","cz",[map_by_hand],reps=2, insert_barriers=True)
ansatz.draw("mpl")
|
https://github.com/Qottmann/Quantum-anomaly-detection
|
Qottmann
|
import time
import numpy as np
import qiskit
from qiskit.opflow import X,Z,I
from qiskit.opflow.state_fns import StateFn, CircuitStateFn
from modules.utils import *
import matplotlib.pyplot as plt
from scipy import sparse
import scipy.sparse.linalg.eigen.arpack as arp
%matplotlib inline
# first need a gate that is the exponential of ZZ, which shouldnt be too difficult since it can be done analytically
L = 2
qreg = qiskit.QuantumRegister(L, 'q')
creg = qiskit.ClassicalRegister(2, 'c')
circ = qiskit.QuantumCircuit(qreg, creg)
circ.rz()
tau = qiskit.circuit.Parameter("tau")
diaglist = [tau, -tau, -tau, tau]
diaglist
dt = 0.05
diaglist = [np.exp(dt), np.exp(-dt), np.exp(-dt), np.exp(dt)]
qiskit.circuit.library.Diagonal(diaglist)
import numpy as np
from qiskit.opflow import I, X, Y, Z, H, CX, Zero, ListOp, PauliExpectation, PauliTrotterEvolution, CircuitSampler, MatrixEvolution, Suzuki
from qiskit.circuit import Parameter
from qiskit import BasicAer
L=5
anti = -1
gx = 1e-2
gz = 1e-2
two_qubit_H2 = QHIsing(L,anti,np.float32(gx),np.float32(gz))
two_qubit_H2 = 1 * (X^X^X^Z^X + Z^Z^Z^Z^X)
two_qubit_H2
evo_time = Parameter('θ')
evolution_op = (evo_time * two_qubit_H2).exp_i()
h2_measurement = StateFn(two_qubit_H2).adjoint()
#print(h2_measurement)
evo_and_meas = h2_measurement @ evolution_op @ Zero
#print(evo_and_meas)
trotterized_op = PauliTrotterEvolution(trotter_mode=Suzuki(order=2, reps=1)).convert(evo_and_meas)
bound = trotterized_op.bind_parameters({evo_time: .5})
diagonalized_meas_op = PauliExpectation().convert(trotterized_op)
evo_time_points = [1,2,3,4]
h2_trotter_expectations = diagonalized_meas_op.bind_parameters({evo_time: evo_time_points})
h2_trotter_expectations.eval()
# Ops
H = QHIsing(L,anti,np.float32(gx),np.float32(gz))
H_meas = StateFn(H).adjoint()
mag = QMag(L,anti)
qiskit.opflow.One^5
qiskit.opflow.VectorStateFn(qiskit.opflow.One^5)
asd = H_meas @ qiskit.opflow.One^5
asd.eval()
evo_time = qiskit.circuit.Parameter('θ') # note that we will have to bind it laterevo_time = Parameter('θ')
evolution_op = (evo_time * H).exp_i() #* 1j
# expH = suzi.convert(H)
evo_and_meas = H_meas @ evolution_op
trotterized_op = qiskit.opflow.evolutions.PauliTrotterEvolution(trotter_mode=qiskit.opflow.evolutions.Suzuki(order=2, reps=1)).convert(evo_and_meas)
bound = trotterized_op.bind_parameters({evo_time: .5})
bound.eval()
diagonalized_meas_op = qiskit.opflow.PauliExpectation().convert(trotterized_op)
h2_trotter_expectations = diagonalized_meas_op.bind_parameters({evo_time: 1})
h2_trotter_expectations.eval()
H.__class__, evolution_op.__class__
def imaginary_time_evolution(tau, L, gx, gz, anti=-1):
H = QHIsing(L,anti,np.float32(gx),np.float32(gz))
evo_time = qiskit.circuit.Parameter('tau') # note that we will have to bind it later
evolution_op = (tau * 1j * H).exp_i()
trotterized_op = qiskit.opflow.evolutions.PauliTrotterEvolution(trotter_mode=qiskit.opflow.evolutions.Suzuki(order=2, reps=1)).convert(H_meas @ evolution_op)
return trotterized_op
trott_op = imaginary_time_evolution(tau=1, L=5, gx=1e-2, gz=1e-2, anti=-1)
trott_op.bind_param({evo_})
bound = trott_op.bind_parameters({evo_time: 1})
bound.to_circuit().draw()
mag_res = ~StateFn(mag) @ StateFn(trott_op)
mag_res.eval()
bound.eval()
bound = trotterized_op.bind_parameters({evo_time: tau})
# this is a dummy circuit for the sake of checking how I can combine the evolved state with a circuit later
circ = qiskit.circuit.library.TwoLocal(5,"rx","cz", reps=1,entanglement="linear")
circ.draw()
circ_fn = CircuitStateFn(circ)
print(circ_fn)
evolved_processed = evolution_op @ circ_fn # first the imaginary time evolution, then the dummy processing
print(evolved_processed )
trotterized_op = qiskit.opflow.evolutions.PauliTrotterEvolution(trotter_mode=qiskit.opflow.evolutions.Suzuki(order=2, reps=1)).convert(evolved_processed)
print(trotterized_op)
bound = trotterized_op.bind_parameters({evo_time: .5j})
bound.to_circuit().draw()
# ED result
Smag = Mag(L,anti)
init_state, E, ham = ising_groundstate(L, anti, np.float(gx), np.float(gz))
Sen = E
Smags = init_state.T.conj()@Smag@init_state #Magnetization with Numpy results
|
https://github.com/Qottmann/Quantum-anomaly-detection
|
Qottmann
|
import time
import numpy as np
import qiskit
from qiskit.opflow import X,Z,I
from qiskit.opflow.state_fns import StateFn, CircuitStateFn
import matplotlib.pyplot as plt
from scipy import sparse
import scipy.sparse.linalg.eigen.arpack as arp
%matplotlib inline
# copypaste niccolo's code
def QNKron(N,op1,op2,pos):
'''
Tensor product operator (Qiskit Pauli operators)
returns tensor product of op1,op2 on sites pos,pos+1 and identity on remaining sites
N:number of sites
op1,op2: Pauli operators on neighboring sites
pos: site to insert op1
'''
temp=np.array([I]*(N))
temp[pos]=op1
if pos!=(N-1):
temp[pos+1]=op2
mat=1
for j in range(N):
mat=mat^temp[j]
return mat
def QHIsing(N,lam,p):
'''
Quantum Ising Hamiltonian (1D) with transverse field (Qiskit Pauli operators)
N:number of sites
lam: transverse field)
'''
H=-QNKron(N,Z,Z,0)-lam*QNKron(N,X,I,0)-p*QNKron(N,Z,I,0)
for i in range(1,N-1):
H=H-QNKron(N,Z,Z,i)-lam*QNKron(N,X,I,i)-p*QNKron(N,Z,I,i)
H=H-lam*QNKron(N,X,I,N-1)-p*QNKron(N,Z,I,N-1)
return H
def NKron(N,op1,op2,pos):
'''
Tensor product operator
returns tensor product of op1,op2 on sites pos,pos+1 and identity on remaining sites
N:number of sites
op1,op2: Pauli operators on neighboring sites
pos: site to insert op1
'''
ide=np.eye(2)
temp=np.array([ide]*(N),dtype=np.complex128)
temp[pos,:,:]=op1
# if pos!=(N-1):
temp[(pos+1)%N,:,:]=op2
mat=1
for j in range(N):
mat=np.kron(mat,temp[j])
return mat
def Mag(N): #magnetization operator (numpy array)
sz=np.array([[1,0],[0,-1]])
M=np.zeros((2**N,2**N))
for i in range(N):
M=M+NKron(N,sz,np.eye(2),i)
return M/N
def QMag(N): #magnetization operator (Qiskit operator)
M=QNKron(N,Z,I,0)
for i in range(1,N):
M=M+QNKron(N,Z,I,i)
return M/N
def ising_groundstate(L, J, g, p):
"""For comparison: obtain ground state energy from exact diagonalization.
Exponentially expensive in L, only works for small enough `L` <~ 20.
"""
if L >= 20:
warnings.warn("Large L: Exact diagonalization might take a long time!")
# get single site operaors
sx = sparse.csr_matrix(np.array([[0., 1.], [1., 0.]]))
sz = sparse.csr_matrix(np.array([[1., 0.], [0., -1.]]))
id = sparse.csr_matrix(np.eye(2))
sx_list = [] # sx_list[i] = kron([id, id, ..., id, sx, id, .... id])
sz_list = []
for i_site in range(L):
x_ops = [id] * L
z_ops = [id] * L
x_ops[i_site] = sx
z_ops[i_site] = sz
X = x_ops[0]
Z = z_ops[0]
for j in range(1, L):
X = sparse.kron(X, x_ops[j], 'csr')
Z = sparse.kron(Z, z_ops[j], 'csr')
sx_list.append(X)
sz_list.append(Z)
H_zz = sparse.csr_matrix((2**L, 2**L))
H_x = sparse.csr_matrix((2**L, 2**L))
for i in range(L - 1):
H_zz = H_zz + J*sz_list[i] * sz_list[(i + 1) % L]
for i in range(L):
H_x = H_x + g*sx_list[i] +p*sz_list[i]
H = - H_zz - H_x
E, V = arp.eigsh(H, k=1, which='SA', return_eigenvectors=True, ncv=20)
return V[:,0], E[0], H
def sort_vals(vals):
""" vals is (unsorted) dictionary of parameters from VQE ansatz circuit, this returns sorted values as list """
indices = np.array([_.index for _ in vals]) # unordered list of indices from the ParameterVectorElement(Theta(INDEX))
vals_sorted = np.array([vals[_] for _ in vals]) # unordered list of values (but same ordering as indices)
return vals_sorted[np.argsort(indices)]
def init_vqe(vals):
return qiskit.circuit.library.EfficientSU2(L, reps=3).assign_parameters(sort_vals(vals))
L = 6
VQE_vals = np.load(f'params_VQE_ising_N{L}.npy', allow_pickle=True).item()
lambdas = np.array([_ for _ in VQE_vals]) # list of lambda values (the items in the dictionary)
# note that Rike calls them gs
mag = QMag(L) #magnetization operator (Qiskit)
Smag=Mag(L) #magnetization operator (numpy)
# the ~ is the adjoint, but also it turns the is_measurement attribute to True
~StateFn(mag)
# state is technically a circuit, that prepares the ground state via VQE circuit
#state.draw() # uncomment to see, but is very long
Qen=np.zeros(len(lambdas)); Sen=np.zeros(len(lambdas)) #energies
Qmags=np.zeros(len(lambdas)); Smags=np.zeros(len(lambdas)) #magnetizations
for j,lambda0 in enumerate(lambdas):
print(lambda0)
H = QHIsing(L,np.float32(lambda0),1e-4) # build Hamiltonian Op
state = init_vqe(VQE_vals[lambda0])
StateFn(state)
meas_outcome = ~StateFn(mag) @ StateFn(state)
Qmags[j]=meas_outcome.eval()
e_outcome = ~StateFn(H) @ StateFn(state)
Qen[j]=e_outcome.eval()
init_state, E, ham = ising_groundstate(L, 1., np.float(lambda0),1e-4)
Sen[j]=E
Smags[j]=init_state.T.conj()@Smag@init_state #Magnetization with Numpy results
lambdas = np.array([_ for _ in VQE_vals],dtype=np.float) # list of lambda values (the items in the dictionary)
plt.figure(1,dpi=220)
plt.scatter(lambdas,Sen)
plt.scatter(lambdas,Qen)
plt.xscale("log")
plt.ylabel("GS energy")
plt.xlabel("Mag. field")
plt.legend(["Sparse","Qiskit"])
plt.grid()
print(Sen,Qen)
plt.figure(2,dpi=220)
plt.scatter(lambdas,Smags)
plt.scatter(lambdas,Qmags)
plt.xscale('log')
plt.ylabel("Magnetization")
plt.xlabel("Mag. field")
plt.legend(["Sparse","Qiskit"])
plt.grid()
print(Smags,Qmags)
|
https://github.com/Qottmann/Quantum-anomaly-detection
|
Qottmann
|
import time
import numpy as np
import qiskit
from qiskit.opflow import X,Z,I
from qiskit.opflow.state_fns import StateFn, CircuitStateFn
import matplotlib.pyplot as plt
from scipy import sparse
import scipy.sparse.linalg.eigen.arpack as arp
%matplotlib inline
# copypaste niccolo's code
def QNKron(N,op1,op2,pos):
'''
Tensor product operator (Qiskit Pauli operators)
returns tensor product of op1,op2 on sites pos,pos+1 and identity on remaining sites
N:number of sites
op1,op2: Pauli operators on neighboring sites
pos: site to insert op1
'''
temp=np.array([I]*(N))
temp[pos]=op1
if pos!=(N-1):
temp[pos+1]=op2
mat=1
for j in range(N):
mat=mat^temp[j]
return mat
def QHIsing(N,lam,p):
'''
Quantum Ising Hamiltonian (1D) with transverse field (Qiskit Pauli operators)
N:number of sites
lam: transverse field)
'''
H=-QNKron(N,Z,Z,0)-lam*QNKron(N,X,I,0)-p*QNKron(N,Z,I,0)
for i in range(1,N-1):
H=H-QNKron(N,Z,Z,i)-lam*QNKron(N,X,I,i)-p*QNKron(N,Z,I,i)
H=H-lam*QNKron(N,X,I,N-1)-p*QNKron(N,Z,I,N-1)
return H
def NKron(N,op1,op2,pos):
'''
Tensor product operator
returns tensor product of op1,op2 on sites pos,pos+1 and identity on remaining sites
N:number of sites
op1,op2: Pauli operators on neighboring sites
pos: site to insert op1
'''
ide=np.eye(2)
temp=np.array([ide]*(N),dtype=np.complex128)
temp[pos,:,:]=op1
# if pos!=(N-1):
temp[(pos+1)%N,:,:]=op2
mat=1
for j in range(N):
mat=np.kron(mat,temp[j])
return mat
def Mag(N): #magnetization operator (numpy array)
sz=np.array([[1,0],[0,-1]])
M=np.zeros((2**N,2**N))
for i in range(N):
M=M+NKron(N,sz,np.eye(2),i)
return M/N
def QMag(N): #magnetization operator (Qiskit operator)
M=QNKron(N,Z,I,0)
for i in range(1,N):
M=M+QNKron(N,Z,I,i)
return M/N
def ising_groundstate(L, J, g, p):
"""For comparison: obtain ground state energy from exact diagonalization.
Exponentially expensive in L, only works for small enough `L` <~ 20.
"""
if L >= 20:
warnings.warn("Large L: Exact diagonalization might take a long time!")
# get single site operaors
sx = sparse.csr_matrix(np.array([[0., 1.], [1., 0.]]))
sz = sparse.csr_matrix(np.array([[1., 0.], [0., -1.]]))
id = sparse.csr_matrix(np.eye(2))
sx_list = [] # sx_list[i] = kron([id, id, ..., id, sx, id, .... id])
sz_list = []
for i_site in range(L):
x_ops = [id] * L
z_ops = [id] * L
x_ops[i_site] = sx
z_ops[i_site] = sz
X = x_ops[0]
Z = z_ops[0]
for j in range(1, L):
X = sparse.kron(X, x_ops[j], 'csr')
Z = sparse.kron(Z, z_ops[j], 'csr')
sx_list.append(X)
sz_list.append(Z)
H_zz = sparse.csr_matrix((2**L, 2**L))
H_x = sparse.csr_matrix((2**L, 2**L))
for i in range(L - 1):
H_zz = H_zz + J*sz_list[i] * sz_list[(i + 1) % L]
for i in range(L):
H_x = H_x + g*sx_list[i] +p*sz_list[i]
H = - H_zz - H_x
E, V = arp.eigsh(H, k=1, which='SA', return_eigenvectors=True, ncv=20)
return V[:,0], E[0], H
def sort_vals(vals):
""" vals is (unsorted) dictionary of parameters from VQE ansatz circuit, this returns sorted values as list """
indices = np.array([_.index for _ in vals]) # unordered list of indices from the ParameterVectorElement(Theta(INDEX))
vals_sorted = np.array([vals[_] for _ in vals]) # unordered list of values (but same ordering as indices)
return vals_sorted[np.argsort(indices)]
def init_vqe(vals):
return qiskit.circuit.library.EfficientSU2(L, reps=3).assign_parameters(sort_vals(vals))
L = 6
VQE_vals = np.load(f'params_VQE_ising_N{L}.npy', allow_pickle=True).item()
lambdas = np.array([_ for _ in VQE_vals]) # list of lambda values (the items in the dictionary)
# note that Rike calls them gs
mag = QMag(L) #magnetization operator (Qiskit)
Smag=Mag(L) #magnetization operator (numpy)
# the ~ is the adjoint, but also it turns the is_measurement attribute to True
~StateFn(mag)
# state is technically a circuit, that prepares the ground state via VQE circuit
#state.draw() # uncomment to see, but is very long
Qen=np.zeros(len(lambdas)); Sen=np.zeros(len(lambdas)) #energies
Qmags=np.zeros(len(lambdas)); Smags=np.zeros(len(lambdas)) #magnetizations
for j,lambda0 in enumerate(lambdas):
print(lambda0)
H = QHIsing(L,np.float32(lambda0),1e-4) # build Hamiltonian Op
state = init_vqe(VQE_vals[lambda0])
StateFn(state)
meas_outcome = ~StateFn(mag) @ StateFn(state)
Qmags[j]=meas_outcome.eval()
e_outcome = ~StateFn(H) @ StateFn(state)
Qen[j]=e_outcome.eval()
init_state, E, ham = ising_groundstate(L, 1., np.float(lambda0),1e-4)
Sen[j]=E
Smags[j]=init_state.T.conj()@Smag@init_state #Magnetization with Numpy results
lambdas = np.array([_ for _ in VQE_vals],dtype=np.float) # list of lambda values (the items in the dictionary)
plt.figure(1,dpi=220)
plt.plot(lambdas,Sen,".--")
plt.plot(lambdas,Qen)
plt.xscale("log")
plt.ylabel("GS energy")
plt.xlabel("Mag. field")
plt.legend(["Sparse","Qiskit"])
plt.grid()
print(Sen,Qen)
plt.figure(2,dpi=220)
plt.scatter(lambdas,Smags)
plt.scatter(lambdas,Qmags)
plt.xscale('log')
plt.ylabel("Magnetization")
plt.xlabel("Mag. field")
plt.legend(["Sparse","Qiskit"])
plt.grid()
print(Smags,Qmags)
|
https://github.com/Qottmann/Quantum-anomaly-detection
|
Qottmann
|
# -*- coding: utf-8 -*-
"""
Created on Mon May 31 22:57:39 2021
@author: nbaldelli
"""
import time
import itertools
import numpy as np
from scipy.optimize import minimize, basinhopping
from qiskit import *
from qiskit.quantum_info import Statevector
from qiskit.algorithms.optimizers import COBYLA, L_BFGS_B, SPSA, SLSQP
from qiskit.algorithms import VQE
from qiskit.opflow.state_fns import StateFn, CircuitStateFn
from qiskit.opflow import CircuitSampler
from qiskit.providers.aer import StatevectorSimulator, AerSimulator
from qiskit.providers.aer.noise import NoiseModel
from qiskit.utils import QuantumInstance
from qiskit.ignis.mitigation.measurement import CompleteMeasFitter,complete_meas_cal
from modules.utils import *
import qiskit
from qiskit import IBMQ
#%%
IBMQ.load_account() # Load account from disk
IBMQ.providers() # List all available providers
provider = IBMQ.get_provider(hub='ibm-q')
print(provider.backends)
real_backend = provider.backends(simulator=False, operational=True)[6]
L=5
mag = QMag(L,-1) #magnetization operator (Qiskit)
~StateFn(mag)
gx_vals = np.logspace(-2,2,10)[::-1]
opt_params_noiseless=[]
opt_params_noisy=[]
Qmags=np.zeros(len(gx_vals))
load=False
##############################################################################
#NOISY SIMULATION
backend = qiskit.providers.aer.AerSimulator.from_backend(real_backend)
coupling_map = backend.configuration().coupling_map
noise_model = NoiseModel.from_backend(backend)
optimizer = SPSA(maxiter=1000)
reps=1
ansatz = qiskit.circuit.library.EfficientSU2(L, reps=reps)
# ansatz = qiskit.circuit.library.TwoLocal(L,rotation_blocks="ry", entanglement_blocks=entanglement_blocks, entanglement=entanglement, reps=reps)
ansatz.draw("mpl")
q_instance = QuantumInstance(backend, coupling_map=coupling_map, noise_model=noise_model, measurement_error_mitigation_cls=CompleteMeasFitter, cals_matrix_refresh_period=30 )
gz=1e-3
filename = f'data/params_VQE_Ising_L{L:.0f}_anti_N_reps{reps}_gz{gz}.npz'
##%%
if load==False:
for i,gx in enumerate(gx_vals):
print('gx: %.2f' %(gx))
if i != 0:
vqe = VQE(ansatz=ansatz, initial_point = opt_params_noisy[-1], optimizer=optimizer, quantum_instance=q_instance)
else:
vqe = VQE(ansatz=ansatz, optimizer=optimizer, quantum_instance=q_instance)
H = QHIsing(L,-1,np.float32(gx),np.float32(gz))
result = vqe.compute_minimum_eigenvalue(H) #ED with Qiskit VQE
opt_params_noisy.append(sort_params(result.optimal_parameters))
np.savez(filename, opt_params=opt_params_noisy)
|
https://github.com/Qottmann/Quantum-anomaly-detection
|
Qottmann
|
from matplotlib import pyplot as plt
import numpy as np
import matplotlib.gridspec as gridspec
path = "data/BH_phase-diagrams-and-loss/data_rike/"
x = np.load(path + "x.npy")
y = np.load(path + "y.npy")
es = np.load(path + "es.npy")
dd = np.load(path + "dd.npy")
cost0 = np.load(path + "cost_bh_L12_trash2_d48_v5_seed17.npy")
cost1 = np.load(path + "cost_bh_L12_trash4_d25_v47_seed16.npy")
cost2 = np.load(path + "cost_bh_L12_trash6_d10_v10_seed22.npy")
vs = np.logspace(-2,2,50)
ds = np.linspace(-0.95,0.95,50)
cbar = np.zeros((2,2), dtype="object")
fig,axs = plt.subplots(ncols=2, nrows=2,figsize=(10,6),sharex=True, sharey=True)
cmap = plt.get_cmap("plasma") #'viridis'
cbar_labelsize = 14
cbar = np.zeros((2,2), dtype="object")
ax = axs[0,0]
im = ax.pcolormesh(x,y,dd.T, cmap=cmap, shading="auto",rasterized=True) #,rasterized=True necessary for pdf export
ax.set_xscale("log")
cbar[0,0] = fig.colorbar(im, ax=ax)
cbar[0,0].ax.tick_params(labelsize=cbar_labelsize)
ax = axs[0,1]
im = ax.pcolormesh(x,y,es.T, cmap=cmap, shading="auto",rasterized=True)
ax.set_xscale("log")
cbar[0,1] = fig.colorbar(im, ax=ax)
cbar[0,1].ax.tick_params(labelsize=cbar_labelsize)
ax = axs[1,0]
im = ax.pcolormesh(x,y,cost2.T,vmin=0, cmap=cmap, shading="auto",rasterized=True)
ax.set_xscale("log")
cbar[1,0] = fig.colorbar(im, ax=ax)
cbar[1,0].ax.tick_params(labelsize=cbar_labelsize)
ax.plot(vs[10],ds[10],"x", color="magenta")
ax = axs[1,1]
im = ax.pcolormesh(x,y,cost1.T,vmin=0, cmap=cmap, shading="auto",rasterized=True)
ax.set_xscale("log")
cbar[1,1] = fig.colorbar(im, ax=ax)
cbar[1,1].ax.tick_params(labelsize=cbar_labelsize)
ax.plot(vs[47],ds[25],"x", color="magenta")
for ax in axs.flatten():
ax.tick_params(labelsize=14)
axs[1,0].set_xlabel(r"$V$",fontsize=18)
axs[1,1].set_xlabel(r"$V$",fontsize=18)
axs[0,0].set_ylabel(r"$\delta t$",fontsize=18)
axs[1,0].set_ylabel(r"$\delta t$",fontsize=18)
plt.tight_layout()
# has to happen after tight_layout()
axs[0,0].text(-0.25,0.9,"a", fontweight="bold", size=18, transform = axs[0,0].transAxes)
axs[1,0].text(-0.25,0.9,"c", fontweight="bold", size=18, transform = axs[1,0].transAxes)
axs[0,1].text(-0.1,0.9,"b", fontweight="bold", size=18, transform = axs[0,1].transAxes)
axs[1,1].text(-0.1,0.9,"d", fontweight="bold", size=18, transform = axs[1,1].transAxes)
cbar[0,0].ax.set_title("$\\langle n_i n_j \\rangle$")
axs[0,0].set_title("density-density correlations", fontsize=14)
cbar[0,1].ax.set_title("$\Delta \\lambda$")
axs[0,1].set_title("Entanglement spectrum degeneracy", fontsize=14)
cbar[1,0].ax.set_title("cost")
cbar[1,1].ax.set_title("cost")
plt.savefig("plots/BH_replot.pdf", bbox_inches='tight')
fig,aa = plt.subplots(figsize=(15,5.7),sharey="row",dpi=220)
ax00 = plt.subplot2grid(shape=(2,5), loc=(0, 0), rowspan=1, colspan=1, xticklabels=[])
ax10 = plt.subplot2grid(shape=(2,5), loc=(1, 0), rowspan=1, colspan=1, xticklabels=[])
ax01 = plt.subplot2grid(shape=(2,5), loc=(0, 1), rowspan=2, colspan=2, xticklabels=[], yticklabels=[])
ax02 = plt.subplot2grid(shape=(2,5), loc=(0, 3), rowspan=2, colspan=2, xticklabels=[], yticklabels=[])
cmap = plt.get_cmap("plasma") #'viridis'
cbar_labelsize = 10
cbar = np.zeros((2,2), dtype="object")
axs=np.array([[ax00,ax10],[ax01,ax02]])
print(axs[0,0])
ax = axs[0,0]
im = ax.pcolormesh(x,y,dd.T, cmap=cmap, shading="auto",rasterized=True) #,rasterized=True necessary for pdf export
#im.ax.tick_params(labelsize=cbar_labelsize)
ax.set_xscale("log")
cbar[0,0] = fig.colorbar(im, ax=ax)
cbar[0,0].ax.tick_params(labelsize=cbar_labelsize)
ax = axs[0,1]
im = ax.pcolormesh(x,y,es.T, cmap=cmap, shading="auto",rasterized=True)
ax.set_xscale("log")
cbar[0,1] = fig.colorbar(im, ax=ax)
cbar[0,1].ax.tick_params(labelsize=cbar_labelsize)
ax = axs[1,0]
im = ax.pcolormesh(x,y,cost2.T,vmin=0, cmap=cmap, shading="auto",rasterized=True)
ax.set_xscale("log")
cbar[1,0] = fig.colorbar(im, ax=ax)
cbar[1,0].ax.tick_params(labelsize=cbar_labelsize)
ax.plot(vs[10],ds[10],"X", color="magenta",markersize=15)
ax = axs[1,1]
im = ax.pcolormesh(x,y,cost1.T,vmin=0, cmap=cmap, shading="auto",rasterized=True)
ax.set_xscale("log")
cbar[1,1] = fig.colorbar(im, ax=ax)
cbar[1,1].ax.tick_params(labelsize=cbar_labelsize)
ax.plot(vs[47],ds[25],"X", color="magenta",markersize=15)
for ax in axs.flatten():
ax.tick_params(labelsize=10)
axs[1,0].set_xlabel(r"$V$",fontsize=12)
axs[0,1].set_ylabel(r"$\delta J$",fontsize=12)
axs[1,1].set_xlabel(r"$V$",fontsize=12)
axs[0,0].set_ylabel(r"$\delta J$",fontsize=12)
axs[1,0].set_ylabel(r"$\delta J$",fontsize=12)
#plt.tight_layout()
# has to happen after tight_layout()
axs[0,0].text(0.01,0.89,"a", fontweight="bold", size=18, transform = axs[0,0].transAxes, color="white")
axs[1,0].text(0.02,0.95,"c", fontweight="bold", size=18, transform = axs[1,0].transAxes)
axs[0,1].text(0.01,0.88,"b", fontweight="bold", size=18, transform = axs[0,1].transAxes)
axs[1,1].text(0.02,0.94,"d", fontweight="bold", size=18, transform = axs[1,1].transAxes)
axs[1,0].text(0.23,0.05,"MI", fontweight="bold", size=18, transform = axs[1,0].transAxes, color="white")
axs[1,0].text(0.23,0.8,"TMI", fontweight="bold", size=18, transform = axs[1,0].transAxes)
axs[1,0].text(0.77,0.5,"CDW", fontweight="bold", size=18, transform = axs[1,0].transAxes)
#cbar[0,0].ax.set_title("$\\langle n_i n_j \\rangle$")
cbar[0,0].ax.set_title("$O_{CDW}$")
axs[0,0].set_title("DMRG", fontsize=14)
cbar[0,1].ax.set_title("ES")
#axs[0,1].set_title("Entanglement spectrum degeneracy", fontsize=14)
cbar[1,0].ax.set_title("cost")
cbar[1,1].ax.set_title("cost")
plt.savefig("plots/BH_replot2.pdf", bbox_inches='tight')
fig, ax = plt.subplots(figsize=(6,4))
im = ax.pcolormesh(x,y,cost2.T,vmin=0, cmap=cmap, shading="auto",rasterized=True)
ax.set_xscale("log")
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=cbar_labelsize)
ax.set_title("Anomaly Syndrome", fontsize=14)
cbar.ax.set_title("cost")
ax.plot(vs[10],ds[10],"x", markersize=15, color="magenta")
ax.text(vs[10],ds[10]-0.25,"Training example", color="magenta")
ax.tick_params(labelsize=14)
#ax.set_xlabel(r"$V$",fontsize=18)
#ax.set_ylabel(r"$\delta t$",fontsize=18)
plt.tight_layout()
plt.savefig("plots/BH_ex_for_presentation.png", dpi=500, bbox_inches='tight')
|
https://github.com/Qottmann/Quantum-anomaly-detection
|
Qottmann
|
import sys
sys.path.insert(1, '..') # sets the import path to the parent folder
import time
import datetime
import numpy as np
from matplotlib import pyplot as plt
anti = -1
L = 5
num_trash = 2
name = "jakarta_execute"
filename = '../data/jakarta_execute'
# load data from simulation and experiment
temp = np.load(filename + "_executed_-real-device-True.npz",allow_pickle=True)
gx_list = temp["gx_list"]
gz = 0
gz_list = [0]
Qmag_sim = temp["Qmag_sim"]
QZ_sim = temp["QZ_sim"]
Qmag_device = temp["Qmag_device"]
QZ_device = temp["QZ_device"]
Qen_sim = temp["Qen_sim"]
Sen = temp["Sen"]
Smag = temp["Smag"]
opt_params = temp["opt_params"]
paramss = temp["paramss"]; valuess = temp["valuess"]; countss = temp["countss"]
run1 = np.load(filename + "run1_device2.npz",allow_pickle=True) # alt: run1_device2
sim1 = np.load(filename + "run1_sim2.npz",allow_pickle=True)
run2 = np.load(filename + "run2_device.npz",allow_pickle=True) # alt: _overnight
sim2 = np.load(filename + "run2_sim.npz",allow_pickle=True)
fig, axs = plt.subplots(nrows=2,figsize=(6,6),sharex=True,gridspec_kw={'height_ratios': [2, 2]})
ax = axs[0]
cost_sim = sim1["cost"]
ax.plot(gx_list, cost_sim,".:", label="cost noisy sim.")
cost_device = run1["cost"]
ax.plot(gx_list, cost_device,".:", label="cost ibmq_jakarta")
ax.plot(gx_list, np.abs(Qmag_sim),"x--", label="$|\hat{S}|$ noisy sim.")
ax.plot(gx_list, np.abs(Qmag_device),"x--", label="$|\hat{S}|$ ibmq_jakarta")
ax.set_xscale("log")
ax.plot(gx_list[0],cost_device[0], "X",markersize=20,alpha=0.4,color="magenta")
ax = axs[1]
cost_sim = sim2["cost"]
ax.plot(gx_list, cost_sim,".:", label="cost noisy sim.")
cost_device = run2["cost"]
ax.plot(gx_list, cost_device,".:", label="cost ibmq_jakarta")
ax.plot(gx_list, np.abs(Qmag_sim),"x--", label="$|\hat{S}|$ noisy sim.")
ax.plot(gx_list, np.abs(Qmag_device),"x--", label="$|\hat{S}|$ ibmq_jakarta")
ax.set_xscale("log")
ax.plot(gx_list[-1],cost_device[-1],"X",markersize=16,alpha=0.4,color="magenta", label= "Training point")
for ax in axs:
ax.tick_params(labelsize=14)
axs[1].legend() #ncol=5
axs[-1].set_xlabel("$g_x$", fontsize=14)
plt.tight_layout()
axs[0].text(0.01,0.9,"a", fontweight="bold", size=18, transform = axs[0].transAxes)
axs[1].text(0.01,0.9,"b", fontweight="bold", size=18, transform = axs[1].transAxes)
plt.savefig("../plots/" + name + "_mainplot.png", bbox_inches='tight')
plt.savefig("../plots/" + name + "_mainplot.pdf", bbox_inches='tight')
run1 = np.load(filename + "run1_device.npz",allow_pickle=True) # alt: run1_device2
sim1 = np.load(filename + "run1_sim.npz",allow_pickle=True)
run2 = np.load(filename + "run2_device_overnight.npz",allow_pickle=True) # alt: run2_device_
sim2 = np.load(filename + "run2_sim.npz",allow_pickle=True)
fig, axs = plt.subplots(nrows=2,figsize=(6,6),sharex=True,gridspec_kw={'height_ratios': [2, 2]})
ax = axs[0]
cost_sim = sim1["cost"]
ax.plot(gx_list, cost_sim,".:", label="cost noisy sim.")
cost_device = run1["cost"]
ax.plot(gx_list, cost_device,".:", label="cost ibmq_jakarta")
ax.plot(gx_list, np.abs(Qmag_sim),"x--", label="$|\hat{S}|$ noisy sim.")
ax.plot(gx_list, np.abs(Qmag_device),"x--", label="$|\hat{S}|$ ibmq_jakarta")
ax.set_xscale("log")
ax.plot(gx_list[0],cost_device[0], "X",markersize=20,alpha=0.4,color="magenta")
ax = axs[1]
cost_sim = sim2["cost"]
ax.plot(gx_list, cost_sim,".:", label="cost noisy sim.")
cost_device = run2["cost"]
ax.plot(gx_list, cost_device,".:", label="cost ibmq_jakarta")
ax.plot(gx_list, np.abs(Qmag_sim),"x--", label="$|\hat{S}|$ noisy sim.")
ax.plot(gx_list, np.abs(Qmag_device),"x--", label="$|\hat{S}|$ ibmq_jakarta")
ax.set_xscale("log")
ax.plot(gx_list[-1],cost_device[-1],"X",markersize=20,alpha=0.4,color="magenta", label= "Training point")
for ax in axs:
ax.tick_params(labelsize=14)
axs[1].legend() #ncol=5
axs[-1].set_xlabel("$g_x$", fontsize=14)
plt.tight_layout()
axs[0].text(0.01,0.9,"a", fontweight="bold", size=18, transform = axs[0].transAxes)
axs[1].text(0.01,0.9,"b", fontweight="bold", size=18, transform = axs[1].transAxes)
plt.savefig("../plots/" + name + "_mainplot_alt.png", bbox_inches='tight')
plt.savefig("../plots/" + name + "_mainplot_alt.pdf", bbox_inches='tight')
|
https://github.com/Qottmann/Quantum-anomaly-detection
|
Qottmann
|
import time
import datetime
import numpy as np
from matplotlib import pyplot as plt
import qiskit
from qiskit import *
from qiskit.opflow import X,Z,I
from qiskit.opflow.state_fns import StateFn, CircuitStateFn
from qiskit.providers.aer import StatevectorSimulator, AerSimulator
from qiskit.algorithms import VQE
from qiskit.algorithms.optimizers import COBYLA, SLSQP, SPSA
from qiskit.opflow import CircuitSampler
from qiskit.ignis.mitigation.measurement import CompleteMeasFitter # you will need to pip install qiskit-ignis
from qiskit.ignis.mitigation.measurement import complete_meas_cal
from scipy import sparse
import scipy.sparse.linalg.eigen.arpack as arp
from modules.utils import *
anti = -1
L = 5
num_trash = 2
name = "ibmq_antiferro-1D-load_bogota-optimize-20points" # 01.06.2021 experiment
filename = "data/noisy_VQE_maxiter-500_Ising_L5_anti_-1_20" #"data/noisy_VQE_maxiter-100_Ising_L5_anti_-1_20_recycle"
print("filename: ", filename, "notebook name: ", name)
# where to get the simulated thetas values from? needs to contain a thetas_mitigated array
filename_simulated_thetas = 'data/ibmq_antiferro-1D-load_bogota-optimize_thetas-loss-cost_run2.npz'
load = True
recompute = False # whether or not to recompute Magnetization, makes sense on device
# this is noisy simulation data
L = 5
num_trash = 2
anti = -1
VQE_params = np.load(filename + ".npz", allow_pickle=True)
pick = np.arange(0,len(VQE_params['gx_list']))
gx_list = VQE_params['gx_list'][pick]
gz_list = VQE_params['gz_list'][pick]
opt_params = VQE_params['opt_params'][pick]
Qmags = VQE_params["Qmag"][pick]
Qen = VQE_params["Qen"][pick]
Sen = VQE_params["Sen"][pick]
Smags = VQE_params["Smag"][pick]
gx_vals = np.unique(gx_list)
gz_vals = np.unique(gz_list)
# these are the results from using precalculated VQE values from noisy simulation on the real device (ibmq_bogota)
if load:
temp = np.load("data/" + name + "executed_mags-Es.npz",allow_pickle=True)
Qmags_executed = temp["Qmags"]
# these are values actually obtained with VQE on ibmq_rome
VQE_params = np.load("data/rome_VQE_maxiter-30_Ising_L5_anti_-1_20_recycle.npz", allow_pickle=True)
print(VQE_params.files)
pick = np.arange(0,len(VQE_params['gx_list']))
gx_list2 = VQE_params['gx_list'][pick]
gz_list2 = VQE_params['gz_list'][pick]
opt_params2 = VQE_params['opt_params'][pick]
Qmags2 = VQE_params["Qmag"][pick]
Qen2 = VQE_params["Qen"][pick]
Sen2 = VQE_params["Sen"][pick]
Smags2 = VQE_params["Smag"][pick]
gx_vals2 = np.unique(gx_list2)
gz_vals2 = np.unique(gz_list2)
fig, axs = plt.subplots(ncols=2,figsize=(14,4))
ax=axs[0]
ax.plot(gx_list, Qmags_executed,"x--", label="simulated VQE on ibmq_bogota")
ax.plot(gx_list2, Qmags2,".:", label = "full mag on ibmq_rome")
ax.set_xscale("log")
ax.legend()
ax=axs[1]
ax.plot(gx_list, abs(Qmags_executed),"x--", label="simulated VQE on ibmq_bogota")
ax.plot(gx_list2, abs(Qmags2),".:", label = "full mag on ibmq_rome")
ax.set_xscale("log")
ax.legend()
fig, axs = plt.subplots(ncols=2, figsize=(10,5))
ax = axs[0]
ax.plot(gx_list, Smags,"x--", label="ED")
ax.plot(gx_list, Qmags,"x--", label="VQE simu w/ noise")
ax.set_xscale("log")
if recompute or load:
ax.plot(gx_list, Qmags_executed,"x--", label="IBMQ")
ax.legend()
ax.set_title("$M$")
ax = axs[1]
ax.plot(gx_list, Smags,"x--", label="ED")
ax.plot(gx_list, abs(Qmags),"x--", label="VQE simu w/ noise")
ax.set_xscale("log")
if recompute or load:
ax.plot(gx_list, abs(Qmags_executed),"x--", label="ibmq_bogota")
ax.legend()
ax.set_title("$\\mid M \\mid$")
##############################################################################
### II - Training ###########################################################
##############################################################################
cal_matrix = np.array([[0.982, 0.036, 0.032, 0.003],
[0.011, 0.959, 0., 0.036],
[0.007, 0.001, 0.962, 0.023],
[0., 0.004, 0.006, 0.938]]) # this was the one from the experiment
from qiskit.tools.visualization import *
temp = np.load("data/" + 'ibmq_antiferro-1D-load_simu' + "raw-vs-mitigated-counts.npz",allow_pickle=True)
plot_histogram([temp["raw_counts"].item(), temp["mitigated_counts"].item()], legend=['raw', 'mitigated'])
counts0 = temp["raw_counts"].item()
counts1 = temp["mitigated_counts"].item()
sortkeys = list(sorted([_ for _ in counts0]))
fig, ax = plt.subplots(figsize=(5,3))
plt.grid()
sortkeys = list(sorted([_ for _ in counts0]))
vals = np.array([counts0[_] for _ in sortkeys])
vals = vals/np.sum(vals)
ax.bar(sortkeys,vals, width=-0.3, align="edge", label="raw")
sortkeys = list(sorted([_ for _ in counts1]))
vals = np.array([counts1[_] for _ in sortkeys])
vals = vals/np.sum(vals)
ax.bar(sortkeys,vals, width=0.3, align="edge", label="mitigated")
ax.legend()
plt.tick_params(labelsize=14)
ax.set_ylabel("counts/shots", fontsize=20)
plt.tight_layout()
plt.savefig("plots/counts_mitigated-vs-raw.png")
plt.savefig("plots/counts_mitigated-vs-raw.pdf")
phis = opt_params
# translate to Rikes naming
gxs = gx_list
gzs = gz_list
phys_params= [0]
fig, ax = plt.subplots(ncols=1,figsize=(6,5))
ax.set_title("Mitigated results")
cost = np.load("data/" + "ibmq_antiferro-1D-load_simu" + "_thetas-loss-cost_run1.npz", allow_pickle=True)["cost_mitigated"]
ax.plot(gx_list, cost,".:", label="loss noisy sim.")
cost = np.load("data/" + name + "_thetas-loss-cost_run1.npz", allow_pickle=True)["cost_mitigated"]
ax.plot(gx_list, cost,".:", label="loss ibmq_bogota")
ax.plot(gx_list, Qmags,"x--", label="$\\langle \\sigma_z \\rangle$ noisy sim.")
ax.plot(gx_list, Qmags_executed,"x--", label="$\\langle \\sigma_z \\rangle$ ibmq_bogota")
ax.set_xscale("log")
for p in phys_params:
ax.plot(gx_list[p],cost[p],"o",alpha=0.3,color="magenta")
ax.legend()
phys_params = [-1]
fig, ax = plt.subplots(ncols=1,figsize=(6,5))
ax.set_title("Mitigated results")
cost = np.load("data/" + "ibmq_antiferro-1D-load_simu" + "_thetas-loss-cost_run2.npz", allow_pickle=True)["cost_mitigated"]
ax.plot(gx_list, cost,"x--", label="loss noisy sim.")
cost = np.load("data/" + name + "_thetas-loss-cost_run2.npz", allow_pickle=True)["cost_mitigated"]
ax.plot(gx_list, cost,".:", label="loss ibmq_bogota")
ax.plot(gx_list, abs(Qmags),"x--", label="$\\mid M \\mid$ noisy sim.")
ax.plot(gx_list, abs(Qmags_executed),".:", label="$\\mid M \\mid$ ibmq_bogota")
ax.set_xscale("log")
for p in phys_params:
ax.plot(gx_list[p],cost[p],"o",alpha=0.3,color="magenta")
ax.legend()
name
fig, axs = plt.subplots(nrows=2,figsize=(6,6),sharex=True,gridspec_kw={'height_ratios': [2, 2]})
ax = axs[0]
cost = np.load("data/" + "ibmq_antiferro-1D-load_simu" + "_thetas-loss-cost_run1.npz", allow_pickle=True)["cost_mitigated"]
ax.plot(gx_list, cost,".:")#, label="loss noisy sim.")
cost = np.load("data/" + name + "_thetas-loss-cost_run1.npz", allow_pickle=True)["cost_mitigated"]
ax.plot(gx_list, cost,".:")#, label="loss ibmq_bogota")
ax.plot(gx_list, Qmags,"x--", label="$\hat{S}$ noisy sim.")
ax.plot(gx_list, Qmags_executed,"x--", label="$\hat{S}$ ibmq_bogota")
ax.set_xscale("log")
ax.plot(gx_list[0],cost[0], "X",markersize=20,alpha=0.4,color="magenta")
ax = axs[1]
cost = np.load("data/" + "ibmq_antiferro-1D-load_simu" + "_thetas-loss-cost_run2.npz", allow_pickle=True)["cost_mitigated"]
ax.plot(gx_list, cost,"x--", label="cost noisy sim.")
cost = np.load("data/" + name + "_thetas-loss-cost_run2.npz", allow_pickle=True)["cost_mitigated"]
ax.plot(gx_list, cost,".:", label="cost ibmq_bogota")
ax.plot(gx_list, abs(Qmags),"x--", label="$\\mid \hat{S} \\mid$ noisy sim.")
ax.plot(gx_list, abs(Qmags_executed),".:", label="$\\mid \hat{S} \\mid$ ibmq_bogota")
ax.set_xscale("log")
ax.plot(gx_list[-1],cost[-1],"X",markersize=20,alpha=0.4,color="magenta")
for ax in axs:
ax.legend()
ax.tick_params(labelsize=14)
axs[-1].set_xlabel("$g_x$", fontsize=14)
plt.tight_layout()
axs[0].text(0.01,0.9,"a", fontweight="bold", size=18, transform = axs[0].transAxes)
axs[1].text(0.01,0.9,"b", fontweight="bold", size=18, transform = axs[1].transAxes)
plt.savefig("plots/" + name + "_mainplot.png", bbox_inches='tight')
plt.savefig("plots/" + name + "_mainplot.pdf", bbox_inches='tight')
fig, ax = plt.subplots(ncols=1,figsize=(6,5))
ax.set_title("Mitigated results")
cost = np.load("data/" + "ibmq_antiferro-1D-load_simu" + "_thetas-loss-cost_run2.npz", allow_pickle=True)["cost_mitigated"]
ax.plot(gx_list, cost,"x--", label="loss noisy sim.")
cost = np.load("data/" + name + "_thetas-loss-cost_run2.npz", allow_pickle=True)["cost_mitigated"]
ax.plot(gx_list, cost,".:", label="loss ibmq_bogota")
ax.plot(gx_list, abs(Qmags),"x--", label="order parameter")
#ax.plot(gx_list, abs(Qmags_executed),".:", label="$\\mid M \\mid$ ibmq_bogota")
ax.set_xscale("log")
ax.plot(gx_list[-1],cost[-1], "X",markersize=20,alpha=0.4,color="magenta")
ax.legend(fontsize=16)
ax.tick_params(labelsize=14)
ax.set_xlabel("$g_x$", fontsize=14)
plt.savefig("plots/IBMQ_presentation.png", dpi=400, bbox_inches='tight')
cost = np.load("data/ibmq_antiferro-2D_load-AD_10x10_noisy-rome-simu_thetas-loss-cost_run1.npy",allow_pickle=True)
qmag = np.load("data/noisy_rome_simu_VQE_maxiter-500_Ising_L5_anti_-1_10x10.npz", allow_pickle=True)["Qmag"].reshape(10,10)
x,y = np.logspace(-2,2,10), np.logspace(-2,2,10)
x,y = np.meshgrid(x,y)
fig,axs = plt.subplots(ncols=1, nrows=2,figsize=(5,5),sharex=True, sharey=True, squeeze=False)
cmap = plt.get_cmap("plasma") #'viridis'
cbar_labelsize = 14
cbar = np.zeros((2,2), dtype="object")
ax = axs[0,0]
im = ax.pcolormesh(x,y,qmag, cmap=cmap, shading="auto",rasterized=True) #,rasterized=True necessary for pdf export
cbar[0,0] = fig.colorbar(im, ax=ax)
cbar[0,0].ax.tick_params(labelsize=cbar_labelsize)
ax = axs[1,0]
im = ax.pcolormesh(x,y,cost,vmin=0, cmap=cmap, shading="auto",rasterized=True)
cbar[1,0] = fig.colorbar(im, ax=ax)
cbar[1,0].ax.tick_params(labelsize=cbar_labelsize)
#ax.plot(vs[47],ds[25],"x", color="magenta")
axs[1,0].plot(1e-2,1e-2,"x", markersize=12, color="magenta")
for ax in axs.flatten():
ax.set_xscale("log")
ax.set_yscale("log")
ax.tick_params(labelsize=14)
axs[1,0].set_xlabel(r"$g_x$",fontsize=18)
axs[0,0].set_ylabel(r"$g_z$",fontsize=18)
axs[1,0].set_ylabel(r"$g_z$",fontsize=18)
plt.tight_layout()
# has to happen after tight_layout()
axs[0,0].text(-0.25,0.9,"a", fontweight="bold", size=18, transform = axs[0,0].transAxes)
axs[1,0].text(-0.25,0.9,"b", fontweight="bold", size=18, transform = axs[1,0].transAxes)
axs[0,0].text(0.05,0.9,"Staggered Magnetization", size=14, transform = axs[0,0].transAxes)
axs[1,0].text(0.05,0.9,"cost", size=14, transform = axs[1,0].transAxes)
plt.savefig("plots/antiferro2D.pdf", bbox_inches='tight')
fig,axs = plt.subplots(ncols=2,figsize=(9,4),sharex=True, sharey=True)
cmap = plt.get_cmap("plasma") #'viridis'
cbar_labelsize = 14
cbar = np.zeros((2), dtype="object")
ax = axs[0]
im = ax.pcolormesh(x,y,qmag, cmap=cmap, shading="auto",rasterized=True) #,rasterized=True necessary for pdf export
cbar[0] = fig.colorbar(im, ax=ax)
cbar[0].ax.tick_params(labelsize=cbar_labelsize)
ax = axs[1]
im = ax.pcolormesh(x,y,cost,vmin=0, cmap=cmap, shading="auto",rasterized=True)
cbar[1] = fig.colorbar(im, ax=ax)
cbar[1].ax.tick_params(labelsize=cbar_labelsize)
#ax.plot(vs[47],ds[25],"x", color="magenta")
axs[1].plot(1e-2,1e-2,"x", markersize=12, color="magenta")
for ax in axs:
ax.set_xscale("log")
ax.set_yscale("log")
ax.tick_params(labelsize=14)
axs[1].set_xlabel(r"$g_x$",fontsize=18)
axs[0].set_xlabel(r"$g_x$",fontsize=18)
axs[0].set_ylabel(r"$g_z$",fontsize=18)
plt.tight_layout()
# has to happen after tight_layout()
#axs[0].text(0.05,0.9,"a Staggered Magnetization", fontweight="bold", size=18, transform = axs[0].transAxes)
#axs[1].text(0.05,0.9,"b cost", fontweight="bold", size=18, transform = axs[1].transAxes)
axs[0].set_title("order parameter", fontsize=14)
axs[1].set_title("cost", fontsize=14)
plt.savefig("plots/antiferro2D_bigger.pdf", bbox_inches='tight')
plt.savefig("plots/antiferro2D_bigger.png", bbox_inches='tight')
|
https://github.com/Qottmann/Quantum-anomaly-detection
|
Qottmann
|
import sys
sys.path.insert(1, '..') # sets the import path to the parent folder
import time
import numpy as np
#import qiskit
#from qiskit.opflow import X,Z,I
#from qiskit.opflow.state_fns import StateFn, CircuitStateFn
#from qiskit.providers.aer import StatevectorSimulator, AerSimulator
#from qiskit.algorithms import VQE
#from qiskit.algorithms.optimizers import COBYLA, SLSQP, SPSA
import matplotlib.pyplot as plt
from scipy import sparse
import scipy.sparse.linalg.eigen.arpack as arp
#from modules.utils import *
from tenpy.networks.mps import MPS
from tenpy.models.hubbard import BoseHubbardChain
from tenpy.algorithms import dmrg
from tenpy.linalg import np_conserved
%matplotlib inline
def DMRG_EBH(L, V, t_list, chi_max=30, bc_MPS='infinite'):
model_params = dict(n_max=1, filling=0.5, bc_MPS=bc_MPS, t=t_list,
L=L, V=V, mu=0, conserve='N', verbose=0)
M = BoseHubbardChain(model_params)
vector=[]
for i in range(M.lat.N_sites):
if i%2:
vector.append(1)
else:
vector.append(0)
psi = MPS.from_product_state(M.lat.mps_sites(), vector, bc=M.lat.bc_MPS)
dmrg_params = {
'mixer': True,
'trunc_params': {
'chi_max': chi_max,
},
'max_E_err': 1.e-16,
'verbose': 0
}
info = dmrg.run(psi, M, dmrg_params)
return info['E'], psi
chi = 50
V_list = np.linspace(2,8,10)
deltat_list = np.linspace(-1,1,10)
L = 4
mu = 0
deltat = 0.5
site = 0
psi_array = []
for deltat in deltat_list:
print('deltat', deltat)
t_list = np.ones(L)
for i in range(len(t_list)):
t_list[i] -= deltat*(-1)**i
psi_0 = []
for V in V_list:
print('V', V)
E0, psi0 = DMRG_EBH(L, V, t_list, chi_max=chi)
psi_0 = np.append(psi_0, psi0)
psi_array.append(psi_0)
np.save('data/BH_MPS.npy', psi_array)
psi_array = np.load('data/BH_MPS.npy', allow_pickle=True)
ES_array = []
parity_array = []
dd_array = []
V_list = np.linspace(2,8,10)
deltat_list = np.linspace(-1,1,10)
for j,deltat in enumerate(deltat_list):
ES_list = []
parity_list = []
dd_list = []
psi_list = psi_array[j]
for i,V in enumerate(V_list):
psi = psi_list[i]
ES = np.exp(-np.array(psi.entanglement_spectrum()[2]))
dES = -np.sum(ES[::2])+np.sum(ES[1::2])
ES_list.append(dES)
dd = psi.expectation_value('dN')[2]
dd_list.append(dd)
dd_array.append(dd_list)
ES_array.append(ES_list)
V_list = np.linspace(2,8,10)
deltat_list = np.linspace(-1,1,10)
fig,ax = plt.subplots(figsize=(8,5))
cmap = plt.get_cmap("plasma") #'viridis'
im = ax.pcolormesh(V_list, deltat_list, np.abs(dd_array), cmap=cmap)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
ax.set_title("density-density correlation",fontsize=20)
ax.set_xlabel(r"$V$",fontsize=24)
ax.set_ylabel(r"$\delta t$",fontsize=24)
ax.tick_params(labelsize=20)
fig,ax = plt.subplots(figsize=(8,5))
cmap = plt.get_cmap("plasma") #'viridis'
im = ax.pcolormesh(V_list, deltat_list, np.abs(ES_array), cmap=cmap)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
ax.set_title("Degeneracy entanglement spectrum",fontsize=20)
ax.set_xlabel(r"$V$",fontsize=24)
ax.set_ylabel(r"$\delta t$",fontsize=24)
ax.tick_params(labelsize=20)
chi = 50
V_list = np.logspace(-2,2,10)
delta_listp= (1-np.logspace(-2,0,5))[::-1]
delta_listn = np.logspace(-2,0,5)-1
deltat_list = np.append(delta_listn, delta_listp)
#deltat_list = np.linspace(-0.95,0.95,10)
L = 10
mu = 0
site = 0
psi_array = []
for deltat in deltat_list:
print('deltat', deltat)
t_list = np.ones(L-1)
for i in range(len(t_list)):
t_list[i] -= deltat*(-1)**i
psi_0 = []
for V in V_list:
print('V', V)
E0, psi0 = DMRG_EBH(L, V, t_list, chi_max=chi, bc_MPS='finite')
psi_0 = np.append(psi_0, psi0)
psi_array.append(psi_0)
np.save('data/BH_MPS_L%.0f_logV_logDeltat.npy' %L, psi_array)
wf_array = []
V_array = []
deltat_array = []
for i, deltat in enumerate(deltat_list):
for j, V in enumerate(V_list):
psi = psi_array[i][j]
wf = psi.get_theta(0,L).to_ndarray().reshape(-1)
wf_array.append(wf)
V_array.append(V)
deltat_array.append(deltat)
np.savez(f'data/wf_BH_L%.0f_logV_logDeltat.npz' %(L), deltat_array=deltat_array, V_array=V_array, wf_array = wf_array)
L = 12
psi_array = np.load('../data/BH_MPS_L%.0f_logV_50x50.npy' %L, allow_pickle=True)
V_list = np.logspace(-2,2,50)
deltat_list = np.linspace(-0.95,0.95,50)
ES_array = []
parity_array = []
dd_array = []
for j,deltat in enumerate(deltat_list):
ES_list = []
parity_list = []
dd_list = []
psi_list = psi_array[j]
for i,V in enumerate(V_list):
psi = psi_list[i]
ES = np.exp(-np.array(psi.entanglement_spectrum()[int(L/2)+1]))
dES = -np.sum(ES[::2])+np.sum(ES[1::2])
ES_list.append(abs(dES))
dd = 0
for i,dd_el in enumerate(psi.expectation_value('dN')[:int(L/2)]):
dd += (-1)**i*dd_el
dd_list.append(dd/(L-6))
dd_array.append(dd_list)
ES_array.append(ES_list)
fig,ax = plt.subplots(figsize=(8,5))
cmap = plt.get_cmap("plasma") #'viridis'
im = ax.pcolormesh(V_list, deltat_list, dd_array, cmap=cmap)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
ax.set_title("density-density correlation",fontsize=20)
ax.set_xlabel(r"$V$",fontsize=24)
ax.set_ylabel(r"$\delta t$",fontsize=24)
ax.tick_params(labelsize=20)
plt.xscale("log")
fig,ax = plt.subplots(figsize=(8,5))
cmap = plt.get_cmap("plasma") #'viridis'
im = ax.pcolormesh(V_list, deltat_list, ES_array, cmap=cmap)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
ax.set_title("Degeneracy entanglement spectrum",fontsize=20)
ax.set_xlabel(r"$V$",fontsize=24)
ax.set_ylabel(r"$\delta t$",fontsize=24)
ax.tick_params(labelsize=20)
plt.xscale("log")
path = "../data/BH_phase-diagrams-and-loss/data_rike/"
np.save(path + "es2.npy", ES_array)
np.save(path + "dd2.npy", dd_array)
L = 12
psi_array = np.load('data/BH_MPS_L%.0f_logV_50x50.npy' %L, allow_pickle=True)
V_list = np.logspace(-2,2,50)
deltat_list = np.linspace(-0.95,0.95,50)
psi = psi_array[0][0]
plt.figure()
plt.ylim(-1,1)
plt.plot(psi.expectation_value('dN'), 'o-')
plt.title('V = %.2f, deltat= %.2f' %(V_list[0], deltat_list[0]))
psi = psi_array[49][0]
plt.figure()
plt.ylim(-1,1)
plt.plot(psi.expectation_value('dN'), 'o-')
plt.title('V = %.2f, deltat= %.2f' %(V_list[0], deltat_list[9]))
psi = psi_array[25][49]
plt.figure()
plt.ylim(-1,1)
plt.plot(psi.expectation_value('dN'), 'o-')
plt.title('V = %.2f, deltat= %.2f' %(V_list[9], deltat_list[5]))
L = 11
psi_array = np.load('data/BH_MPS_L%.0f_logV_25x25.npy' %L, allow_pickle=True)
V_list = np.logspace(-2,2,25)
deltat_list = np.linspace(-0.95,0.95,25)
ES_array = []
parity_array = []
dd_array = []
for j,deltat in enumerate(deltat_list):
ES_list = []
parity_list = []
dd_list = []
psi_list = psi_array[j]
for i,V in enumerate(V_list):
psi = psi_list[i]
ES = np.exp(-np.array(psi.entanglement_spectrum()[int(L/2)]))
dES = -np.sum(ES[::2])+np.sum(ES[1::2])
ES_list.append(dES)
dd = 0
for dd_el in psi.expectation_value('dN')[3:L-3]:
dd += (-1)**i*dd_el
#dd = np.sum(abs(psi.expectation_value('dN')[3:L-3]))
dd_list.append(dd)
dd_array.append(dd_list)
ES_array.append(ES_list)
fig,ax = plt.subplots(figsize=(8,5))
cmap = plt.get_cmap("plasma") #'viridis'
im = ax.pcolormesh(V_list, deltat_list, np.abs(dd_array), cmap=cmap)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
ax.set_title("density-density correlation",fontsize=20)
ax.set_xlabel(r"$V$",fontsize=24)
ax.set_ylabel(r"$\delta t$",fontsize=24)
ax.tick_params(labelsize=20)
plt.xscale("log")
fig,ax = plt.subplots(figsize=(8,5))
cmap = plt.get_cmap("plasma") #'viridis'
im = ax.pcolormesh(V_list, deltat_list, np.abs(ES_array), cmap=cmap)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
ax.set_title("Degeneracy entanglement spectrum",fontsize=20)
ax.set_xlabel(r"$V$",fontsize=24)
ax.set_ylabel(r"$\delta t$",fontsize=24)
ax.tick_params(labelsize=20)
plt.xscale("log")
L = 11
psi_array = np.load('data/BH_MPS_L%.0f_logV_25x25.npy' %L, allow_pickle=True)
V_list = np.logspace(-2,2,25)
deltat_list = np.linspace(-0.95,0.95,25)
psi = psi_array[0][0]
plt.figure()
plt.ylim(-1,1)
plt.plot(psi.expectation_value('dN'), 'o-')
plt.title('V = %.2f, deltat= %.2f' %(V_list[0], deltat_list[0]))
psi = psi_array[24][0]
plt.figure()
plt.ylim(-1,1)
plt.plot(psi.expectation_value('dN'), 'o-')
plt.title('V = %.2f, deltat= %.2f' %(V_list[0], deltat_list[9]))
psi = psi_array[15][24]
plt.figure()
plt.ylim(-1,1)
plt.plot(psi.expectation_value('dN'), 'o-')
plt.title('V = %.2f, deltat= %.2f' %(V_list[9], deltat_list[5]))
|
https://github.com/Qottmann/Quantum-anomaly-detection
|
Qottmann
|
import time
import numpy as np
import qiskit
from qiskit.opflow import X,Z,I
from qiskit.opflow.state_fns import StateFn, CircuitStateFn
from qiskit.providers.aer import StatevectorSimulator, AerSimulator
from qiskit.algorithms import VQE
from qiskit.algorithms.optimizers import COBYLA, SLSQP, SPSA
import matplotlib.pyplot as plt
from scipy import sparse
import scipy.sparse.linalg.eigen.arpack as arp
from modules.utils import *
from tenpy.networks.mps import MPS
from tenpy.models.hubbard import BoseHubbardChain
from tenpy.algorithms import dmrg
from tenpy.linalg import np_conserved
def DMRG_EBH(L, V, t_list, chi_max=30, bc_MPS='infinite'):
model_params = dict(n_max=1, filling=0.5, bc_MPS=bc_MPS, t=t_list,
L=L, V=V, mu=0, conserve='N', verbose=0)
M = BoseHubbardChain(model_params)
vector=[]
for i in range(M.lat.N_sites):
if i%2:
vector.append(1)
else:
vector.append(0)
psi = MPS.from_product_state(M.lat.mps_sites(), vector, bc=M.lat.bc_MPS)
dmrg_params = {
'mixer': True,
'trunc_params': {
'chi_max': chi_max,
},
'max_E_err': 1.e-16,
'verbose': 0
}
info = dmrg.run(psi, M, dmrg_params)
return info['E'], psi
chi = 50
V_list = np.logspace(-2,2,50)
deltat_list = np.linspace(-0.95,0.95,50)
L = 12
mu = 0
site = 0
psi_array = []
for deltat in deltat_list:
print('deltat', deltat)
t_list = np.ones(L-1)
for i in range(len(t_list)):
t_list[i] -= deltat*(-1)**i
psi_0 = []
for V in V_list:
print('V', V)
E0, psi0 = DMRG_EBH(L, V, t_list, chi_max=chi, bc_MPS='finite')
psi_0 = np.append(psi_0, psi0)
psi_array.append(psi_0)
np.save('data/BH_MPS_L%.0f_logV_50x50.npy' %L, psi_array)
wf_array = []
V_array = []
deltat_array = []
for i, deltat in enumerate(deltat_list):
for j, V in enumerate(V_list):
psi = psi_array[i][j]
wf = psi.get_theta(0,L).to_ndarray().reshape(-1)
wf_array.append(wf)
V_array.append(V)
deltat_array.append(deltat)
np.savez(f'data/wf_BH_L%.0f_logV_50x50.npz' %(L), deltat_array=deltat_array, V_array=V_array, wf_array = wf_array)
|
https://github.com/Qottmann/Quantum-anomaly-detection
|
Qottmann
|
import time
import itertools
import numpy as np
from scipy.optimize import minimize, basinhopping
from qiskit import *
from qiskit.quantum_info import Statevector
from qiskit.algorithms.optimizers import COBYLA, L_BFGS_B, SPSA
from qiskit.opflow.state_fns import StateFn, CircuitStateFn
from qiskit.providers.aer import StatevectorSimulator, AerSimulator
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
from matplotlib.colors import BoundaryNorm
from modules.utils import *
%matplotlib inline
cmap = plt.get_cmap("plasma") #'viridis'
import qiskit
#%matplotlib inline
print(qiskit.__version__)
#reset random seed
np.random.seed(123)
from qiskit.utils import algorithm_globals
algorithm_globals.random_seed = 123
### Preliminaries
L = 5
num_trash = 2
logspace_size=50
name = f'VQE_Ising_L5_anti_-1_50x50' # _1e-3 # name of the data produced by this notebook
filename = "data/params_" + name # name of the data file that is used
L = 5
anti = -1.
VQE_params = np.load(filename + ".npz", allow_pickle=True)
gx_list = VQE_params['gx_list']
gz_list = VQE_params['gz_list']
opt_params = VQE_params['opt_params']
gx_vals = np.unique(gx_list)
gz_vals = np.unique(gz_list)
mag = QMag(L,anti) #magnetization operator (Qiskit)
Smag = Mag(L,anti) #magnetization operator (numpy)
# the ~ is the adjoint, but also it turns the is_measurement attribute to True
~StateFn(mag)
# state is technically a circuit, that prepares the ground state via VQE circuit
#state.draw() # uncomment to see, but is very long
Qen=np.zeros(len(opt_params)); Sen=np.zeros(len(opt_params)) #energies
Qmags=np.zeros(len(opt_params)); Smags=np.zeros(len(opt_params)) #magnetizations
load = True
if load:
temp = np.load(filename + "_mags-Es.npz",allow_pickle=True)
Qmags = temp["Qmags"]
Qen = temp["Qen"]
Sen = temp["Sen"]
Smags = temp["Smags"]
if not load:
for j in range(len(opt_params)):
gx = gx_list[j]
gz = gz_list[j]
H = QHIsing(L, anti, np.float32(gx), np.float32(gz)) # build Hamiltonian Op
state = init_vqe(opt_params[j], L=L)
meas_outcome = ~StateFn(mag) @ StateFn(state)
Qmags[j] = meas_outcome.eval()
e_outcome = ~StateFn(H) @ StateFn(state)
Qen[j] = e_outcome.eval()
init_state, E, ham = ising_groundstate(L, anti, np.float(gx), np.float(gz))
Sen[j] = E
Smags[j] = init_state.T.conj()@Smag@init_state #Magnetization with Numpy results
np.savez(filename + "_mags-Es",Qmags=Qmags, Qen=Qen, Sen=Sen, Smags=Smags) # for large parameter space takes quite a while because of the exact diagonalization
fig,ax = plt.subplots(figsize=(8,5))
cmap = plt.get_cmap("plasma") #'viridis'
im = ax.pcolormesh(gz_vals, gx_vals, abs(Smags).reshape(len(gz_vals),len(gx_vals)), cmap=cmap)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
plt.yscale("log")
ax.set_title("Magnetization ED L %.0f anti %.0f" %(L, anti),fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
fig,ax = plt.subplots(figsize=(8,5))
cmap = plt.get_cmap("plasma") #'viridis'
im = ax.pcolormesh(gz_vals, gx_vals, abs(Qmags).reshape(len(gz_vals),len(gx_vals)), cmap=cmap)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
plt.yscale("log")
ax.set_title("Magnetization VQE L %.0f anti %.0f" %(L, anti),fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
fig,ax = plt.subplots(figsize=(8,5))
cmap = plt.get_cmap("plasma") #'viridis'
im = ax.pcolormesh(gz_vals, gx_vals, Sen.reshape(len(gz_vals),len(gx_vals)) - Qen.reshape(len(gz_vals),len(gx_vals)), cmap=cmap)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
plt.yscale("log")
ax.set_title("Difference in energy L %.0f anti %.0f" %(L, anti),fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
##############################################################################
### II - Training ###########################################################
##############################################################################
# Use Aer's qasm_simulator
backend_sim = Aer.get_backend('qasm_simulator')
thetas = np.random.uniform(0, 2*np.pi, 2*L+2) # initial parameters without feature encoding
# thetas = np.random.uniform(0, 2*np.pi, (2*L+2, 2)) # initial parameters with feature encoding
# linear entangler (as in scales linearly with trash qubits)
def get_entangler_map(L, num_trash, i_permut=1):
result = []
nums = list(range(L)) # here was the problem, it doesnt like when list elements are taken from numpy
nums_compressed = nums.copy()[:L-num_trash]
nums_trash = nums.copy()[-num_trash:]
#print(nums, nums_compressed, nums_trash)
# combine all trash qubits with themselves
for trash_q in nums_trash[:-1]:
result.append((trash_q+1,trash_q))
# combine each of the trash qubits with every n-th
repeated = list(nums_trash) * (L-num_trash) # repeat the list of trash indices cyclicly
for i in range(L-num_trash):
result.append((repeated[i_permut + i], nums_compressed[i]))
return result
def QAE_Ansatz(thetas, L, num_trash, insert_barriers=False, parametrized_gate = "ry", entangling_gate = "cz"):
entanglement = [get_entangler_map(L,num_trash,i_permut) for i_permut in range(num_trash)]
circ = qiskit.circuit.library.TwoLocal(L,
parametrized_gate,
entangling_gate,
entanglement,
reps=num_trash,
insert_barriers=insert_barriers,
skip_final_rotation_layer=True
).assign_parameters(thetas[:-num_trash])
if insert_barriers: circ.barrier()
for i in range(num_trash):
circ.ry(thetas[L-i-1], L-i-1)
#circ.ry(circuit.Parameter(f'θ{i}'), L-i-1)
return circ
def prepare_circuit(thetas, L=6, num_trash=2, init_state=None, measurement=True, vqe=True):
qreg = QuantumRegister(L, 'q')
creg = ClassicalRegister(num_trash, 'c')
circ = QuantumCircuit(qreg, creg)
circ += QAE_Ansatz(thetas, L, num_trash, insert_barriers=True)#.assign_parameters(thetas) # difference to bind?
if measurement:
for i in range(num_trash):
circ.measure(qreg[L-i-1], creg[i])
if init_state is not None:
if vqe:
circ = init_vqe(init_state,L=L) + circ
else:
circ.initialize(init_state, qreg)
return circ
def feature_encoding(thetas, x):
""" thetas: parameters to be optimized, x: Ising model parameter (eg. field) """
new_thetas = []
thetas = thetas.reshape((-1,2))
for theta in thetas:
new_thetas.append(theta[0] * x + theta[1])
return new_thetas
idx = 30
num_trash = 2
J, gx, gz = -1., gx_list[idx], gz_list[idx] # Ising parameters for which ground state should be compressed
phi = opt_params[idx] # train on smallest lambda ;; this may grammatically be confusing, init_state = dictionary with unsorted parameters
def run_circuit(thetas, L, num_trash, init_state, vqe=True, shots=100):
circ = prepare_circuit(thetas, L, num_trash, init_state, vqe=vqe)
# Execute the circuit on the qasm simulator.
job_sim = execute(circ, backend_sim, shots=shots, seed_simulator=123, seed_transpiler=234) # fix seed to make it reproducible
# Grab the results from the job.
result_sim = job_sim.result()
counts = result_sim.get_counts(circ)
# print(counts)
# mems = result_sim.get_memory(circ)
# print(mems)
return counts
run_circuit(thetas, L, num_trash, phi)['11']
# translate to Rikes naming
phis = opt_params
gxs = gx_list
gzs = gz_list
def cost_function_single(thetas, L, num_trash, p, shots=1000, vqe=True, param_encoding=False, x=0):
""" Optimizes circuit """
if vqe:
init_state = phis[p]
else:
J, gx, gz = p
init_state, _ = ising_groundstate(L, J, gx, gz)
if param_encoding: thetas = feature_encoding(thetas, x)
out = run_circuit(thetas, L, num_trash, init_state, vqe=vqe, shots=shots)
cost = out.get('11', 0)*2 + out.get('01', 0) + out.get('10', 0)
return cost/shots
def cost_function(thetas, L, num_trash, ising_params, shots=1000, vqe=True, param_encoding=False, x=0):
""" Optimizes circuit """
cost = 0.
n_samples = len(ising_params)
for i, p in enumerate(ising_params):
if param_encoding:
cost += cost_function_single(thetas, L, num_trash, p, shots, vqe, param_encoding, x[i])
else:
cost += cost_function_single(thetas, L, num_trash, p, shots, vqe, param_encoding)
return cost/n_samples
def optimize(ising_params, L=6, num_trash=2, thetas=None, shots=1000, max_iter=400, vqe=True, param_encoding=False, x=0, pick_optimizer = None):
if thetas is None:
n_params = (2*L+2)*2 if param_encoding else (2*L+2)
thetas = np.random.uniform(0, 2*np.pi, n_params) # initial parameters without feature encoding
print("Initial cost: {:.3f}".format(cost_function(thetas, L, num_trash, ising_params, shots, vqe, param_encoding, x)))
counts, values, accepted = [], [], []
def store_intermediate_result(eval_count, parameters, mean, std, ac):
# counts.append(eval_count)
values.append(mean)
accepted.append(ac)
# Initialize optimizer
if pick_optimizer == "cobyla":
optimizer = COBYLA(maxiter=max_iter, tol=0.0001)
if pick_optimizer == "adam" or pick_optimizer == "ADAM":
optimizer = qiskit.algorithms.optimizers.ADAM(maxiter=max_iter)
# optimizer = L_BFGS_B(maxfun=300, maxiter=max_iter)#, factr=10, iprint=- 1, epsilon=1e-08)
if pick_optimizer == "spsa" or pick_optimizer == None:
optimizer = SPSA(maxiter=max_iter,
#blocking=True,
callback=store_intermediate_result,
#learning_rate=1e-1,
#perturbation=0.4
) # recommended from qiskit (first iteraction takes quite long)
# to reduce time figure out optimal learning rate and perturbation in advance
start_time = time.time()
ret = optimizer.optimize(
num_vars=len(thetas),
objective_function=(lambda thetas: cost_function(thetas, L, num_trash, ising_params, shots, vqe, param_encoding, x)),
initial_point=thetas
)
print("Time: {:.5f} sec".format(time.time()-start_time))
print(ret)
return ret[0], values, accepted
x,y = np.meshgrid(gx_vals, gz_vals)
def run_inference(thetas, shots=1000, L=5):
points = 50
J = -1.0
x,y = np.meshgrid(gx_vals, gz_vals)
cost = np.zeros((len(gx_vals) * len(gz_vals)))
Smags = np.zeros((len(gx_vals) * len(gz_vals)))
shots = 1000
for i,p in enumerate(list(zip(gxs, gzs))):
cost[i] = cost_function_single(thetas, L, num_trash, i, shots=shots)
cost = cost.reshape((len(gx_vals), len(gz_vals)))
return cost
cmap = plt.get_cmap("plasma")
def plot_result(cost):
fig,axs = plt.subplots(ncols=2,figsize=(15,5))
nbins=100
ax = axs[0]
im = ax.pcolormesh(x, y, cost, cmap=cmap, shading="auto")
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
ax.set_xscale("log")
ax.set_yscale("log")
ax.set_title(f"Loss",fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
for p in params:
gz = gz_list[p]
gx = gx_list[p]
ax.plot(gz,gx,"o",label="training",color="cyan")
ax = axs[1]
im = ax.pcolormesh(gz_vals, gx_vals, abs(Qmags).reshape(len(gz_vals),len(gx_vals)), cmap=cmap, shading="auto")
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
ax.set_xscale("log")
ax.set_yscale("log")
ax.set_title("Magnetization VQE L %.0f anti %.0f" %(L, anti),fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
paramss =[
[0], # train on bottom left point
np.intersect1d(np.where(gx_list > 99),np.where(gz_list == 1e-2)), # top left
np.intersect1d(np.where(gz_list > 99),np.where(gx_list == 1e-2)), # bottom right
[2450, 49] # both paramagnetic phases
]
for i,params in enumerate(paramss):
print(f"{i} / {len(paramss)} - params (gz, gx) in {[(gzs[p], gxs[p]) for p in params]}")
thetass = [None] * len(paramss)
losss = [None] * len(paramss)
costs = [None] * len(paramss)
for i,params in enumerate(paramss):
print(f"{i+1} / {len(paramss)} - params (gz, gx) in {[(gzs[p], gxs[p]) for p in params]}")
thetas, loss, accepted = optimize(params, max_iter=100, L=5) #, pick_optimizer="adam")
thetass[i], losss[i] = thetas, loss
plt.plot(loss)
cost = run_inference(thetas)
costs[i] = cost
np.savez("data/" + name + "_thetas-loss-cost", losss = losss, costs = costs, thetass = thetass)
for i,params in enumerate(paramss):
print(f"{i+1} / {len(paramss)} - params (gz, gx) in {[(gzs[p], gxs[p]) for p in params]}")
plot_result(np.load("data/" + name + "_thetas-loss-cost.npz",allow_pickle=True)["costs"][i])
plt.show()
fig, axs = plt.subplots(ncols=2,figsize=(10,5))
ax = axs[0]
ax.imshow(np.load("data/" + name + "_thetas-loss-cost.npz",allow_pickle=True)["costs"][0])
ax = axs[1]
ax.imshow(np.load("data/" + name + "_thetas-loss-cost2.npz",allow_pickle=True)["costs"][0])
algorithm_globals.random_seed = 321
for i,params in enumerate(paramss):
print(f"{i+1} / {len(paramss)} - params (gz, gx) in {[(gzs[p], gxs[p]) for p in params]}")
thetas, loss, accepted = optimize(params, max_iter=100, L=5) #, pick_optimizer="adam")
thetass[i], losss[i] = thetas, loss
plt.plot(loss)
cost = run_inference(thetas)
costs[i] = cost
np.savez("data/" + name + "_thetas-loss-cost2", losss = losss, costs = costs, thetass = thetass)
for i,params in enumerate(paramss):
print(f"{i+1} / {len(paramss)} - params (gz, gx) in {[(gzs[p], gxs[p]) for p in params]}")
plot_result(np.load("data/" + name + "_thetas-loss-cost2.npz",allow_pickle=True)["costs"][i])
plt.show()
for i,params in enumerate(paramss):
print(f"{i+1} / {len(paramss)} - params (gz, gx) in {[(gzs[p], gxs[p]) for p in params]}")
thetas, loss, accepted = optimize(params, max_iter=100, L=5, pick_optimizer="cobyla")
thetass[i], losss[i] = thetas, loss
plt.plot(loss)
cost = run_inference(thetas)
costs[i] = cost
np.savez("data/" + name + "_thetas-loss-cost_cobyla", losss = losss, costs = costs, thetass = thetass)
for i,params in enumerate(paramss):
print(f"{i+1} / {len(paramss)} - params (gz, gx) in {[(gzs[p], gxs[p]) for p in params]}")
plot_result(np.load("data/" + name + "_thetas-loss-cost_cobyla.npz",allow_pickle=True)["costs"][i])
plt.show()
|
https://github.com/Qottmann/Quantum-anomaly-detection
|
Qottmann
|
import time
import itertools
import numpy as np
from scipy.optimize import minimize, basinhopping
from qiskit import *
from qiskit.quantum_info import Statevector
from qiskit.algorithms.optimizers import COBYLA, L_BFGS_B, SPSA
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
from matplotlib.colors import BoundaryNorm
from modules.utils import Mag
%matplotlib inline
cmap = plt.get_cmap("plasma") #'viridis'
plt.rc('font', family='serif')#, serif='Times')
plt.rc('text', usetex=True)
plt.rc('xtick', labelsize=14)
plt.rc('ytick', labelsize=14)
plt.rc('axes', labelsize=16)
plt.rc('legend', fontsize=14)
plt.rc('legend', handlelength=2)
plt.rc('axes', titlesize=20)
# from: https://tenpy.readthedocs.io/en/latest/toycodes/tfi_exact.html
import numpy as np
import scipy.sparse as sparse
import scipy.sparse.linalg.eigen.arpack as arp
import warnings
import scipy.integrate
def ising_groundstate(L, J, gx, gz=0): # gx is transverse field, gz the longitudinal
"""For comparison: obtain ground state energy from exact diagonalization.
Exponentially expensive in L, only works for small enough `L` <~ 20.
"""
if L >= 20:
warnings.warn("Large L: Exact diagonalization might take a long time!")
# get single site operaors
sx = sparse.csr_matrix(np.array([[0., 1.], [1., 0.]]))
sz = sparse.csr_matrix(np.array([[1., 0.], [0., -1.]]))
id = sparse.csr_matrix(np.eye(2))
sx_list = [] # sx_list[i] = kron([id, id, ..., id, sx, id, .... id])
sz_list = []
for i_site in range(L):
x_ops = [id] * L
z_ops = [id] * L
x_ops[i_site] = sx
z_ops[i_site] = sz
X = x_ops[0]
Z = z_ops[0]
for j in range(1, L):
X = sparse.kron(X, x_ops[j], 'csr')
Z = sparse.kron(Z, z_ops[j], 'csr')
sx_list.append(X)
sz_list.append(Z)
H_zz = sparse.csr_matrix((2**L, 2**L))
H_z = sparse.csr_matrix((2**L, 2**L))
H_x = sparse.csr_matrix((2**L, 2**L))
for i in range(L - 1):
H_zz = H_zz + sz_list[i] * sz_list[(i + 1) % L]
for i in range(L):
H_z = H_z + sz_list[i]
H_x = H_x + sx_list[i]
H = -J * H_zz - gx * H_x - gz * H_z
E, V = arp.eigsh(H, k=1, which='SA', return_eigenvectors=True, ncv=20)
return V[:,0], E[0]
init_state, E = ising_groundstate(6, 1., 1., 1.)
def sort_vals(vals):
""" vals is (unsorted) dictionary of parameters from VQE ansatz circuit, this returns sorted values as list """
indices = np.array([_.index for _ in vals]) # unordered list of indices from the ParameterVectorElement(Theta(INDEX))
vals_sorted = np.array([vals[_] for _ in vals]) # unordered list of values (but same ordering as indices)
return vals_sorted[np.argsort(indices)]
def init_vqe(vals, L):
#return qiskit.circuit.library.EfficientSU2(L, reps=3).assign_parameters(sort_vals(vals))
return qiskit.circuit.library.EfficientSU2(L, reps=3).assign_parameters(vals)
L = 5
anti = -1
#filename = f'data/params_VQE_Ising_L{L}_anti_{anti}.npz' # name of the data file that is used
filename = f'data/params_VQE_Ising_L{L}_anti_{anti}_50x50.npz'
VQE_vals = np.load(filename, allow_pickle=True)
gxs = VQE_vals['gx_list']
gzs = VQE_vals['gz_list']
phis = VQE_vals['opt_params']
N = int(np.sqrt(gxs.size))
gxs = np.logspace(-2, 2, N)#gxs.reshape(N,N)
gzs = np.logspace(-2, 2, N)#gzs.reshape(N,N)
phis = phis.reshape(N,N,-1) #(gx, gz)
# Use Aer's qasm_simulator
backend_sim = Aer.get_backend('qasm_simulator')
L = 5 # system size
num_trash = 2
thetas = np.random.uniform(0, 2*np.pi, num_trash*L+num_trash) # initial parameters without feature encoding
# thetas = np.random.uniform(0, 2*np.pi, (2*L+2, 2)) # initial parameters with feature encoding
def prepare_circuit(init_state=None, measurement=True):
qreg = QuantumRegister(L, 'q')
creg = ClassicalRegister(2, 'c')
circ = QuantumCircuit(qreg, creg)
entangler_map1 = [(5, 4), (5, 3), (5, 1), (4, 2), (4, 0)]
entangler_map2 = [(5, 4), (5, 2), (4, 3), (5, 0), (4, 1)]
circ += circuit.library.TwoLocal(L, 'ry', 'cz', entanglement = [entangler_map1, entangler_map2], reps=2, insert_barriers=True, skip_final_rotation_layer=True)
circ.ry(circuit.Parameter('θ1'), 4)
circ.ry(circuit.Parameter('θ2'), 5)
if measurement:
circ.measure(qreg[4], creg[0])
circ.measure(qreg[5], creg[1])
if init_state is not None:
circ = init_vqe(init_state) + circ
return circ
# same circuit as above (more explicit)
def prepare_circuit2(thetas, init_state=None, measurement=True):
qreg = QuantumRegister(L, 'q')
creg = ClassicalRegister(2, 'c')
circ = QuantumCircuit(qreg, creg)
#if init_state is not None: circ.initialize(init_state, qreg)
for i,t in enumerate(thetas[:L]):
circ.ry(t, i)
circ.cz(5,4)
circ.cz(5,3)
circ.cz(5,1)
circ.cz(4,2)
circ.cz(4,0)
for i,t in enumerate(thetas[L:2*L]):
circ.ry(t, i)
circ.cz(5,4)
circ.cz(5,2)
circ.cz(4,3)
circ.cz(5,0)
circ.cz(4,1)
circ.ry(thetas[2*L], 4)
circ.ry(thetas[2*L+1], 5)
if measurement:
circ.measure(qreg[4], creg[0])
circ.measure(qreg[5], creg[1])
if init_state is not None:
circ = init_vqe(init_state) + circ
return circ
# linear entangler (as in scales linearly with trash qubits)
def get_entangler_map(L, num_trash, i_permut=1):
result = []
nums = list(range(L)) # here was the problem, it doesnt like when list elements are taken from numpy
nums_compressed = nums.copy()[:L-num_trash]
nums_trash = nums.copy()[-num_trash:]
#print(nums, nums_compressed, nums_trash)
# combine all trash qubits with themselves
for trash_q in nums_trash[:-1]:
result.append((trash_q+1,trash_q))
# combine each of the trash qubits with every n-th
repeated = list(nums_trash) * (L-num_trash) # repeat the list of trash indices cyclicly
for i in range(L-num_trash):
result.append((repeated[i_permut + i], nums_compressed[i]))
return result
def QAE_Ansatz(thetas, L, num_trash, insert_barriers=False, parametrized_gate = "ry", entangling_gate = "cz"):
entanglement = [get_entangler_map(L,num_trash,i_permut) for i_permut in range(num_trash)]
circ = qiskit.circuit.library.TwoLocal(L,
parametrized_gate,
entangling_gate,
entanglement,
reps=num_trash,
insert_barriers=insert_barriers,
skip_final_rotation_layer=True
).assign_parameters(thetas[:-num_trash])
if insert_barriers: circ.barrier()
for i in range(num_trash):
circ.ry(thetas[L-i-1], L-i-1)
#circ.ry(circuit.Parameter(f'θ{i}'), L-i-1)
return circ
def prepare_circuit3(thetas, L=6, num_trash=2, init_state=None, measurement=True, vqe=True):
qreg = QuantumRegister(L, 'q')
creg = ClassicalRegister(num_trash)#?, 'c')
circ = QuantumCircuit(qreg, creg)
print(circ.cregs)
if init_state is not None:
if not vqe:
circ.initialize(init_state, qreg)
circ += QAE_Ansatz(thetas, L, num_trash, insert_barriers=True)#.assign_parameters(thetas) # difference to bind?
if measurement:
for i in range(num_trash):
circ.measure(qreg[L-i-1], creg[i])
if init_state is not None:
if vqe:
circ = init_vqe(init_state, L) + circ
#else:
# circ.initialize(init_state, qreg)
return circ
def feature_encoding(thetas, x):
""" thetas: parameters to be optimized, x: Ising model parameter (eg. field) """
new_thetas = []
thetas = thetas.reshape((-1,2))
for theta in thetas:
new_thetas.append(theta[0] * x + theta[1])
return new_thetas
idx = 30
J, gx, gz = -1., gxs[idx], gzs[idx]
phi = phis[idx, idx]
def run_circuit(thetas, L, num_trash, init_state, vqe=True, shots=100):
circ = prepare_circuit3(thetas, L, num_trash, init_state, vqe=vqe)
# Execute the circuit on the qasm simulator.
job_sim = execute(circ, backend_sim, shots=shots)#, memory=True)
# Grab the results from the job.
result_sim = job_sim.result()
counts = result_sim.get_counts(circ)
# print(counts)
# mems = result_sim.get_memory(circ)
# print(mems)
return counts
run_circuit(thetas, L, num_trash, phi)
#reset random seed
np.random.seed(123)
def hamming_distance(out):
return sum(key.count('1') * value for key, value in out.items())
def cost_function_single(thetas, L, num_trash, p, shots=1000, vqe=True, param_encoding=False, x=0, model="bh"):
""" Optimizes circuit """
if vqe:
init_state = phis[p]
else:
if model=="ising":
J, gx, gz = p
init_state, _ = ising_groundstate(L, J, gx, gz)
elif model=="bh":
init_state = p#init_states[p]
if param_encoding: thetas = feature_encoding(thetas, x)
out = run_circuit(thetas, L, num_trash, init_state, vqe=vqe, shots=shots)
cost = hamming_distance(out)
return cost/shots
def cost_function(thetas, L, num_trash, ising_params, shots=1000, vqe=True, param_encoding=False, x=0):
""" Optimizes circuit """
cost = 0.
n_samples = len(ising_params)
for i, p in enumerate(ising_params):
if param_encoding:
cost += cost_function_single(thetas, L, num_trash, p, shots, vqe, param_encoding, x[i])
else:
cost += cost_function_single(thetas, L, num_trash, p, shots, vqe, param_encoding)
return cost/n_samples
def optimize(ising_params, L=6, num_trash=2, thetas=None, shots=1000, max_iter=500, lr=None, perturbation=None, vqe=True, param_encoding=False, x=0):
if thetas is None:
n_params = (num_trash*L+num_trash)*2 if param_encoding else (num_trash*L+num_trash)
thetas = np.random.uniform(0, 2*np.pi, n_params) # initial parameters without feature encoding
print("Initial cost: {:.3f}".format(cost_function(thetas, L, num_trash, ising_params, shots, vqe, param_encoding, x)))
counts, values, accepted = [], [], []
def store_intermediate_result(eval_count, parameters, mean, std, ac):
# counts.append(eval_count)
values.append(mean)
accepted.append(ac)
# Initialize optimizer
# optimizer = COBYLA(maxiter=max_iter, tol=0.0001)
# optimizer = L_BFGS_B(maxfun=300, maxiter=max_iter)#, factr=10, iprint=- 1, epsilon=1e-08)
optimizer = SPSA(maxiter=max_iter,
#blocking=True,
# callback=store_intermediate_result,
learning_rate=lr,
perturbation=perturbation
) # recommended from qiskit (first iteraction takes quite long)
# to reduce time figure out optimal learning rate and perturbation in advance
start_time = time.time()
ret = optimizer.optimize(
num_vars=len(thetas),
objective_function=(lambda thetas: cost_function(thetas, L, num_trash, ising_params, shots, vqe, param_encoding, x)),
initial_point=thetas
)
print("Time: {:.5f} sec".format(time.time()-start_time))
print(ret)
return ret[0], values, accepted
thetas, loss, accepted = optimize(params, L, 2, max_iter=itera)
fig = plt.figure(figsize=(12,4))
plt.subplot(1, 2, 1)
plt.plot(losses[2,1,:], "x--") #2,2 1,2
plt.yscale("log")
plt.xlabel("step")
plt.ylabel("Loss");
for i,lr in enumerate(lrs):
for j,p in enumerate(perturbations):
fig = plt.figure(figsize=(12,4))
plt.subplot(1, 2, 1)
plt.plot(losses[i,j,:], "x--")
plt.yscale("log")
plt.xlabel("step")
plt.ylabel("Loss");
for i,lr in enumerate(lrs):
for j,p in enumerate(perturbations):
fig = plt.figure(figsize=(12,4))
plt.subplot(1, 2, 1)
plt.plot(losses[i,j,:], "x--")
plt.yscale("log")
plt.xlabel("step")
plt.ylabel("Loss");
params = [(13,13)]
num_trash = 2
print(gxs[params[0][0]], gzs[params[0][1]])
thetas, loss, accepted = optimize(params, L, 2, max_iter=40)
thetas_normal_phase_2 = thetas
len(loss)
params = [(13,13)]
num_trash = 2
print(gxs[params[0][0]], gzs[params[0][1]])
thetas, loss, accepted = optimize(params, L, 2, max_iter=40)
thetas_normal_phase_2 = thetas
params = [(18,38)]
thetas_test, loss, accepted = optimize(params, L, max_iter=40)
params = [(13,13)]
thetas_test, loss, accepted = optimize(params, 5, 2, max_iter=40, vqe=False)
L = 5
anti = -1
# filename = f'data/params_VQE_Ising_L{L}_anti_{anti}.npz' # name of the data file that is used
filename = f'data/params_VQE_Ising_L{L}_anti_{anti}_50x50.npz'
VQE_vals = np.load(filename, allow_pickle=True)
gxs = VQE_vals['gx_list']
gzs = VQE_vals['gz_list']
phis = VQE_vals['opt_params']
N = int(np.sqrt(gxs.size))
gxs = np.logspace(-2, 2, N)#gxs.reshape(N,N)
gzs = np.logspace(-2, 2, N)#gzs.reshape(N,N)
phis = phis.reshape(N,N,-1) #(gx, gz)
params = [(16,37),(37,16)]
num_trash = 2
print(gxs[params[0][0]], gzs[params[0][1]])
thetas, loss, accepted = optimize(params, L, num_trash)
thetas_normal_phase_2 = thetas
seeds = np.arange(20) + 10
for seed in seeds:
cost = np.load(f'data_rike/cost_bh_L12_trash2_d48_v5_seed{seed}.npy', allow_pickle=True) # 7, 10
params = [[48,5]]
# cost = np.load(f'data_rike/cost_bh_L12_trash2_d2_v5_seed{seed}.npy', allow_pickle=True) # -2
# cost = np.load(f'data_rike/cost_bh_L12_trash4_d2_v5_seed{seed}.npy', allow_pickle=True)
# params = [[2,5]]
# cost = np.load(f'data_rike/cost_bh_L12_trash2_d10_v10_seed{seed}.npy', allow_pickle=True) # 3, 6
# cost = np.load(f'data_rike/cost_bh_L12_trash4_d10_v10_seed{seed}.npy', allow_pickle=True)
# params = [[10,10]]
fig,axs = plt.subplots(figsize=(8,5),squeeze=False)
nbins=100
ax = axs[0,0]
levels = MaxNLocator(nbins=nbins).tick_values(cost.min(),cost.max())
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
im = ax.pcolormesh(y, x, cost.T, cmap=cmap, norm=norm)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
# plt.yscale("log")
ax.set_title("Loss",fontsize=20)
ax.set_xlabel(r"$V$",fontsize=24)
ax.set_ylabel(r"$\delta t$",fontsize=24)
ax.tick_params(labelsize=20)
for p in params:
d = deltas[p[0]]
V = Vs[p[1]]
ax.plot(V,d,"o",label="training",color="cyan")
plt.show()
fig = plt.figure(figsize=(12,4))
plt.subplot(1, 2, 1)
plt.plot(loss, "x--")
plt.yscale("log")
plt.xlabel("step")
plt.ylabel("Loss");
points = 50
J = -1.0
x,y = np.meshgrid(gxs,gzs)
cost = np.zeros((points,points))
#Smags = np.zeros((points,points))
shots = 1000
for i,gx in enumerate(gxs):
for j,gz in enumerate(gzs):
cost[i,j] = cost_function_single(thetas, L, num_trash, (i,j), shots=shots)
#init_state, _ = ising_groundstate(5, J, gx, gz)
#Smags[i,j] = np.real(init_state.T.conj()@Mag(5,-1)@init_state)
fig,axs = plt.subplots(figsize=(8,5),squeeze=False)
nbins=100
ax = axs[0,0]
levels = MaxNLocator(nbins=nbins).tick_values(cost.min(),cost.max())
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
im = ax.pcolormesh(x, y, cost, cmap=cmap, norm=norm)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
plt.yscale("log")
ax.set_title("Loss",fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
fig,axs = plt.subplots(figsize=(8,5),squeeze=False)
nbins=100
ax = axs[0,0]
levels = MaxNLocator(nbins=nbins).tick_values(Smags.min(),Smags.max())
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
im = ax.pcolormesh(x, y, Smags, cmap=cmap, norm=norm)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
plt.yscale("log")
ax.set_title("Staggered magnetization",fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
fig,axs = plt.subplots(figsize=(8,5),squeeze=False)
nbins=100
ax = axs[0,0]
levels = MaxNLocator(nbins=nbins).tick_values(cost.min(),cost.max())
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
im = ax.pcolormesh(x, y, cost, cmap=cmap, norm=norm)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
plt.yscale("log")
ax.set_title("Loss",fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
fig,axs = plt.subplots(figsize=(8,5),squeeze=False)
nbins=100
ax = axs[0,0]
levels = MaxNLocator(nbins=nbins).tick_values(Smags.min(),Smags.max())
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
im = ax.pcolormesh(x, y, Smags, cmap=cmap, norm=norm)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
plt.yscale("log")
ax.set_title("Staggered magnetization",fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
J = -1
gz = gzs[0]
cost1, Smags1, Smags12 = [], [], []
shots = 10000
Ll = 5
for i,gx in enumerate(gxs):
cost1.append(cost_function_single(thetas, L, num_trash, (i, 0), shots=shots))
init_state, _ = ising_groundstate(Ll, J, gx, gz)
Smags1.append(np.real(init_state.T.conj()@Mag(Ll,-1)@init_state))
#init_state, _ = ising_groundstate(Ll+2, J, gx, gz)
#Smags12.append(np.real(init_state.T.conj()@Mag(Ll+2,-1)@init_state))
gx = gxs[0]
cost2, Smags2, Smags22 = [], [], []
for i,gz in enumerate(gzs):
cost2.append(cost_function_single(thetas, L, num_trash, (0, i), shots=shots))
init_state, _ = ising_groundstate(Ll, J, gx, gz)
Smags2.append(np.real(init_state.T.conj()@Mag(Ll,-1)@init_state))
#init_state, _ = ising_groundstate(Ll+2, J, gx, gz)
#Smags22.append(np.real(init_state.T.conj()@Mag(Ll+2,-1)@init_state))
fig = plt.figure(figsize=(12,4))
ax1 = plt.subplot(121)
ax2 = ax1.twinx()
ax3 = plt.subplot(122)
ax4 = ax3.twinx()
ax1.set_xlabel(r"$g_z$")
ax1.set_ylabel("Cost")
ax1.plot(gzs, cost1, "x--")
ax2.plot(gzs, Smags1, "g-.")
#ax2.plot(gzs, Smags12, "r--")
ax2.set_ylabel("Staggered mag.")
ax2.set_xscale("log")
ax3.set_xlabel(r"$g_x$")
ax3.set_ylabel("Cost")
ax3.plot(gzs, cost2, "x--")
ax4.plot(gzs, Smags2, "g-.")
#ax4.plot(gzs, Smags22, "r--")
ax4.set_xscale("log")
ax4.set_ylabel("Staggered mag.")
fig.tight_layout()
params = [(13,13)]
num_trash = 4
print(gxs[params[0][0]], gzs[params[0][1]])
thetas, loss, accepted = optimize(params, L, num_trash)
thetas_ordered_phase_4 = thetas
fig = plt.figure(figsize=(12,4))
plt.subplot(1, 2, 1)
plt.plot(loss, "x--")
plt.yscale("log")
plt.xlabel("step")
plt.ylabel("Loss");
thetas = thetas_ordered_phase_2
num_trash = 2
points = 50
J = -1.0
x,y = np.meshgrid(gxs,gzs)
cost = np.zeros((points,points))
#Smags = np.zeros((points,points))
shots = 1000
for i,gx in enumerate(gxs):
for j,gz in enumerate(gzs):
cost[i,j] = cost_function_single(thetas, L, num_trash, (i,j), shots=shots)
#init_state, _ = ising_groundstate(5, J, gx, gz)
#Smags[i,j] = np.real(init_state.T.conj()@Mag(5,-1)@init_state)
fig,axs = plt.subplots(figsize=(8,5),squeeze=False)
nbins=100
ax = axs[0,0]
levels = MaxNLocator(nbins=nbins).tick_values(cost.min(),cost.max())
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
im = ax.pcolormesh(x, y, cost, cmap=cmap, norm=norm)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
plt.yscale("log")
ax.set_title("Loss",fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
fig,axs = plt.subplots(figsize=(8,5),squeeze=False)
nbins=100
ax = axs[0,0]
levels = MaxNLocator(nbins=nbins).tick_values(Smags.min(),Smags.max())
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
im = ax.pcolormesh(x, y, Smags, cmap=cmap, norm=norm)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
plt.yscale("log")
ax.set_title("Staggered magnetization",fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
# 3 trash qubits
fig,axs = plt.subplots(figsize=(8,5),squeeze=False)
nbins=100
ax = axs[0,0]
levels = MaxNLocator(nbins=nbins).tick_values(cost.min(),cost.max())
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
im = ax.pcolormesh(x, y, cost, cmap=cmap, norm=norm)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
plt.yscale("log")
ax.set_title("Loss",fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
fig,axs = plt.subplots(figsize=(8,5),squeeze=False)
nbins=100
ax = axs[0,0]
levels = MaxNLocator(nbins=nbins).tick_values(Smags.min(),Smags.max())
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
im = ax.pcolormesh(x, y, Smags, cmap=cmap, norm=norm)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
plt.yscale("log")
ax.set_title("Staggered magnetization",fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
# 2 trash qubits
fig,axs = plt.subplots(figsize=(8,5),squeeze=False)
cost_shift = np.abs(1-cost)
nbins=100
ax = axs[0,0]
levels = MaxNLocator(nbins=nbins).tick_values(cost_shift.min(),cost_shift.max())
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
im = ax.pcolormesh(x, y, cost_shift, cmap=cmap, norm=norm)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
plt.yscale("log")
ax.set_title("Loss",fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
fig,axs = plt.subplots(figsize=(8,5),squeeze=False)
nbins=100
ax = axs[0,0]
levels = MaxNLocator(nbins=nbins).tick_values(Smags.min(),Smags.max())
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
im = ax.pcolormesh(x, y, Smags, cmap=cmap, norm=norm)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
plt.yscale("log")
ax.set_title("Staggered magnetization",fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
# 2 trash qubits
cmap = plt.get_cmap("plasma")
def plot_result(cost, params):
fig,axs = plt.subplots(figsize=(8,5),squeeze=False)
nbins=100
ax = axs[0,0]
im = ax.pcolormesh(x, y, cost.T, cmap=cmap, shading="auto")
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
ax.set_yscale("log")
# plt.xscale("log")
ax.set_title("Loss",fontsize=20)
ax.set_ylabel(r"$V$",fontsize=24)
ax.set_xlabel(r"$\delta t$",fontsize=24)
ax.tick_params(labelsize=20)
for p in params:
d = deltas[p[0]]
V = Vs[p[1]]
ax.plot(d,V,"o",label="training",color="cyan")
L = 12
N = 50
#filename = f'data/wf_BH_L{L}.npz'
#filename = '../wf_BH_L20.npz'
#filename = 'data/wf_BH_L10_logV.npz'
filename = 'data/wf_BH_L12_logV_50x50.npz'
VQE_vals = np.load(filename, allow_pickle=True)
deltas = VQE_vals['deltat_array']
Vs = VQE_vals['V_array']
init_states = VQE_vals['wf_array']
# Vs = np.linspace(2,8,10)
# Vs = np.linspace(0,1,10)
Vs = np.logspace(-2,2,N)
# deltas = np.linspace(-1,1,10)
# deltas = np.linspace(-2,2,10)
deltas = np.linspace(-0.95,0.95,N)
init_states = init_states.reshape(N,N,-1) #(delta, V)
dd_vals = np.load('data/BH_dd_L12.npz', allow_pickle=True)
# deltas = dd_vals['deltat_array']
dd = dd_vals['dd_array'].reshape(N,N)
# init_states = VQE_vals['wf_array']
es_vals = np.load('data/BH_ES_L12.npz', allow_pickle=True)
es = es_vals['ES_array'].reshape(N,N)
print(deltas[48], Vs[1])
num_trash = 2
thetas, loss, accepted = optimize([init_states[-2,1]], L, num_trash, max_iter=1500, vqe=False)
thetas_bh12_10_2 = thetas
fig = plt.figure(figsize=(12,4))
plt.subplot(1, 2, 1)
plt.plot(loss, "x--")
#plt.yscale("log")
plt.xlabel("step")
plt.ylabel("Loss");
thetas = thetas_bh__1_2
points = 50
x,y = np.meshgrid(deltas,Vs)
# cost = np.zeros((points,points))
# Smags = np.zeros((points,points))
# shots = 1000
# for i,d in enumerate(deltas):
# print(i)
# for j,v in enumerate(Vs):
# cost[i,j] = cost_function_single(thetas, L, num_trash, init_states[i,j], shots=shots, vqe=False)
#cost_bh12_10_2 = cost
np.save(f'data_rike/cost_bh_L{L}_trash{num_trash}_d5_v50.npy', cost_bh12__1_2)
dd.shape
np.save('../data_rike/es.npy', es)
x,y = np.meshgrid(deltas,Vs)
np.save('../data_rike/x.npy', y)
x
params = [[-2,1]]
cost = dd
fig,axs = plt.subplots(figsize=(8,5),squeeze=False)
nbins=100
ax = axs[0,0]
levels = MaxNLocator(nbins=nbins).tick_values(cost.min(),cost.max())
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
im = ax.pcolormesh(y, x, cost.T, cmap=cmap, norm=norm)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
# plt.yscale("log")
ax.set_title("Loss",fontsize=20)
ax.set_xlabel(r"$V$",fontsize=24)
ax.set_ylabel(r"$\delta t$",fontsize=24)
ax.tick_params(labelsize=20)
for p in params:
d = deltas[p[0]]
V = Vs[p[1]]
ax.plot(V,d,"o",label="training",color="cyan")
#2 trash qubit
params = [[0,0]]
fig,axs = plt.subplots(figsize=(8,5),squeeze=False)
nbins=100
ax = axs[0,0]
levels = MaxNLocator(nbins=nbins).tick_values(cost.min(),cost.max())
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
im = ax.pcolormesh(y, x, cost.T, cmap=cmap, norm=norm)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
# plt.yscale("log")
ax.set_title("Loss",fontsize=20)
ax.set_xlabel(r"$V$",fontsize=24)
ax.set_ylabel(r"$\delta t$",fontsize=24)
ax.tick_params(labelsize=20)
for p in params:
d = deltas[p[0]]
V = Vs[p[1]]
ax.plot(V,d,"o",label="training",color="cyan")
#2 trash qubit
params = [[5,-1]]
fig,axs = plt.subplots(figsize=(8,5),squeeze=False)
nbins=100
ax = axs[0,0]
levels = MaxNLocator(nbins=nbins).tick_values(cost.min(),cost.max())
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
im = ax.pcolormesh(y, x, cost.T, cmap=cmap, norm=norm)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
# plt.yscale("log")
ax.set_title("Loss",fontsize=20)
ax.set_xlabel(r"$V$",fontsize=24)
ax.set_ylabel(r"$\delta t$",fontsize=24)
ax.tick_params(labelsize=20)
for p in params:
d = deltas[p[0]]
V = Vs[p[1]]
ax.plot(V,d,"o",label="training",color="cyan")
#2 trash qubit
params = [[0,0]]
fig,axs = plt.subplots(figsize=(8,5),squeeze=False)
nbins=100
ax = axs[0,0]
levels = MaxNLocator(nbins=nbins).tick_values(cost.min(),cost.max())
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
im = ax.pcolormesh(y, x, cost.T, cmap=cmap, norm=norm)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
# plt.yscale("log")
ax.set_title("Loss",fontsize=20)
ax.set_xlabel(r"$V$",fontsize=24)
ax.set_ylabel(r"$\delta t$",fontsize=24)
ax.tick_params(labelsize=20)
for p in params:
d = deltas[p[0]]
V = Vs[p[1]]
ax.plot(V,d,"o",label="training",color="cyan")
#2 trash qubit
params = [[-1,0]]
fig,axs = plt.subplots(figsize=(8,5),squeeze=False)
nbins=100
ax = axs[0,0]
levels = MaxNLocator(nbins=nbins).tick_values(cost.min(),cost.max())
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
im = ax.pcolormesh(y, x, cost.T, cmap=cmap, norm=norm)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
# plt.yscale("log")
ax.set_title("Loss",fontsize=20)
ax.set_xlabel(r"$V$",fontsize=24)
ax.set_ylabel(r"$\delta t$",fontsize=24)
ax.tick_params(labelsize=20)
for p in params:
d = deltas[p[0]]
V = Vs[p[1]]
ax.plot(V,d,"o",label="training",color="cyan")
#2 trash qubit
params = [[5,-1]]
fig,axs = plt.subplots(figsize=(8,5),squeeze=False)
nbins=100
ax = axs[0,0]
levels = MaxNLocator(nbins=nbins).tick_values(cost.min(),cost.max())
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
im = ax.pcolormesh(y, x, cost.T, cmap=cmap, norm=norm)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
# plt.yscale("log")
ax.set_title("Loss",fontsize=20)
ax.set_xlabel(r"$V$",fontsize=24)
ax.set_ylabel(r"$\delta t$",fontsize=24)
ax.tick_params(labelsize=20)
for p in params:
d = deltas[p[0]]
V = Vs[p[1]]
ax.plot(V,d,"o",label="training",color="cyan")
#2 trash qubit
cost = np.load('data_rike/cost_bh_L12_trash2_d48_v5_seed10.npy', allow_pickle=True)
# cost = np.load('data_rike/cost_bh_L12_trash6_d25_v47_seed29.npy', allow_pickle=True)
params = [[25,47]]
cost = es
fig,axs = plt.subplots(figsize=(8,5),squeeze=False)
nbins=100
ax = axs[0,0]
levels = MaxNLocator(nbins=nbins).tick_values(cost.min(),cost.max())
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
im = ax.pcolormesh(y, x, cost.T, cmap=cmap, norm=norm)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
# plt.yscale("log")
# ax.set_title("Density-density correlation",fontsize=22)
# ax.set_title("Loss",fontsize=22)
ax.set_title("Degeneracy entanglement spectrum",fontsize=22)
ax.set_xlabel(r"$V$",fontsize=24)
ax.set_ylabel(r"$\delta t$",fontsize=24)
ax.tick_params(labelsize=20)
# for p in params:
# d = deltas[p[0]]
# V = Vs[p[1]]
# ax.plot(V,d,"o",label="training",color="cyan")
fig.tight_layout()
# plt.savefig('plots_rike/cost_bh_L12_trash6_d25_v47_seed29.png', dpi=300)
plt.savefig('plots_rike/es_bh_L12.pdf', dpi=300)
seeds = np.arange(20) + 10
for seed in seeds:
# cost = np.load(f'data_rike/cost_bh_L12_trash2_d48_v5_seed{seed}.npy', allow_pickle=True) # 7, 10
# cost = np.load(f'data_rike/cost_bh_L12_trash4_d48_v5_seed{seed}.npy', allow_pickle=True) # 3
# cost = np.load(f'data_rike/cost_bh_L12_trash6_d48_v5_seed{seed}.npy', allow_pickle=True) # 0
# params = [[48,5]]
# cost = np.load(f'data_rike/cost_bh_L12_trash2_d2_v5_seed{seed}.npy', allow_pickle=True) # -2
# cost = np.load(f'data_rike/cost_bh_L12_trash4_d2_v5_seed{seed}.npy', allow_pickle=True) # 0
# cost = np.load(f'data_rike/cost_bh_L12_trash6_d2_v5_seed{seed}.npy', allow_pickle=True) # 2, 10
# params = [[2,5]]
# cost = np.load(f'data_rike/cost_bh_L12_trash2_d10_v10_seed{seed}.npy', allow_pickle=True) # 3, 6
# cost = np.load(f'data_rike/cost_bh_L12_trash4_d10_v10_seed{seed}.npy', allow_pickle=True) # -8
# cost = np.load(f'data_rike/cost_bh_L12_trash6_d10_v10_seed{seed}.npy', allow_pickle=True) # 6, 12
# params = [[10,10]]
# cost = np.load(f'data_rike/cost_bh_L12_trash2_d25_v47_seed{seed}.npy', allow_pickle=True) # 1
# cost = np.load(f'data_rike/cost_bh_L12_trash4_d25_v47_seed{seed}.npy', allow_pickle=True) # 6
cost = np.load(f'data_rike/cost_bh_L12_trash6_d25_v47_seed{seed}.npy', allow_pickle=True) # 1, 12, 19
params = [[25,47]]
fig,axs = plt.subplots(figsize=(8,5),squeeze=False)
nbins=100
ax = axs[0,0]
levels = MaxNLocator(nbins=nbins).tick_values(cost.min(),cost.max())
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
im = ax.pcolormesh(y, x, cost.T, cmap=cmap, norm=norm)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
# plt.yscale("log")
ax.set_title("Loss",fontsize=20)
ax.set_xlabel(r"$V$",fontsize=24)
ax.set_ylabel(r"$\delta t$",fontsize=24)
ax.tick_params(labelsize=20)
for p in params:
d = deltas[p[0]]
V = Vs[p[1]]
ax.plot(V,d,"o",label="training",color="cyan")
seeds = np.arange(20) + 10
for seed in seeds:
cost = np.load(f'data_rike/cost_bh_L12_trash2_d48_v5_seed{seed}.npy', allow_pickle=True) # 7, 10
params = [[48,5]]
# cost = np.load(f'data_rike/cost_bh_L12_trash2_d2_v5_seed{seed}.npy', allow_pickle=True) # -2
# cost = np.load(f'data_rike/cost_bh_L12_trash4_d2_v5_seed{seed}.npy', allow_pickle=True)
# params = [[2,5]]
# cost = np.load(f'data_rike/cost_bh_L12_trash2_d10_v10_seed{seed}.npy', allow_pickle=True) # 3, 6
# cost = np.load(f'data_rike/cost_bh_L12_trash4_d10_v10_seed{seed}.npy', allow_pickle=True)
# params = [[10,10]]
fig,axs = plt.subplots(figsize=(8,5),squeeze=False)
nbins=100
ax = axs[0,0]
levels = MaxNLocator(nbins=nbins).tick_values(cost.min(),cost.max())
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
im = ax.pcolormesh(y, x, cost.T, cmap=cmap, norm=norm)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
# plt.yscale("log")
ax.set_title("Loss",fontsize=20)
ax.set_xlabel(r"$V$",fontsize=24)
ax.set_ylabel(r"$\delta t$",fontsize=24)
ax.tick_params(labelsize=20)
for p in params:
d = deltas[p[0]]
V = Vs[p[1]]
ax.plot(V,d,"o",label="training",color="cyan")
plt.show()
seeds = np.arange(20) + 10
for seed in seeds:
# loss = np.load(f'data_rike/cost_bh_L12_trash2_d48_v5_seed{seed}.npy', allow_pickle=True)
# params = [[48,5]]
# loss = np.load(f'data_rike/cost_bh_L12_trash2_d2_v5_seed{seed}.npy', allow_pickle=True)
# loss = np.load(f'data_rike/cost_bh_L12_trash4_d2_v5_seed{seed}.npy', allow_pickle=True)
# params = [[2,5]]
# loss = np.load(f'data_rike/loss_bh_L12_trash2_d10_v10_seed{seed}.npy', allow_pickle=True)
# loss = np.load(f'data_rike/loss_bh_L12_trash4_d10_v10_seed{seed}.npy', allow_pickle=True)
loss = np.load(f'data_rike/loss_bh_L12_trash2_d25_v47_seed{seed}.npy', allow_pickle=True)
# loss = np.load(f'data_rike/loss_bh_L12_trash4_d25_v47_seed{seed}.npy', allow_pickle=True)
fig = plt.figure(figsize=(12,4))
plt.subplot(1, 2, 1)
plt.plot(loss, "x--")
plt.yscale("log")
plt.xlabel("step")
plt.ylabel("Loss")
|
https://github.com/Qottmann/Quantum-anomaly-detection
|
Qottmann
|
import time
import itertools
import random
import numpy as np
from scipy.optimize import minimize, basinhopping
from qiskit import *
from qiskit.quantum_info import Statevector
from qiskit.algorithms.optimizers import COBYLA, L_BFGS_B, SPSA
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
from matplotlib.colors import BoundaryNorm
from modules.utils import Mag
%matplotlib inline
cmap = plt.get_cmap("plasma") #'viridis'
# from: https://tenpy.readthedocs.io/en/latest/toycodes/tfi_exact.html
import numpy as np
import scipy.sparse as sparse
import scipy.sparse.linalg.eigen.arpack as arp
import warnings
import scipy.integrate
def ising_groundstate(L, J, gx, gz=0): # gx is transverse field, gz the longitudinal
"""For comparison: obtain ground state energy from exact diagonalization.
Exponentially expensive in L, only works for small enough `L` <~ 20.
"""
if L >= 20:
warnings.warn("Large L: Exact diagonalization might take a long time!")
# get single site operaors
sx = sparse.csr_matrix(np.array([[0., 1.], [1., 0.]]))
sz = sparse.csr_matrix(np.array([[1., 0.], [0., -1.]]))
id = sparse.csr_matrix(np.eye(2))
sx_list = [] # sx_list[i] = kron([id, id, ..., id, sx, id, .... id])
sz_list = []
for i_site in range(L):
x_ops = [id] * L
z_ops = [id] * L
x_ops[i_site] = sx
z_ops[i_site] = sz
X = x_ops[0]
Z = z_ops[0]
for j in range(1, L):
X = sparse.kron(X, x_ops[j], 'csr')
Z = sparse.kron(Z, z_ops[j], 'csr')
sx_list.append(X)
sz_list.append(Z)
H_zz = sparse.csr_matrix((2**L, 2**L))
H_z = sparse.csr_matrix((2**L, 2**L))
H_x = sparse.csr_matrix((2**L, 2**L))
for i in range(L - 1):
H_zz = H_zz + sz_list[i] * sz_list[(i + 1) % L]
for i in range(L):
H_z = H_z + sz_list[i]
H_x = H_x + sx_list[i]
H = -J * H_zz - gx * H_x - gz * H_z
E, V = arp.eigsh(H, k=1, which='SA', return_eigenvectors=True, ncv=20)
return V[:,0], E[0]
init_state, E = ising_groundstate(6, 1., 1., 1.)
# Use Aer's qasm_simulator
backend_sim = Aer.get_backend('qasm_simulator')
L = 6 # system size
thetas = np.random.uniform(0, 2*np.pi, 2*L+2) # initial parameters without feature encoding
# thetas = np.random.uniform(0, 2*np.pi, (2*L+2, 2)) # initial parameters with feature encoding
def prepare_circuit(init_state=None, measurement=True):
qreg = QuantumRegister(L, 'q')
creg = ClassicalRegister(2, 'c')
circ = QuantumCircuit(qreg, creg)
entangler_map1 = [(5, 4), (5, 3), (5, 1), (4, 2), (4, 0)]
entangler_map2 = [(5, 4), (5, 2), (4, 3), (5, 0), (4, 1)]
circ += circuit.library.TwoLocal(L, 'ry', 'cz', entanglement = [entangler_map1, entangler_map2], reps=2, insert_barriers=True, skip_final_rotation_layer=True)
circ.ry(circuit.Parameter('θ1'), 4)
circ.ry(circuit.Parameter('θ2'), 5)
if measurement:
circ.measure(qreg[4], creg[0])
circ.measure(qreg[5], creg[1])
if init_state is not None: circ.initialize(init_state, qreg)
return circ
# same circuit as above (more explicit)
def prepare_circuit2(thetas, init_state=None, measurement=True):
qreg = QuantumRegister(L, 'q')
creg = ClassicalRegister(2, 'c')
circ = QuantumCircuit(qreg, creg)
if init_state is not None: circ.initialize(init_state, qreg)
for i,t in enumerate(thetas[2:(L+2)]):
# for i,t in enumerate(thetas[0:L]):
circ.ry(t, i)
circ.cz(5,4)
circ.cz(5,3)
circ.cz(5,1)
circ.cz(4,2)
circ.cz(4,0)
circ.barrier()
for i,t in enumerate(thetas[(L+2):]):
# for i,t in enumerate(thetas[L:2*L]):
circ.ry(t, i)
circ.cz(5,4)
circ.cz(5,2)
circ.cz(4,3)
circ.cz(5,0)
circ.cz(4,1)
circ.barrier()
# circ.ry(thetas[2*L], 4)
# circ.ry(thetas[2*L+1], 5)
circ.ry(thetas[0], 4)
circ.ry(thetas[1], 5)
circ.barrier()
if measurement:
circ.measure(qreg[4], creg[0])
circ.measure(qreg[5], creg[1])
return circ
def feature_encoding(thetas, x):
""" thetas: parameters to be optimized, x: Ising model parameter (eg. field) """
new_thetas = []
thetas = thetas.reshape((-1,2))
for theta in thetas:
new_thetas.append(theta[0] * x + theta[1])
return new_thetas
circ = prepare_circuit()
circ.draw('mpl') # trash qubits are at the bottom
circ = prepare_circuit2(thetas)
# circ = prepare_circuit(feature_encoding(thetas, .6))
circ.draw('mpl') # identical to above
np.random.seed(123)
def run_circuit(thetas, init_state, shots=100000):
circ = prepare_circuit2(thetas, init_state)
# Execute the circuit on the qasm simulator.
job_sim = execute(circ, backend_sim, shots=shots)#, seed_simulator=123, seed_transpiler=234)#, memory=True)
# Grab the results from the job
result_sim = job_sim.result()
counts = result_sim.get_counts(circ)
# print(counts)
# mems = result_sim.get_memory(circ)
# print(mems)
return counts
# run_circuit(thetas, init_state)['11']
init_state, E = ising_groundstate(6, 1., 1.6)
run_circuit(thetas, init_state)
def encoder_evolv(thetas, J, gx, gz, param_encoding=False, x=0):
""" full quantum state evolution with encoder part of circuit """
if param_encoding: thetas = feature_encoding(thetas, x)
circ = prepare_circuit2(thetas, measurement=False)
init_state, _ = ising_groundstate(L, J, g, gx)
state = Statevector(init_state)
state = state.evolve(circ)
traced_state = qiskit.quantum_info.partial_trace(state,range(0,4))
return traced_state
def encoder_decoder_evolv(thetas, J, gx, gz, param_encoding=False, x=0):
""" full quantum state evolution with encoder & decoder part of circuit """
if param_encoding: thetas = feature_encoding(thetas, x)
circ = prepare_circuit2(thetas, measurement=False)
circ_dagger = circ.inverse()
# encoder part
init_state, _ = ising_groundstate(L, J, gx, gz)
state = Statevector(init_state)
state = state.evolve(circ)
traced_state = qiskit.quantum_info.partial_trace(state,[4,5])
# decoder part
ancilla = np.zeros(4)
ancilla[0] = 1
ancilla = qiskit.quantum_info.DensityMatrix(ancilla)
new_state = traced_state.expand(ancilla)
final_state = new_state.evolve(circ_dagger)
fid = qiskit.quantum_info.state_fidelity(Statevector(init_state), final_state)
return final_state, init_state, fid
# old version
init_state, E = ising_groundstate(6, 1., 0.0)
circ.initialize(init_state, range(L))
# new_thetas = feature_encoding(thetas, g) # comment out when running w/o feature encoding
new_thetas = thetas
t_qc = transpile(circ, backend=backend_sim, seed_transpiler=234)
qobj = compiler.assemble(t_qc.bind_parameters(new_thetas), backend=backend_sim, seed_simulator=123, shots=100000)
result = backend_sim.run(qobj).result()
out = result.get_counts()
out
#reset random seed
np.random.seed(123)
random.seed(123)
def cost_function_single(thetas, J, gx, gz, shots=1000, param_encoding=False, x=0):
""" Optimizes circuit """
init_state, _ = ising_groundstate(L, J, gx, gz)
if param_encoding: thetas = feature_encoding(thetas, x)
out = run_circuit(thetas, init_state, shots=shots)
cost = out.get('11', 0)*2 + out.get('01', 0) + out.get('10', 0)
return cost/shots
def cost_function(thetas, ising_params, shots=1000, param_encoding=False, x=0):
""" Optimizes circuit """
cost = 0.
n_samples = len(ising_params)
for i, p in enumerate(ising_params):
J, gx, gz = p
if param_encoding:
cost += cost_function_single(thetas, J, gx, gz, shots, param_encoding, x[i])
else:
cost += cost_function_single(thetas, J, gx, gz, shots, param_encoding)
return cost/n_samples
def optimize(ising_params, thetas=None, shots=1000, max_iter=400, param_encoding=False, x=0):
np.random.seed(123)
if thetas is None:
n_params = (2*L+2)*2 if param_encoding else (2*L+2)
thetas = np.random.uniform(0, 2*np.pi, n_params) # initial parameters without feature encoding
print("Initial cost: {:.3f}".format(cost_function(thetas, ising_params, shots, param_encoding, x)))
print(thetas)
counts, values, accepted = [], [], []
def store_intermediate_result(eval_count, parameters, mean, std, ac):
# counts.append(eval_count)
values.append(mean)
accepted.append(ac)
# Initialize optimizer
# optimizer = COBYLA(maxiter=max_iter, tol=0.0001)
# optimizer = L_BFGS_B(maxfun=300, maxiter=max_iter)#, factr=10, iprint=- 1, epsilon=1e-08)
optimizer = SPSA(maxiter=max_iter,
#blocking=True,
callback=store_intermediate_result,
#learning_rate=1e-1,
#perturbation=0.4
) # recommended from qiskit (first iteraction takes quite long)
# to reduce time figure out optimal learning rate and perturbation in advance
start_time = time.time()
ret = optimizer.optimize(
num_vars=len(thetas),
objective_function=(lambda thetas: cost_function(thetas, ising_params, shots, param_encoding, x)),
initial_point=thetas
)
print("Time: {:.5f} sec".format(time.time()-start_time))
print(ret)
return ret[0], values, accepted
params = [(-1.0, 0.1, 0.1)]
thetas, loss, accepted = optimize(params, max_iter=40)
params = [(-1.0, 0.1, 0.1)]
thetas, loss, accepted = optimize(params, max_iter=40)
fig = plt.figure(figsize=(12,4))
plt.subplot(1, 2, 1)
plt.plot(loss, "x--")
# plt.yscale("log")
plt.xlabel("step")
plt.ylabel("Loss")
plt.subplot(1, 2, 2)
plt.plot(accepted, "x--")
plt.xlabel("step")
plt.ylabel("Accepted");
Js = [-1]*2
gxs = [0.1,10]
gzs = [10,0.1]
params = list(zip(Js, gxs, gzs))
thetas, loss, accepted = optimize(params)
thetas_normal_phase = thetas
thetas = thetas_normal_phase
fig = plt.figure(figsize=(12,4))
plt.subplot(1, 2, 1)
plt.plot(loss, "x--")
plt.yscale("log")
plt.xlabel("step")
plt.ylabel("Loss");
points = 50
J = -1.0
gxs = np.logspace(-2, 2, points)
gzs = np.logspace(-2, 2, points)
x,y = np.meshgrid(gxs,gzs)
cost = np.zeros((points,points))
Smags = np.zeros((points,points))
shots = 1000
for i,gx in enumerate(gxs):
for j,gz in enumerate(gzs):
cost[i,j] = cost_function_single(thetas, J, gx, gz, shots=shots)
init_state, _ = ising_groundstate(5, J, gx, gz)
Smags[i,j] = init_state.T.conj()@Mag(5,-1)@init_state
fig,axs = plt.subplots(figsize=(8,5),squeeze=False)
nbins=100
ax = axs[0,0]
levels = MaxNLocator(nbins=nbins).tick_values(cost.min(),cost.max())
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
im = ax.pcolormesh(x, y, cost, cmap=cmap, norm=norm)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
plt.yscale("log")
ax.set_title("Loss",fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
fig,axs = plt.subplots(figsize=(8,5),squeeze=False)
nbins=100
ax = axs[0,0]
levels = MaxNLocator(nbins=nbins).tick_values(Smags.min(),Smags.max())
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
im = ax.pcolormesh(x, y, Smags, cmap=cmap, norm=norm)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
plt.yscale("log")
ax.set_title("Staggered magnetization",fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
J = -1
gxs = np.logspace(-2, 2, 50)
gz = .01
cost1, Smags1, Smags12 = [], [], []
shots = 10000
Ll = 5
for gx in gxs:
cost1.append(cost_function_single(thetas, J, gx, gz, shots=shots))
init_state, _ = ising_groundstate(Ll, J, gx, gz)
Smags1.append(np.real(init_state.T.conj()@Mag(Ll,-1)@init_state))
init_state, _ = ising_groundstate(Ll+2, J, gx, gz)
Smags12.append(np.real(init_state.T.conj()@Mag(Ll+2,-1)@init_state))
gzs = np.logspace(-2, 2, 50)
gx = .01
cost2, Smags2, Smags22 = [], [], []
for gz in gzs:
cost2.append(cost_function_single(thetas, J, gx, gz, shots=shots))
init_state, _ = ising_groundstate(Ll, J, gx, gz)
Smags2.append(np.real(init_state.T.conj()@Mag(Ll,-1)@init_state))
init_state, _ = ising_groundstate(Ll+2, J, gx, gz)
Smags22.append(np.real(init_state.T.conj()@Mag(Ll+2,-1)@init_state))
fig = plt.figure(figsize=(12,4))
ax1 = plt.subplot(121)
ax2 = ax1.twinx()
ax3 = plt.subplot(122)
ax4 = ax3.twinx()
ax1.set_xlabel(r"$g_z$")
ax1.set_ylabel("Cost")
ax1.plot(gzs, cost1, "x--")
ax2.plot(gzs, Smags1, "g-.")
ax2.plot(gzs, Smags12, "r--")
ax2.set_ylabel("Staggered mag.")
ax2.set_xscale("log")
ax3.set_xlabel(r"$g_x$")
ax3.set_ylabel("Cost")
ax3.plot(gzs, cost2, "x--")
ax4.plot(gzs, Smags2, "g-.")
ax4.plot(gzs, Smags22, "r--")
ax4.set_xscale("log")
ax4.set_ylabel("Staggered mag.")
fig.tight_layout()
fig = plt.figure(figsize=(15,4))
plt.subplot(1, 3, 1)
J, g, gx = -1., .1, 10.
traced_state = encoder_evolv(thetas, J, gx, gz)
plt.imshow(np.real(traced_state.data), cmap='RdBu', vmin=-1.0, vmax=1.0, interpolation='none')
plt.colorbar()
plt.axis('off');
plt.subplot(1, 3, 2)
J, g, gx = -1., 10., .1
traced_state = encoder_evolv(thetas, J, gx, gz)
plt.imshow(np.real(traced_state.data), cmap='RdBu', vmin=-1.0, vmax=1.0, interpolation='none')
plt.colorbar()
plt.axis('off');
plt.subplot(1, 3, 3)
J, g, gx = -1., .1, .1
traced_state = encoder_evolv(thetas, J, gx, gz)
plt.imshow(np.real(traced_state.data), cmap='RdBu', vmin=-1.0, vmax=1.0, interpolation='none')
plt.colorbar()
plt.axis('off');
# upper left corner corresponds to |00><00|, the first two images are the ones for which circuit was optimized
points = 50
J = 1.0
gxs = np.logspace(-2, 2, points)
gzs = np.logspace(-2, 2, points)
x,y = np.meshgrid(gxs,gzs)
cost = np.zeros((points,points))
shots = 1000
for i,gx in enumerate(gxs):
for j,gz in enumerate(gzs):
cost[i,j] = cost_function_single(thetas, J, gx, gz, shots=shots)
fig,axs = plt.subplots(figsize=(8,5),squeeze=False)
nbins=100
ax = axs[0,0]
levels = MaxNLocator(nbins=nbins).tick_values(cost.min(),cost.max())
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
im = ax.pcolormesh(x, y, cost, cmap=cmap, norm=norm)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
plt.yscale("log")
ax.set_title("Loss",fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
np.random.seed(123)
thetas_random = np.random.uniform(0, 2*np.pi, 2*L+2) # initial parameters without feature encoding
points = 50
J = -1.0
gxs = np.logspace(-2, 2, points)
gzs = np.logspace(-2, 2, points)
x,y = np.meshgrid(gxs,gzs)
cost = np.zeros((points,points))
shots = 1000
for i,gx in enumerate(gxs):
for j,gz in enumerate(gzs):
cost[i,j] = cost_function_single(thetas_random, J, gx, gz, shots=shots)
fig,axs = plt.subplots(figsize=(8,5),squeeze=False)
nbins=100
ax = axs[0,0]
levels = MaxNLocator(nbins=nbins).tick_values(cost.min(),cost.max())
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
im = ax.pcolormesh(x, y, cost, cmap=cmap, norm=norm)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
plt.yscale("log")
ax.set_title("Loss",fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
params = [(-1.0, 0.1, 0.1)]
thetas, loss, accepted = optimize(params)
thetas_ordered_phase = thetas
thetas = thetas_ordered_phase
fig = plt.figure(figsize=(12,4))
plt.subplot(1, 2, 1)
plt.plot(loss, "x--")
plt.yscale("log")
plt.xlabel("step")
plt.ylabel("Loss");
points = 50
J = -1.0
gxs = np.logspace(-2, 2, points)
gzs = np.logspace(-2, 2, points)
x,y = np.meshgrid(gxs,gzs)
cost = np.zeros((points,points))
Smags = np.zeros((points,points))
shots = 1000
for i,gx in enumerate(gxs):
for j,gz in enumerate(gzs):
cost[i,j] = cost_function_single(thetas, J, gx, gz, shots=shots)
init_state, _ = ising_groundstate(5, J, gx, gz)
Smags[i,j] = init_state.T.conj()@Mag(5,-1)@init_state
fig,axs = plt.subplots(figsize=(8,5),squeeze=False)
nbins=100
ax = axs[0,0]
levels = MaxNLocator(nbins=nbins).tick_values(cost.min(),cost.max())
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
im = ax.pcolormesh(x, y, cost, cmap=cmap, norm=norm)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
plt.yscale("log")
ax.set_title("Loss",fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
fig,axs = plt.subplots(figsize=(8,5),squeeze=False)
nbins=100
ax = axs[0,0]
levels = MaxNLocator(nbins=nbins).tick_values(Smags.min(),Smags.max())
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
im = ax.pcolormesh(x, y, Smags, cmap=cmap, norm=norm)
cbar = fig.colorbar(im, ax=ax)
cbar.ax.tick_params(labelsize=20)
plt.xscale("log")
plt.yscale("log")
ax.set_title("Staggered magnetization",fontsize=20)
ax.set_xlabel(r"$g_z$",fontsize=24)
ax.set_ylabel(r"$g_x$",fontsize=24)
ax.tick_params(labelsize=20)
J = -1
gxs = np.logspace(-2, 2, 50)
gz = .01
cost1, Smags1, Smags12 = [], [], []
shots = 10000
Ll = 5
for gx in gxs:
cost1.append(cost_function_single(thetas, J, gx, gz, shots=shots))
init_state, _ = ising_groundstate(Ll, J, gx, gz)
Smags1.append(np.real(init_state.T.conj()@Mag(Ll,-1)@init_state))
init_state, _ = ising_groundstate(Ll+2, J, gx, gz)
Smags12.append(np.real(init_state.T.conj()@Mag(Ll+2,-1)@init_state))
gzs = np.logspace(-2, 2, 50)
gx = .01
cost2, Smags2, Smags22 = [], [], []
for gz in gzs:
cost2.append(cost_function_single(thetas, J, gx, gz, shots=shots))
init_state, _ = ising_groundstate(Ll, J, gx, gz)
Smags2.append(np.real(init_state.T.conj()@Mag(Ll,-1)@init_state))
init_state, _ = ising_groundstate(Ll+2, J, gx, gz)
Smags22.append(np.real(init_state.T.conj()@Mag(Ll+2,-1)@init_state))
Js = np.linspace(-10, 10, 50)
gx = 0.1
gz = 0.1
cost3, Smags3, Smags32 = [], [], []
for J in Js:
cost3.append(cost_function_single(thetas, J, gx, gz, shots=shots))
init_state, _ = ising_groundstate(Ll, J, gx, gz)
Smags3.append(np.real(init_state.T.conj()@Mag(Ll,-1)@init_state))
init_state, _ = ising_groundstate(Ll+2, J, gx, gz)
Smags32.append(np.real(init_state.T.conj()@Mag(Ll+2,-1)@init_state))
fig = plt.figure(figsize=(18,4))
ax1 = plt.subplot(131)
ax2 = ax1.twinx()
ax3 = plt.subplot(132)
ax4 = ax3.twinx()
ax5 = plt.subplot(133)
ax6 = ax5.twinx()
ax1.set_xlabel(r"$g_z$")
ax1.set_ylabel("Cost")
ax1.plot(gzs, cost1, "x--")
ax2.plot(gzs, Smags1, "g-.")
ax2.plot(gzs, Smags12, "r--")
ax2.set_ylabel("Staggered mag.")
ax2.set_xscale("log")
ax3.set_xlabel(r"$g_x$")
ax3.set_ylabel("Cost")
ax3.plot(gzs, cost2, "x--")
ax4.plot(gzs, Smags2, "g-.")
ax4.plot(gzs, Smags22, "r--")
ax4.set_xscale("log")
ax4.set_ylabel("Staggered mag.")
ax5.set_xlabel(r"$J$")
ax5.set_ylabel("Cost")
ax5.plot(Js, cost3, "x--")
ax6.plot(Js, Smags3, "g-.")
ax6.plot(Js, Smags32, "r--")
ax6.set_ylabel("Staggered mag.")
fig.tight_layout();
fig = plt.figure(figsize=(15,4))
plt.subplot(1, 3, 1)
J, g, gx = -1., .1, .1
traced_state = encoder_evolv(thetas, J, gx, gz)
plt.imshow(np.real(traced_state.data), cmap='RdBu', vmin=-1.0, vmax=1.0, interpolation='none')
plt.colorbar()
plt.axis('off');
plt.subplot(1, 3, 2)
J, g, gx = -1., 2., 2.
traced_state = encoder_evolv(thetas, J, gx, gz)
plt.imshow(np.real(traced_state.data), cmap='RdBu', vmin=-1.0, vmax=1.0, interpolation='none')
plt.colorbar()
plt.axis('off');
plt.subplot(1, 3, 3)
J, g, gx = 1., .1, .1
traced_state = encoder_evolv(thetas, J, gx, gz)
plt.imshow(np.real(traced_state.data), cmap='RdBu', vmin=-1.0, vmax=1.0, interpolation='none')
plt.colorbar()
plt.axis('off');
# upper left corner corresponds to |00><00|, the first image is the one for which circuit was optimized
J, gx, gz = -1., .1, .1
gzs = np.logspace(-2, 2, 50)
fid1 = []
for gz in gzs:
fid1.append(encoder_decoder_evolv(thetas, J, gx, gz)[2])
J, gx, gz = -1., .1, .1
gxs = np.logspace(-2, 2, 50)
fid2 = []
for gx in gxs:
fid2.append(encoder_decoder_evolv(thetas, J, gx, gz)[2])
J, gx, gz = -1., .1, .1
Js = np.linspace(-10, 10, 50)
fid3 = []
for J in Js:
fid3.append(encoder_decoder_evolv(thetas, J, gx, gz)[2])
fig = plt.figure(figsize=(18,4))
ax1 = plt.subplot(131)
ax2 = ax1.twinx()
ax3 = plt.subplot(132)
ax4 = ax3.twinx()
ax5 = plt.subplot(133)
ax6 = ax5.twinx()
ax1.set_xlabel(r"$g_z$")
ax1.set_ylabel("Fidelity")
ax1.plot(gzs, fid1, "x--")
ax2.plot(gzs, Smags2, "g-.")
ax2.plot(gzs, Smags22, "r--")
ax2.set_ylabel("Staggered mag.")
ax2.set_xscale("log")
ax3.set_xlabel(r"$g_x$")
ax3.set_ylabel("Fidelity")
ax3.plot(gzs, fid2, "x--")
ax4.plot(gzs, Smags1, "g-.")
ax4.plot(gzs, Smags12, "r--")
ax4.set_xscale("log")
ax4.set_ylabel("Staggered mag.")
ax5.set_xlabel(r"$J$")
ax5.set_ylabel("Fidelity")
ax5.plot(Js, fid3, "x--")
ax6.plot(Js, Smags3, "g-.")
ax6.plot(Js, Smags32, "r--")
ax6.set_ylabel("Staggered mag.")
fig.tight_layout();
fig = plt.figure(figsize=(18,4))
plt.subplot(1, 3, 1)
plt.plot(gzs, fid1, "x--")
plt.xscale("log")
plt.xlabel(r"$g_z$")
plt.ylabel("Fidelity");
plt.subplot(1, 3, 2)
plt.plot(gxs, fid2, "x--")
plt.xscale("log")
plt.xlabel(r"$g_x$")
plt.ylabel("Fidelity");
plt.subplot(1, 3, 3)
plt.plot(gxs, fid3, "x--")
plt.xscale("log")
plt.xlabel(r"$g_x$")
plt.ylabel("Fidelity");
# with feature encoding
Js = [1.0]
gxs = np.linspace(0.1, 0.6, 10)
gzs = [0.01]
a = [Js, gxs, gzs]
params = list(itertools.product(*a))
thetas, loss, accepted = optimize(params, max_iter=1000, param_encoding=True, x=gxs)
thetas2 = thetas
# without feature encoding
Js = [1.0]
gxs = np.linspace(0.1, 0.6, 10)
gzs = [0.01]
a = [Js, gxs, gzs]
params = list(itertools.product(*a))
thetas, loss2, accepted = optimize(params, max_iter=200, param_encoding=False)
thetas3 = thetas
fig = plt.figure(figsize=(12,4))
plt.subplot(1, 2, 1)
plt.plot(loss, "x--")
plt.plot(loss2, "o-.")
plt.yscale("log")
plt.xlabel("step")
plt.ylabel("Loss")
J = 1.
gxs = np.linspace(0.01, 1.2, 50)
gz = .01
cost1, cost2 = [], []
shots = 10000
param_encoding=False
for gx in gxs:
cost1.append(cost_function_single(thetas2, J, gx, gz, shots=shots, param_encoding=True, x=gx))
cost2.append(cost_function_single(thetas3, J, gx, gz, shots=shots, param_encoding=False, x=gx))
fig = plt.figure(figsize=(18,4))
plt.subplot(1, 3, 1)
plt.plot(gxs, cost1, "x--")
plt.plot(gxs, cost2, "o-.")
# plt.xscale("log")
plt.vlines(0.6, 0, 1.2, colors="tab:gray", linestyles='dotted')
plt.vlines(0.1, 0, 1.2, colors="tab:gray", linestyles='dotted')
plt.xlabel(r"$g_x$")
plt.ylabel("Cost")
fig = plt.figure(figsize=(10,10))
plt.subplot(2, 2, 1)
J, g, gx = 1., .6, .01
traced_state = encoder_evolv(thetas2, J, gx, gz, param_encoding=True, x=gx)
plt.imshow(np.real(traced_state.data), cmap='RdBu', vmin=-1.0, vmax=1.0, interpolation='none')
plt.colorbar()
plt.axis('off');
plt.subplot(2, 2, 2)
J, g, gx = 1., .6, .01
traced_state = encoder_evolv(thetas3, J, gx, gz)
plt.imshow(np.real(traced_state.data), cmap='RdBu', vmin=-1.0, vmax=1.0, interpolation='none')
plt.colorbar()
plt.axis('off');
plt.subplot(2, 2, 3)
J, g, gx = 1., .75, .01
traced_state = encoder_evolv(thetas2, J, gx, gz, param_encoding=True, x=gx)
plt.imshow(np.real(traced_state.data), cmap='RdBu', vmin=-1.0, vmax=1.0, interpolation='none')
plt.colorbar()
plt.axis('off');
plt.subplot(2, 2, 4)
J, g, gx = 1., .75, .01
traced_state = encoder_evolv(thetas3, J, gx, gz)
plt.imshow(np.real(traced_state.data), cmap='RdBu', vmin=-1.0, vmax=1.0, interpolation='none')
plt.colorbar()
plt.axis('off');
# same as Fig. 5
|
https://github.com/suvoooo/Qubits-Qiskit
|
suvoooo
|
!pip3 install qiskit
import qiskit as q
!pip3 install pylatexenc
import math
import numpy as np
statevec_sim = q.Aer.get_backend("statevector_simulator")
def statevec_exec(circ):
statevec = q.execute(circ, backend=statevec_sim).result().get_statevector()
return statevec
# qr = q.QuantumRegister(2, name="q")
# cr = q.ClassicalRegister(2, name="cr")
def build_circ(num_qbits, num_cbits):
qr = q.QuantumRegister(num_qbits)
cr = q.ClassicalRegister(num_cbits)
final_circ = q.QuantumCircuit(qr, cr)
return final_circ, qr, cr
# simple_circuit, qr, cr = build_circ(2)
# simple_circuit.draw()
def h_gates(qcirc, a, b): # 2 inputs and h gates in parallel
qcirc.h(a)
qcirc.h(b)
#### start of by applying a rotation around y axis
simple_circuit, qr, cr = build_circ(2, 2)
simple_circuit.ry(np.pi/6, qr[0])
simple_circuit.ry(np.pi/6, qr[1])
simple_circuit_statevec = statevec_exec(simple_circuit)
simple_circuit.measure([0,1], [0,1])
simple_circuit.draw()
q.visualization.plot_bloch_multivector(simple_circuit_statevec)
qasm_sim = q.Aer.get_backend('qasm_simulator')
orig_counts = q.execute(simple_circuit, backend=qasm_sim, shots=1024).result().get_counts()
q.visualization.plot_histogram([orig_counts], legend=['output: org qs'])
hadamard_front, qr1, cr1 = build_circ(2, 2)
hadamard_front.draw()
h_gates(hadamard_front, qr1[0], qr1[1])
hadamard_front.ry(math.pi/6, qr1[0])
hadamard_front.ry(math.pi/6, qr1[1])
hadamard_front_statevec = statevec_exec(hadamard_front)
hadamard_front.measure([0,1], [0,1])
hadamard_front.draw()
q.visualization.plot_bloch_multivector(hadamard_front_statevec)
#qasm_sim = q.Aer.get_backend('qasm_simulator')
hfront_counts = q.execute(hadamard_front, backend=qasm_sim, shots=1024).result().get_counts()
q.visualization.plot_histogram([hfront_counts], legend=['output: hfront qs'])
hadamard_front_back, qr2, cr2 = build_circ(2, 2)
h_gates(hadamard_front_back, qr2[0], qr2[1])
hadamard_front_back.ry(math.pi/6, [qr2[0]])
hadamard_front_back.ry(math.pi/6, [qr2[1]])
h_gates(hadamard_front_back, qr2[0], qr2[1])
#hadamard_front.barrier()
hadamard_front_back_statevec = statevec_exec(hadamard_front_back)
hadamard_front_back.measure([0,1], [0,1])
hadamard_front_back.draw()
q.visualization.plot_bloch_multivector(hadamard_front_back_statevec)
hfront_back_counts = q.execute(hadamard_front_back, backend=qasm_sim, shots=1024).result().get_counts()
q.visualization.plot_histogram([hfront_back_counts], legend=['output: hfront-back qs'])
num_input = 3
balanced_circuit = q.QuantumCircuit(num_input+1) # num_input + one more where qubit is initialized to |1>
### we can create the balanced oracle by applying CNOT gate to every input and/or by wrapping the second CNOT gate with X gates
x_str = "010" # this is for iterating over the string and add X gate when there's 1.
for n in range(len(x_str)):
if x_str[n]=="1":
balanced_circuit.x(n)
balanced_circuit.draw()
### add control not for all the inputs
balanced_circuit.barrier()
for i in range(num_input):
balanced_circuit.cx(i, num_input)
balanced_circuit.barrier()
balanced_circuit.draw()
### add another x gate
for n in range(len(x_str)):
if x_str[n]=="1":
balanced_circuit.x(n)
balanced_circuit.draw()
init_circuit, qr, cr = build_circ(num_input+1, num_input)
### all the input qubits are initialized to 0, while the target qubit is initialized to 1.
for n in range(num_input):
init_circuit.h(n)
init_circuit.x(qr[num_input]) # state is now initialized to |1>
init_circuit.h(qr[num_input])
init_circuit.draw()
complete_circuit = init_circuit.compose(balanced_circuit)
complete_circuit.draw()
## now we add h gates again to the input qubits
for n in range(num_input):
complete_circuit.h(n)
complete_circuit.barrier()
# Now we measure the input qubits
for n in range(num_input):
complete_circuit.measure(n, n)
complete_circuit.draw()
from google.colab import drive
drive.mount('/content/drive')
path = '/content/drive/My Drive/'
style = {'backgroundcolor': 'lavender', 'dpi':200, 'subfontsize':10}
complete_circuit.draw(output='mpl',scale=0.8, style=style, filename=path+'DJ_Circ_balanced.png')
qasm_sim = q.Aer.get_backend('qasm_simulator')
balanced_counts = q.execute(complete_circuit, backend=qasm_sim, shots=1024).result().get_counts()
q.visualization.plot_histogram([balanced_counts], legend=['output: balanced'])
### let's use IBM Quantum Computer to simulate this,
from qiskit import IBMQ
IBMQ.save_account('find your token')
IBMQ.load_account()
provider = IBMQ.get_provider('ibm-q')
qcomp = provider.get_backend('ibmq_athens') # check which computer has 0 jobs on queue
job = q.execute(complete_circuit, backend=qcomp)
q_result = job.result()
q.visualization.plot_histogram(q_result.get_counts(complete_circuit))
|
https://github.com/suvoooo/Qubits-Qiskit
|
suvoooo
|
# !pip3 install qiskit
import qiskit as q
import matplotlib.pyplot as plt
from qiskit import IBMQ
qr = q.QuantumRegister(2)
cr = q.ClassicalRegister(2)
circuit = q.QuantumCircuit(qr, cr)
circuit.draw()
circuit.h(qr[0]) # apply hadamard gate on first qubit.
circuit.h(qr[1]) # apply hadamard gate on second qubit.
# apply cnot gate.
display(circuit.draw())
#### see the state-vector
statevec = q.Aer.get_backend('statevector_simulator')
final_state = q.execute(circuit, statevec).result().get_statevector()
q.visualization.plot_bloch_multivector(final_state)
circuit.measure(qr, cr)
circuit.draw(scale=2)
#### simulate result on a local computer
simulator = q.Aer.get_backend(name='qasm_simulator')
results = q.execute(circuit, backend=simulator, ).result()
q.visualization.plot_histogram(results.get_counts(circuit))
### try a different circuit where we apply X gate before applying H gate.
qr1 = q.QuantumRegister(3)
cr1 = q.ClassicalRegister(3)
circuit1 = q.QuantumCircuit(qr1, cr1)
# circuit1.draw()
circuit1.h(qr1[0]) # apply the H gate on q0
circuit1.h(qr1[1]) # apply H gate on q_1
circuit1.h(qr1[2]) # add H gate on q_2
### draw the circuit
circuit1.draw()
#### plt the state vector
statevec1 = q.Aer.get_backend('statevector_simulator')
final_state1 = q.execute(circuit1, statevec1).result().get_statevector()
q.visualization.plot_bloch_multivector(final_state1) # the state doesn't not change after application of the CNOT gate
#### unchanged state as expected
circuit1.measure(qr1, cr1)
circuit1.draw(scale=2)
circuit1.measure(qr1, cr1)
### simulate result on local computer
simulator1 = q.Aer.get_backend('qasm_simulator')
results1 = q.execute(circuit1, backend=simulator1, ).result()
q.visualization.plot_histogram(results1.get_counts(circuit1))
# Now we will use IBM Quantum Computer to see the result
IBMQ.save_account('your token')
IBMQ.load_account()
provider = IBMQ.get_provider('ibm-q')
qcomp = provider.get_backend('ibmq_valencia') # check which computer has 0 jobs on queue
job = q.execute(circuit1, backend=qcomp)
q_result = job.result()
q.visualization.plot_histogram(q_result.get_counts(circuit1))
|
https://github.com/suvoooo/Qubits-Qiskit
|
suvoooo
|
# # install and import qiskit
# !pip3 install qiskit
import qiskit as q
import matplotlib.pyplot as plt
from qiskit import IBMQ
IBMQ.save_account('putyourtoken')
IBMQ.load_account()
qr = q.QuantumRegister(2)
cr = q.ClassicalRegister(2)
circuit = q.QuantumCircuit(qr, cr)
circuit.draw()
# apply the Hadamard gate on 0th qubit
circuit.h(qr[0])
circuit.draw(output='mpl')
# now we apply the cnot gate
# as explained before the control qubit should be in superposition state
circuit.cx(qr[0], qr[1])
circuit.draw(output='mpl')
circuit.measure(qr, cr) # measure quantum bit into classical bit
circuit.draw(output='mpl')
# let's simulate the results on a local computer
simulator = q.Aer.get_backend('qasm_simulator') # qasm : quantum assembly lang.
results = q.execute(circuit, backend=simulator).result() # execute the circuit based on the simulator as backend
q.visualization.plot_histogram(results.get_counts(circuit))
provider = IBMQ.get_provider('ibm-q')
qcomp = provider.get_backend('ibmq_london') # check which computer has 0 jobs on queue
job = q.execute(circuit, backend=qcomp)
q_result = job.result()
q.visualization.plot_histogram(q_result.get_counts(circuit))
|
https://github.com/suvoooo/Qubits-Qiskit
|
suvoooo
|
!pip3 install qiskit
import qiskit as q
print ("check version: ", q.__qiskit_version__)
from qiskit import IBMQ
import matplotlib.pyplot as plt
IBMQ.save_account('find your own number')
IBMQ.load_account()
circuit = q.QuantumCircuit(2, 2) # creates a new circuit with 2 classical and 2 quantum bits
# apply a simple not gate
circuit.x(1) # as both the bits are at 0, 0 this will flip the second bit to 1. # the number generally always represents the index, i.e. 1-- second index
circuit.draw()
# after flipping the second bit to 1 now we apply the controlled not gate
circuit.cx(1, 0) # conrolled qubit is the 2nd one and the target qubit is the 1st one
# QuantumCircuit.cx(control_qubit, target_qubit, *, label=None, ctrl_state=None, ctl=None, tgt=None)
# here's the description [https://qiskit.org/documentation/stubs/qiskit.circuit.QuantumCircuit.cx.html].
# since the controlled bit is 1 so it will flip the target qubit which was initially sitting at 0, to 1.
# rather than values think of this 0, 1 inside circuit.cx() as indices of the qubits.
# so at this stage we will have 1, 1 as output.
circuit.draw()
circuit.measure([0, 1], [0, 1]) # measure: quantum bit into classical bit,
# again think of the 0, 1 as indices, first list is the quantum register consisting of 2 qubits
# second list is the classical register consisting of 2 classical bits.
# think of this as value for qubit 0 is what will be returned for classical bit 0 and so on.
circuit.draw(output='mpl')
# let's simulate this before sending it to a quantum computer
# idea is we simulate the quantum computer computation in local computer using Aer framework
simulator = q.Aer.get_backend('qasm_simulator') # qasm : quantum assembly lang.
results = q.execute(circuit, backend=simulator).result() # execute the circuit based on the simulator as backend
q.visualization.plot_histogram(results.get_counts(circuit))
# Now we will use IBM Quantum Computer to see the result
provider = IBMQ.get_provider('ibm-q')
qcomp = provider.get_backend('ibmq_burlington') # check which computer has 0 jobs on queue
job = q.execute(circuit, backend=qcomp)
q_result = job.result()
q.visualization.plot_histogram(q_result.get_counts(circuit))
|
https://github.com/suvoooo/Qubits-Qiskit
|
suvoooo
|
# !pip3 install qiskit
import numpy as np
import qiskit as q
### build a circuit for 3 qubits
qr = q.QuantumRegister(3)
circ = q.QuantumCircuit(qr)
### add the first H gate (in qiskit east significant bit has the lowest index)
circ.h(qr[2])
### add the controlled phase gate
circ.cp(np.pi/2, qr[1], qr[2]) # based on qubit 1 apply 2pi/2**(k-1) rotation to qubit 2
### add the next cp gate
circ.cp(np.pi/4, qr[0], qr[2]) # based on qubit 0 apply 2pi/2**(k-1) rotation to qubit 2
### repeat the process for qubit 1
circ.h(qr[1])
circ.cp(np.pi/2, qr[0], qr[1])
### add the final h gate
circ.h(qr[0])
### finally swap the bits 0 th, and 2nd qubit
circ.swap(qr[0], qr[2])
circ.draw()
from google.colab import drive
drive.mount('/content/drive')
# !pip install pylatexenc
path='/content/drive/My Drive/Colab Notebooks/'
style = {'backgroundcolor': 'lavender', 'dpi':200, 'subfontsize':10}
circ.draw('mpl', scale=0.8, style=style, filename=path+'qfouriert_3bits.png')
qr = q.QuantumRegister(3)
circ1 = q.QuantumCircuit(qr)
### encode the state 110 at first
circ1.x(qr[2])
circ1.x(qr[1])
### repeat what's done before
### add the first H gate (in qiskit east significant bit has the lowest index)
circ1.h(qr[2])
### add the controlled phase gate
circ1.cp(np.pi/2, qr[1], qr[2]) # based on qubit 1 apply 2pi/2**(k-1) rotation to qubit 2
### add the next cp gate
circ1.cp(np.pi/4, qr[0], qr[2]) # based on qubit 0 apply 2pi/2**(k-1) rotation to qubit 2
### repeat the process for qubit 1
circ1.h(qr[1])
circ1.cp(np.pi/2, qr[0], qr[1])
### add the final h gate
circ1.h(qr[0])
### finally swap the bits 0 th, and 2nd qubit
circ1.swap(qr[0], qr[2])
circ1.draw()
circ1.save_statevector()
qasm_sim = q.Aer.get_backend('qasm_simulator')
statevector = qasm_sim.run(circ1).result().get_statevector()
q.visualization.plot_bloch_multivector(statevector)
qr = q.QuantumRegister(3)
circ2 = q.QuantumCircuit(qr)
### encode the state 101 at first
circ2.x(qr[2])
circ2.x(qr[0])
### repeat what's done before
### add the first H gate (in qiskit east significant bit has the lowest index)
circ2.h(qr[2])
### add the controlled phase gate
circ2.cp(np.pi/2, qr[1], qr[2]) # based on qubit 1 apply 2pi/2**(k-1) rotation to qubit 2
### add the next cp gate
circ2.cp(np.pi/4, qr[0], qr[2]) # based on qubit 0 apply 2pi/2**(k-1) rotation to qubit 2
### repeat the process for qubit 1
circ2.h(qr[1])
circ2.cp(np.pi/2, qr[0], qr[1])
### add the final h gate
circ2.h(qr[0])
### finally swap the bits 0 th, and 2nd qubit
circ2.swap(qr[0], qr[2])
circ2.draw()
circ2.save_statevector()
qasm_sim = q.Aer.get_backend('qasm_simulator')
statevector2 = qasm_sim.run(circ2).result().get_statevector()
q.visualization.plot_bloch_multivector(statevector2)
|
https://github.com/suvoooo/Qubits-Qiskit
|
suvoooo
|
!pip3 install qiskit
# !pip install pylatexenc
import qiskit as q
import matplotlib.pyplot as plt
qr = q.QuantumRegister(2)
cr = q.ClassicalRegister(2)
circuit = q.QuantumCircuit(qr, cr)
circuit.draw()
circuit.h(qr[0]) # apply hadamard gate on first qubit.
circuit.h(qr[1]) # apply hadamard gate on second qubit.
# apply cnot gate.
circuit.cx(qr[0], qr[1]) # in qiskit cnot-- first bit is set as control bit and second as target bit.
display(circuit.draw())
#### see the state-vector
statevec = q.Aer.get_backend('statevector_simulator')
final_state = q.execute(circuit, statevec).result().get_statevector()
q.visualization.plot_bloch_multivector(final_state) # the state doesn't not change after application of the CNOT gate
circuit.measure(qr, cr)
circuit.draw(scale=2)
#### simulate result on a local computer
simulator = q.Aer.get_backend(name='qasm_simulator')
results = q.execute(circuit, backend=simulator, ).result()
q.visualization.plot_histogram(results.get_counts(circuit))
### try a different circuit where we apply X gate before applying H gate.
qr1 = q.QuantumRegister(2)
cr1 = q.ClassicalRegister(2)
circuit1 = q.QuantumCircuit(qr1, cr1)
# circuit1.draw()
circuit1.h(qr1[0]) # apply the H gate on q0
circuit1.x(qr1[1]) # apply Not gate on q_1
circuit1.h(qr1[1]) # add H gate on q_1
circuit1.cx(qr1[0], qr1[1]) # apply cx gate with control bit at 0th index
### draw the circuit
circuit1.draw()
#### plt the state vector
statevec1 = q.Aer.get_backend('statevector_simulator')
final_state1 = q.execute(circuit1, statevec1).result().get_statevector()
q.visualization.plot_bloch_multivector(final_state1) # the state doesn't not change after application of the CNOT gate
#### unchanged state as expected
circuit1.measure(qr1, cr1)
### simulate result on local computer
simulator1 = q.Aer.get_backend('qasm_simulator')
results1 = q.execute(circuit1, backend=simulator1, ).result()
q.visualization.plot_histogram(results1.get_counts(circuit1))
### try a different circuit where we apply X gate before applying H gate.
### but we do this for first qubit.
qr2 = q.QuantumRegister(2)
cr2 = q.ClassicalRegister(2)
circuit2 = q.QuantumCircuit(qr2, cr2)
# circuit2.draw()
circuit2.x(qr2[0])
circuit2.h(qr2[0])
circuit2.h(qr2[1])
circuit2.cx(qr2[0], qr2[1])
circuit2.draw()
#### plt the state vector
statevec2 = q.Aer.get_backend('statevector_simulator')
final_state2 = q.execute(circuit2, statevec2).result().get_statevector()
q.visualization.plot_bloch_multivector(final_state2)
circuit2.measure(qr2, cr2)
### simulate result on local computer
simulator2 = q.Aer.get_backend('qasm_simulator')
results2 = q.execute(circuit2, backend=simulator1, ).result()
q.visualization.plot_histogram(results2.get_counts(circuit2))
qr3 = q.QuantumRegister(2)
cr3 = q.ClassicalRegister(2)
circuit3 = q.QuantumCircuit(qr3, cr3)
# circuit3.draw()
circuit3.h(qr3[0])
circuit3.h(qr3[1])
## apply hadamard gates to both qubits.
circuit3.cx(qr3[0], qr3[1])
## till this is first circuit. Now we add one more H gate.
circuit3.h(qr3[0])
circuit3.draw(scale=2)
### plot the state vector
statevec3 = q.Aer.get_backend('statevector_simulator')
final_state3 = q.execute(circuit3, statevec3).result().get_statevector()
q.visualization.plot_bloch_multivector(final_state3)
# let's simulate this on local computer
circuit3.measure(qr3, cr3)
### simulate result on local computer
simulator3 = q.Aer.get_backend('qasm_simulator')
results3 = q.execute(circuit3, backend=simulator3, ).result()
q.visualization.plot_histogram(results3.get_counts(circuit3))
qr4 = q.QuantumRegister(2)
cr4 = q.ClassicalRegister(2)
circuit4 = q.QuantumCircuit(qr4, cr4)
circuit4.h(qr4[0])
circuit4.h(qr4[1])
## apply hadamard gates to both qubits.
circuit4.cx(qr4[0], qr4[1])
## till this is first circuit. Now we add one more H gate.
circuit4.h(qr4[1]) # but now apply the H gate to the second qubit.
circuit4.draw(scale=2)
### plot the state vector
statevec4 = q.Aer.get_backend('statevector_simulator')
final_state4 = q.execute(circuit4, statevec4).result().get_statevector()
q.visualization.plot_bloch_multivector(final_state4)
# let's simulate this on local computer
circuit4.measure(qr4, cr4)
### simulate result on local computer
simulator4 = q.Aer.get_backend('qasm_simulator')
results4 = q.execute(circuit4, backend=simulator4, ).result()
q.visualization.plot_histogram(results4.get_counts(circuit4)) ### !!! great !!! as expected
|
https://github.com/suvoooo/Qubits-Qiskit
|
suvoooo
|
# !pip3 install qiskit
# !pip3 install pylatexenc
import qiskit as q
import numpy as np
import matplotlib.pyplot as plt
qr = q.QuantumRegister(3, name="q")
crz, crx = q.ClassicalRegister(1, name="crz"), q.ClassicalRegister(1, name="crx")
teleport_circuit = q.QuantumCircuit(qr, crz, crx)
teleport_circuit.draw()
def epr_pair(qc, a, b):
'''
qc is the quantum circuit
a, b are two different qubits
'''
qc.h(a)
qc.cx(a, b)
epr_pair(teleport_circuit, qr[1], qr[2]) # we leave 0th qubit (this will be used later as Alice's qubit the she wants to teleport)
teleport_circuit.draw()
# assume alice owns q_1 and bob owns q_2
# now we are ready to take the transformations that Alice applies
# the qubit she wants to teleport is control bit and the epr member bit is the target bit
def alice_steps(qc, psi, a):
qc.cx(psi, a)
qc.h(psi)
teleport_circuit.barrier() # barrier ensures the epr pair creation part isn't hampered/optimized by whatever happens next
alice_steps(teleport_circuit, qr[0], qr[1])
teleport_circuit.draw()
# after this alice applies the measurement
def alice_measure(qc, a, b):
qc.barrier()
qc.measure(a, 0) # measures the first qubit and put it into first classical register
# measure goes as
# Measure quantum bit into classical bit (tuples).
# Args:
# qubit (QuantumRegister|list|tuple): quantum register
# cbit (ClassicalRegister|list|tuple): classical register
qc.measure(b, 1)
alice_measure(teleport_circuit, 0, 1) # 0th qubit is the one she wants to send and 1st qubit is one of the EPR pairs
teleport_circuit.draw()
def bob_transform(qc, bob_bit, crz, crx):
qc.x(bob_bit).c_if(crx, 1) #c_if Add classical condition on register classical and value val c_if(self, classical, val)
qc.z(bob_bit).c_if(crz, 1) # apply x and z gate only when if any of then are in the state '1'
teleport_circuit.barrier()
bob_transform(teleport_circuit, qr[2], crz, crx)
teleport_circuit.draw()
from qiskit.quantum_info import random_statevector
psi = random_statevector(2, seed=10)
print ('check the qubit Alice wants to teleport: ', psi, type(psi), type(psi.data))
q.visualization.plot_bloch_multivector(psi)
from qiskit.extensions import Initialize
init_qubit = Initialize(psi.data)
init_qubit.label = "init"
inverse_init_qubit = init_qubit.gates_to_uncompute()
#initialization start from zero to psi, we reverse this initialization so it takes us back to zero
qc_new_teleport = q.QuantumCircuit(qr, crz, crx)
qc_new_teleport.append(init_qubit, [qr[0]])
qc_new_teleport.barrier()
epr_pair(qc_new_teleport, qr[1], qr[2])
qc_new_teleport.barrier()
alice_steps(qc_new_teleport, qr[0], qr[1])
alice_measure(qc_new_teleport, qr[0], qr[1])
bob_transform(qc_new_teleport, qr[2], crz, crx)
qc_new_teleport.draw()
from google.colab import drive
drive.mount('/content/drive')
path = '/content/drive/My Drive/'
style = {'backgroundcolor': 'lavender', 'dpi':200, 'subfontsize':10}
qc_new_teleport.draw(output='mpl',scale=0.8, style=style, filename=path+'teleport_1.png')
statevec_sim = q.Aer.get_backend('statevector_simulator')
qobj = q.assemble(qc_new_teleport)
out_vector = statevec_sim.run(qobj).result().get_statevector()
q.visualization.plot_bloch_multivector(out_vector,)
qc_teleport_check = q.QuantumCircuit(qr, crz, crx)
qc_teleport_check.append(init_qubit, [qr[0]])
qc_teleport_check.barrier()
epr_pair(qc_teleport_check, qr[1], qr[2])
qc_teleport_check.barrier()
alice_steps(qc_teleport_check, qr[0], qr[1])
alice_measure(qc_teleport_check, qr[0], qr[1])
bob_transform(qc_teleport_check, qr[2], crz, crx)
qc_teleport_check.append(inverse_init_qubit, [qr[2]])
style = {'backgroundcolor': 'lavender'}
qc_teleport_check.draw(output='mpl',scale=0.8, style=style)
cr_result = q.ClassicalRegister(1) # we need another register to measure if we get 0 or not
qc_teleport_check.add_register(cr_result)
qc_teleport_check.measure(qr[2], 2) # measure the last qubit and put it into the last classical register
qc_teleport_check.draw(output='mpl', scale=0.8, style={'backgroundcolor': 'lavender', 'subfontsize':12})
simulator = q.Aer.get_backend(name='qasm_simulator')
results = q.execute(qc_teleport_check, backend=simulator,).result()
q.visualization.plot_histogram(results.get_counts(qc_teleport_check))
|
https://github.com/suvoooo/Qubits-Qiskit
|
suvoooo
|
# !pip install qiskit
import math
import matplotlib.pyplot as plt
import numpy as np
import qiskit as q
from qiskit.visualization import plot_bloch_vector, plot_state_city
from google.colab import drive
drive.mount('/content/drive')
path = '/content/drive/My Drive/Colab Notebooks/Quantum_Compute/'
coords = [1, math.pi/2, math.pi]
plot_bloch_vector(coords, coord_type='spherical', figsize=(6, 6), )
## check density matrix from state vector
initial_state1 = [0.j + 1./np.sqrt(2), 0, 0, 1/np.sqrt(2) + 0.j]
initial_state2 = [0.j + 1./2., 0, 0, 0.j + np.sqrt(3)/2.]
m1 = q.quantum_info.Statevector(initial_state1)
print ('check the first statevector: ', m1)
m2 = q.quantum_info.Statevector(initial_state2)
print ('check the first statevector: ', m2)
rho_1 = q.quantum_info.DensityMatrix(m1, dims=4)
print ('check the density matrix corresponding to 1st statevector: ', '\n', rho_1)
rho_2 = q.quantum_info.DensityMatrix(m2, dims=4)
print ('check the density matrix corresponding to 2nd statevector: ', '\n', rho_2)
rho_1.draw('latex', prefix='\\rho_{1} = ')
plot_state_city(rho_1.data, title=r'Density Matrix for StateVector: $\frac{1}{\sqrt{2}}\left(|0\rangle + |1\rangle\right)$',
color=['magenta', 'purple'], alpha=0.7, figsize=(11, 7), )
rho_2.draw('latex', prefix='\\rho_{2} = ')
plot_state_city(rho_2.data, title=r'Density Matrix for StateVector: $\left(\frac{1}{2}|0\rangle + \frac{\sqrt{3}}{2}|1\rangle\right)$',
color=['magenta', 'purple'], alpha=0.7, figsize=(11, 7), )
rho_H_matrix = np.array([[9/20, 0, 0, np.sqrt(3)/20 + 8/20], [0, 0, 0, 0], [0, 0, 0, 0], [np.sqrt(3)/20 + 8/20, 0, 0, 11/20]])
rho_H = q.quantum_info.DensityMatrix(rho_H_matrix)
rho_H.draw('latex', prefix='\\rho_{mix} = ')
final_density_matrix = 0.8*rho_1 + 0.2*rho_2
check_rho_H = q.quantum_info.DensityMatrix(final_density_matrix)
check_rho_H.draw('latex', prefix='\\rho_{mix} = ')
### check the first property
rho_H_matrix_square = np.matmul(rho_H_matrix, rho_H_matrix)
rho_H_square = q.quantum_info.DensityMatrix(rho_H_matrix_square)
rho_H_matrix_square_Trace = np.trace(rho_H_matrix_square)
print ('Trace of the square of the density matrix: ', rho_H_matrix_square_Trace)
rho_H_square.draw('latex', prefix='\\rho_{mix}^2 = ')
### check the second property
rho_H_matrix_eigenvals = np.linalg.eigvals(rho_H_matrix)
print ('check the eigenvalues of the density matrix', rho_H_matrix_eigenvals[0], rho_H_matrix_eigenvals[1])
print ('check sum of the eigenvalues of the density matrix: ', np.sum(rho_H_matrix_eigenvals))
plot_state_city(check_rho_H.data,
title=r'Density Matrix for Mixed State: $0.8\times\frac{1}{\sqrt{2}}\left(|0\rangle + |1\rangle\right) + 0.2\times \left(\frac{1}{2}|0\rangle + \frac{\sqrt{3}}{2}|1\rangle\right)$',
color=['crimson', 'purple'], alpha=0.4, figsize=(11, 7), )
reduced_density_matrix = np.array([[9/20, np.sqrt(3)/20 + 8/20], [np.sqrt(3)/20 + 8/20, 11/20]])
red_rho_H = q.quantum_info.DensityMatrix(reduced_density_matrix)
red_rho_H.draw('latex', prefix='\\rho_{mix} = ')
from qiskit.visualization import plot_bloch_multivector
plot_bloch_multivector(state=red_rho_H.data, figsize=(6, 6), )
|
https://github.com/suvoooo/Qubits-Qiskit
|
suvoooo
|
# !pip3 install qiskit
import qiskit as q
import matplotlib.pyplot as plt
import numpy as np
def build_circ(num_qbits, num_cbits):
qr = q.QuantumRegister(num_qbits)
cr = q.ClassicalRegister(num_cbits)
final_circ = q.QuantumCircuit(qr, cr)
return final_circ, qr, cr
def h_gates(qcirc, a, b): # 2 inputs and h gates in parallel
qcirc.h(a)
qcirc.h(b)
hadamard_front, qr, cr = build_circ(2, 2)
h_gates(hadamard_front, qr[0], qr[1])
hadamard_front.draw()
### define the oracle for state 00
hadamard_front.x(qr[0])
hadamard_front.x(qr[1])
hadamard_front.cz(qr[0], qr[1])
hadamard_front.x(qr[0])
hadamard_front.x(qr[1])
hadamard_front.draw()
### We apply the steps below to create the reflection about the original superposition state
### 1. Apply H to each qubit in the register.
### 2. A conditional phase shift of −1 to every computational basis state except |0⟩.
### This can be represented by the unitary operation −O_0, as O_0 represents the conditional phase shift to |0⟩ only.
### 3. Apply H to each qubit in the register.
### reflection about state |0> can be thought of as circuit below
### apply z gate to both qubits, then add a cz gate
def reflection_psi(qcirc, a, b):
h_gates(qcirc, a, b)
qcirc.z(a)
qcirc.z(b)
qcirc.cz(a, b)
h_gates(qcirc, a, b)
reflection_psi_circ, qr_rf, cr_rf = build_circ(2, 2)
reflection_psi(reflection_psi_circ, qr_rf[0], qr_rf[1])
reflection_psi_circ.draw()
# !pip3 install pylatexenc
complete_circuit = hadamard_front.compose(reflection_psi_circ)
for n in range(2):
complete_circuit.measure(n, n)
complete_circuit.draw('mpl', filename=path+'Grover_Algo_00.png', scale=1.1)
statevec_sim = q.Aer.get_backend("statevector_simulator")
def statevec_exec(circ):
statevec = q.execute(circ, backend=statevec_sim).result().get_statevector()
return statevec
complete_circuit_statevec = statevec_exec(complete_circuit)
q.visualization.plot_bloch_multivector(complete_circuit_statevec)
qasm_sim = q.Aer.get_backend('qasm_simulator')
counts = q.execute(complete_circuit, backend=qasm_sim, shots=1024).result().get_counts()
q.visualization.plot_histogram([counts], legend=['output'])
from qiskit import IBMQ
IBMQ.save_account('find your token')
IBMQ.load_account()
provider = IBMQ.get_provider('ibm-q')
qcomp = provider.get_backend('ibmq_manila') # check which computer has 0 jobs on queue
job = q.execute(complete_circuit, backend=qcomp)
q_result = job.result()
q.visualization.plot_histogram(q_result.get_counts(complete_circuit))
check_circ = q.QuantumCircuit(2)
# check_circ.iswap(0, 1)
check_circ.rz(np.pi/2, 0)
check_circ.rz(np.pi/2, 1)
check_circ.cz(0, 1)
check_circ.rz(np.pi/2, 0)
check_circ.rz(np.pi/2, 1)
qasm_sim = q.Aer.get_backend('unitary_simulator')
# we need to make a copy of the circuit with the 'save_statevector'
# instruction to run on the Aer simulator
job = q.execute(check_circ, qasm_sim)
result = job.result()
print(result.get_unitary(check_circ, decimals=2))
from google.colab import drive
drive.mount('/content/drive')
path = '/content/drive/My Drive/'
### let's use the oracke above to check whether we get 00 state as solution or not
hadamard_front_check, qr_check, cr_check = build_circ(2, 2)
h_gates(hadamard_front_check, qr_check[0], qr_check[1])
#### oracle with rz and cz gate
hadamard_front_check.rz(np.pi/2, qr_check[0])
hadamard_front_check.rz(np.pi/2, qr_check[1])
hadamard_front_check.cz(qr_check[0], qr_check[1])
hadamard_front_check.rz(np.pi/2, qr_check[0])
hadamard_front_check.rz(np.pi/2, qr_check[1])
####
reflection_psi_circ_check, qr_rf_check, cr_rf_check = build_circ(2, 2)
reflection_psi(reflection_psi_circ_check, qr_rf_check[0], qr_rf_check[1])
complete_circuit_check = hadamard_front_check.compose(reflection_psi_circ_check)
for n in range(2):
complete_circuit_check.measure(n, n)
style = {'backgroundcolor': 'lavender', 'dpi':200, 'subfontsize':10}
complete_circuit_check.draw('mpl', scale=0.8, style=style, filename=path+'Grover_Algo_RZ_00.png')
qasm_sim = q.Aer.get_backend('qasm_simulator')
counts = q.execute(complete_circuit_check, backend=qasm_sim, shots=1024).result().get_counts()
q.visualization.plot_histogram([counts], legend=['output'])
provider = IBMQ.get_provider('ibm-q')
qcomp = provider.get_backend('ibmq_manila') # check which computer has 0 jobs on queue
job = q.execute(complete_circuit_check, backend=qcomp)
q_result = job.result()
q.visualization.plot_histogram(q_result.get_counts(complete_circuit_check))
|
https://github.com/suvoooo/Qubits-Qiskit
|
suvoooo
|
### calculate the probability for theta = 1 degree
import math
import cmath
def probs_unitary(ang):
ang = math.radians(ang) # 1 degree
y = complex(0, ang) #i1
y0 = 0.5*(1 + cmath.exp(y))
#print ('probability for getting 0: ', abs(y0)**2)
y1 = 0.5*(1 - cmath.exp(y))
#print ('probability for getting 1: ', abs(y1)**2)
return abs(y0)**2, abs(y1)**2
prob0, prob1 = probs_unitary(1)
print (r'probability for getting 0 and 1 for $\theta=1$: ', prob0, prob1)
prob0_10, prob1_10 = probs_unitary(10)
print (r'probability for getting 0 and 1 for $\theta=10$: ', prob0_10, prob1_10)
from google.colab import drive
drive.mount('/content/drive/')
!pip install qiskit
import qiskit as q
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
# from mpl_toolkits.mplot3d import proj3d
print (q.__version__)
print(matplotlib.__version__)
style = {'backgroundcolor': 'lavender', 'dpi':200, 'subfontsize':10}
PEcircuit = q.QuantumCircuit(3, 2)
PEcircuit.x(2) # this is the eigen state of S gate; in figures above this $\psi$
PEcircuit.draw()
# start by applying the H gates
for qubit in range(2):
PEcircuit.h(qubit)
PEcircuit.draw()
repetitions = 1
for counting_qubit in range(2):
for i in range(repetitions):
PEcircuit.cp(np.pi/2, counting_qubit, 2); # This is CU # QuantumCircuit.cp(theta, control_qubit, target_qubit, label=None, ctrl_state=None)
# importantly to apply controlled phase rotation we need to know the rotation angle beforehand
repetitions *= 2 # 2^{n-1} factor
PEcircuit.draw()
!pip install pylatexenc
style = {'backgroundcolor': 'lavender', 'dpi':200, 'subfontsize':10}
PEcircuit.draw(output='mpl', scale=0.8, style=style, filename='/content/drive/My Drive/Colab Notebooks/Quantum_Compute/FourierTransform/2bits_QPE_QiskitEx.png')
def qft_dagger(qc, n):
"""n-qubit inverse qft
first n qubits will be transformed
check the qft circuit implementation first
"""
for qubit in range(n//2):
qc.swap(qubit, n-qubit-1)
for j in range(n):
for m in range(j):
qc.cp(-math.pi/float(2**(j-m)), m, j)
qc.h(j)
PEcircuit.barrier()
# Apply inverse QFT
qft_dagger(PEcircuit, 2)
# Measure
PEcircuit.barrier()
for n in range(2):
PEcircuit.measure(n,n)
# PEcircuit.draw(output='mpl', style=style, filename='/content/drive/My Drive/Colab Notebooks/Quantum_Compute/FourierTransform/2bits_QPE_QiskitEx_Full.png')
aer_sim = q.Aer.get_backend('aer_simulator')
shots = 2048
t_PEcircuit = q.transpile(PEcircuit, aer_sim)
qobj = q.assemble(t_PEcircuit, shots=shots)
results = aer_sim.run(qobj).result()
answer = results.get_counts()
q.visualization.plot_histogram(answer)
PEcircuit_1 = q.QuantumCircuit(4, 3)
PEcircuit_1.x(3) # this is the eigen state of S gate; in figures above this $\psi$
for qubit in range(3):
PEcircuit_1.h(qubit)
repetitions = 1
for counting_qubit in range(3):
for i in range(repetitions):
PEcircuit_1.cp(np.pi/2, counting_qubit, 3) # This is CU # QuantumCircuit.cp(theta, control_qubit, target_qubit, label=None, ctrl_state=None)
# importantly to apply controlled phase rotation we need to know the rotation angle beforehand
repetitions *= 2 # 2^{n-1} factor
PEcircuit_1.draw()
PEcircuit_1.barrier()
# Apply inverse QFT
qft_dagger(PEcircuit_1, 3)
# Measure
PEcircuit_1.barrier()
for n in range(3):
PEcircuit_1.measure(n, n)
PEcircuit_1.draw(output='mpl', style=style, filename='/content/drive/My Drive/Colab Notebooks/Quantum_Compute/FourierTransform/3bits_QPE_QiskitEx.png')
aer_sim = q.Aer.get_backend('aer_simulator')
shots = 2048
t_PEcircuit_1 = q.transpile(PEcircuit_1, aer_sim)
qobj = q.assemble(t_PEcircuit_1, shots=shots)
results_1 = aer_sim.run(qobj).result()
answer_1 = results_1.get_counts()
q.visualization.plot_histogram(answer_1)
PEcircuit_1.num_qubits-1
def qpe_circ(nq, angle):
"""
generic qpe circuit
nq: number of qubits
angle: theta as defined in description
"""
qc = q.QuantumCircuit(nq+1, nq) # num qubits + eigen state, for measuring we need num qubits = classical bits for measure.
qc.x(nq) # apply the not gate to create state 1, i.e. eigen state at the last qubit
for qubit in range(nq):
qc.h(qubit)
repetitions = 1
for counting_qubit in range(qc.num_qubits - 1): #qc.num_qubits - 1 = nq
for i in range(repetitions):
qc.cp(np.pi *2 * angle, counting_qubit, qc.num_qubits-1); # This is CU # QuantumCircuit.cp(theta, control_qubit, target_qubit, label=None, ctrl_state=None)
# importantly to apply controlled phase rotation we need to know the rotation angle beforehand
repetitions *= 2 # 2^{n-1} factor
qc.barrier()
qft_dagger(qc, qc.num_qubits-1)
qc.barrier()
for n1 in range(qc.num_qubits -1):
qc.measure(n1, n1)
return qc
circuit_1 = qpe_circ(3, 1/5)
circuit_1.draw(output='mpl', style=style, filename='/content/drive/My Drive/Colab Notebooks/Quantum_Compute/FourierTransform/3bits_QPE_QiskitEx_NonInt_Theta.png')
aer_sim = q.Aer.get_backend('aer_simulator')
shots = 2048
t_circuit_1 = q.transpile(circuit_1, aer_sim)
qobj = q.assemble(t_circuit_1, shots=shots)
results_c1 = aer_sim.run(qobj).result()
answer_c1 = results_c1.get_counts()
q.visualization.plot_histogram(answer_c1)
circuit_2 = qpe_circ(5, 1/5)
circuit_2.draw(output='mpl', style=style, filename='/content/drive/My Drive/Colab Notebooks/Quantum_Compute/FourierTransform/5bits_QPE_QiskitEx_NonInt_Theta.png')
aer_sim = q.Aer.get_backend('aer_simulator')
shots = 2048
t_circuit_2 = q.transpile(circuit_2, aer_sim)
qobj = q.assemble(t_circuit_2, shots=shots)
results_c2 = aer_sim.run(qobj).result()
answer_c2 = results_c2.get_counts()
fig, ax = plt.subplots(figsize=(14, 6))
q.visualization.plot_histogram(answer_c2, figsize=(14, 6), color='violet', title=r'QPE Measurement Result with 5 Qubits: $\theta = 1/5$', ax=ax)
fig.savefig('/content/drive/My Drive/Colab Notebooks/Quantum_Compute/FourierTransform/5bits_QPE_QiskitEx_NonInt_Theta_Histogram.png', dpi=250)
for k, v in answer_c2.items():
print(k, v)
|
https://github.com/suvoooo/Qubits-Qiskit
|
suvoooo
|
# !pip3 install qiskit
import numpy as np
import qiskit as q
### build a circuit for 3 qubits
qr = q.QuantumRegister(3)
circ = q.QuantumCircuit(qr)
### add the first H gate (in qiskit east significant bit has the lowest index)
circ.h(qr[2])
### add the controlled phase gate
circ.cp(np.pi/2, qr[1], qr[2]) # based on qubit 1 apply 2pi/2**(k-1) rotation to qubit 2
### add the next cp gate
circ.cp(np.pi/4, qr[0], qr[2]) # based on qubit 0 apply 2pi/2**(k-1) rotation to qubit 2
### repeat the process for qubit 1
circ.h(qr[1])
circ.cp(np.pi/2, qr[0], qr[1])
### add the final h gate
circ.h(qr[0])
### finally swap the bits 0 th, and 2nd qubit
circ.swap(qr[0], qr[2])
circ.draw()
from google.colab import drive
drive.mount('/content/drive')
# !pip install pylatexenc
path='/content/drive/My Drive/Colab Notebooks/'
style = {'backgroundcolor': 'lavender', 'dpi':200, 'subfontsize':10}
circ.draw('mpl', scale=0.8, style=style, filename=path+'qfouriert_3bits.png')
qr = q.QuantumRegister(3)
circ1 = q.QuantumCircuit(qr)
### encode the state 110 at first
circ1.x(qr[2])
circ1.x(qr[1])
### repeat what's done before
### add the first H gate (in qiskit east significant bit has the lowest index)
circ1.h(qr[2])
### add the controlled phase gate
circ1.cp(np.pi/2, qr[1], qr[2]) # based on qubit 1 apply 2pi/2**(k-1) rotation to qubit 2
### add the next cp gate
circ1.cp(np.pi/4, qr[0], qr[2]) # based on qubit 0 apply 2pi/2**(k-1) rotation to qubit 2
### repeat the process for qubit 1
circ1.h(qr[1])
circ1.cp(np.pi/2, qr[0], qr[1])
### add the final h gate
circ1.h(qr[0])
### finally swap the bits 0 th, and 2nd qubit
circ1.swap(qr[0], qr[2])
circ1.draw()
circ1.save_statevector()
qasm_sim = q.Aer.get_backend('qasm_simulator')
statevector = qasm_sim.run(circ1).result().get_statevector()
q.visualization.plot_bloch_multivector(statevector)
qr = q.QuantumRegister(3)
circ2 = q.QuantumCircuit(qr)
### encode the state 101 at first
circ2.x(qr[2])
circ2.x(qr[0])
### repeat what's done before
### add the first H gate (in qiskit east significant bit has the lowest index)
circ2.h(qr[2])
### add the controlled phase gate
circ2.cp(np.pi/2, qr[1], qr[2]) # based on qubit 1 apply 2pi/2**(k-1) rotation to qubit 2
### add the next cp gate
circ2.cp(np.pi/4, qr[0], qr[2]) # based on qubit 0 apply 2pi/2**(k-1) rotation to qubit 2
### repeat the process for qubit 1
circ2.h(qr[1])
circ2.cp(np.pi/2, qr[0], qr[1])
### add the final h gate
circ2.h(qr[0])
### finally swap the bits 0 th, and 2nd qubit
circ2.swap(qr[0], qr[2])
circ2.draw()
circ2.save_statevector()
qasm_sim = q.Aer.get_backend('qasm_simulator')
statevector2 = qasm_sim.run(circ2).result().get_statevector()
q.visualization.plot_bloch_multivector(statevector2)
|
https://github.com/suvoooo/Qubits-Qiskit
|
suvoooo
|
path_to_file = '/content/drive/My Drive/Colab Notebooks/Quantum_Compute/'
from google.colab import drive
drive.mount('/content/drive/')
###### can we visualize the period
import math
import matplotlib.pyplot as plt
N = 21 #15
a_1 = 5 #13 # starting co-prime
a_2 = 11
x = list(range(N))
K_1 = [a_1**r % N for r in x]
K_2 = [a_2**r % N for r in x]
# K_3 = [a_3**r % N for r in x]
period = x[K_2[1:].index(1) + 1]
print ('period of the function: ', x[K_1[1:].index(1) + 1]) # (index of the 1 after the first 1) + 1
print ('period of the function: ', x[K_2[1:].index(1) + 1])
fig=plt.figure(figsize=(15, 5))
fig.add_subplot(121)
plt.plot(x, K_1, label='a = %d'%(a_1))
plt.xlabel('x', fontsize=14)
plt.ylabel(r'$a^x\, (\mathrm{mod}\, N)$', fontsize=14)
plt.legend(fontsize=12)
fig.add_subplot(122)
plt.plot(x, K_2, linestyle='--', color='purple', alpha=0.6, label='a = %d'%(a_2))
plt.axvline(x=period, lw=0.8, ls='--', color='magenta', alpha=0.6, label='Period=%d'%(period))
plt.axvline(x=2*period, lw=0.8, ls='--', color='magenta', alpha=0.6)
plt.xlabel('x', fontsize=14)
# plt.legend(fontsize=12)
# fig.add_subplot(133)
# plt.plot(x, K_3, linestyle=':', color='lime', alpha=0.6, label='a = 8')
# plt.xlabel('x', fontsize=12)
plt.legend(fontsize=12)
plt.savefig(path_to_file +'/Period_Mod_N.png', dpi=250)
plt.show()
# print ('period of the function: ', x[K_3[1:].index(1) + 1])
import numpy as np
for y in range(15):
exp = np.exp(-1j*2*np.pi*(3*y/16)) + np.exp(-1j*2*np.pi*(7*y/16)) + np.exp(-1j*2*np.pi*(11*y/16)) + np.exp(-1j*2*np.pi*(15*y/16))
if abs(exp) < 1e-10:
exp = 0
print(exp, y, abs(exp))
# !pip install qiskit
import qiskit as q
import numpy as np
import matplotlib.pyplot as plt
def c_amod15(a, power):
"""Controlled multiplication by a mod 15"""
if a not in [2,4,7,8,11,13]:
raise ValueError("'a' must be 2,4,7,8,11 or 13")
U = q.QuantumCircuit(4)
for iteration in range(power):
if a in [2,13]:
U.swap(0,1)
U.swap(1,2)
U.swap(2,3)
if a in [7,8]:
U.swap(2,3)
U.swap(1,2)
U.swap(0,1)
if a in [4, 11]:
U.swap(1,3)
U.swap(0,2)
if a in [7,11,13]:
for i in range(4):
U.x(i)
U = U.to_gate()
U.name = "%i^%i mod 15" % (a, power)
c_U = U.control()
return c_U
def qft_dagger(n):
"""n-qubit QFTdagger the first n qubits in circ"""
qc = q.QuantumCircuit(n)
# Don't forget the Swaps!
for qubit in range(n//2):
qc.swap(qubit, n-qubit-1)
for j in range(n):
for m in range(j):
qc.cp(-np.pi/float(2**(j-m)), m, j)
qc.h(j)
qc.name = "QFT†"
return qc
n_count = 8
a = 7
# Create QuantumCircuit with n_count counting qubits
# plus 4 qubits for U to act on
qc = q.QuantumCircuit(n_count + 4, n_count)
# # Initialize counting qubits
# # in state |+>
for i in range(n_count):
qc.h(i)
# # And auxiliary register in state |1>
qc.x(3+n_count)
# # Do controlled-U operations
for k in range(n_count):
qc.append(c_amod15(a, 2**k),
[k] + [i+n_count for i in range(4)])
# # Do inverse-QFT
qc.append(qft_dagger(n_count), range(n_count))
# # Measure circuit
qc.measure(range(n_count), range(n_count))
qc.draw() # -1 means 'do not fold'
|
https://github.com/QuSTaR/kaleidoscope
|
QuSTaR
|
# -*- coding: utf-8 -*-
# This code is part of Qiskit.
#
# (C) Copyright IBM 2017.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
# pylint: disable=wrong-import-order
"""Main Qiskit public functionality."""
import pkgutil
# First, check for required Python and API version
from . import util
# qiskit errors operator
from .exceptions import QiskitError
# The main qiskit operators
from qiskit.circuit import ClassicalRegister
from qiskit.circuit import QuantumRegister
from qiskit.circuit import QuantumCircuit
# pylint: disable=redefined-builtin
from qiskit.tools.compiler import compile # TODO remove after 0.8
from qiskit.execute import execute
# The qiskit.extensions.x imports needs to be placed here due to the
# mechanism for adding gates dynamically.
import qiskit.extensions
import qiskit.circuit.measure
import qiskit.circuit.reset
# Allow extending this namespace. Please note that currently this line needs
# to be placed *before* the wrapper imports or any non-import code AND *before*
# importing the package you want to allow extensions for (in this case `backends`).
__path__ = pkgutil.extend_path(__path__, __name__)
# Please note these are global instances, not modules.
from qiskit.providers.basicaer import BasicAer
# Try to import the Aer provider if installed.
try:
from qiskit.providers.aer import Aer
except ImportError:
pass
# Try to import the IBMQ provider if installed.
try:
from qiskit.providers.ibmq import IBMQ
except ImportError:
pass
from .version import __version__
from .version import __qiskit_version__
|
https://github.com/QuSTaR/kaleidoscope
|
QuSTaR
|
# -*- coding: utf-8 -*-
# This code is part of Kaleidoscope.
#
# (C) Copyright IBM 2020.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
# This code is part of Qiskit.
#
# (C) Copyright IBM 2017, 2018.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
# The Hamming distance and internal plot histogram data are part of
# Qiskit. The latter will be modified at some point.
"""Interactive histogram from experiment counts"""
import functools
from collections import Counter, OrderedDict
import numpy as np
import plotly.graph_objects as go
from kaleidoscope.errors import KaleidoscopeError
from kaleidoscope.colors import COLORS1, COLORS2, COLORS3, COLORS4, COLORS5, COLORS14
from kaleidoscope.colors.utils import find_text_color
from .plotly_wrapper import PlotlyWidget, PlotlyFigure
def hamming_distance(str1, str2):
"""Calculate the Hamming distance between two bit strings
Args:
str1 (str): First string.
str2 (str): Second string.
Returns:
int: Distance between strings.
Raises:
ValueError: Strings not same length
"""
if len(str1) != len(str2):
raise ValueError('Strings not same length.')
return sum(s1 != s2 for s1, s2 in zip(str1, str2))
VALID_SORTS = ['asc', 'desc', 'hamming']
DIST_MEAS = {'hamming': hamming_distance}
def probability_distribution(data, figsize=(None, None), colors=None,
scale='linear',
number_to_keep=None,
sort='asc', target_string=None,
legend=None, bar_labels=True,
state_labels_kind='bits',
state_labels=None,
title=None,
plot_background_color='#e5e0df',
background_color='#ffffff',
as_widget=False):
"""Interactive histogram plot of probability distributions.
Parameters:
data (list or dict): This is either a list of dictionaries or a single
dict containing the values to represent (ex {'001': 130})
figsize (tuple): Figure size in pixels.
colors (list or str): String or list of strings for histogram bar colors.
scale (str): Probability scale 'linear' (default) or 'log'.
number_to_keep (int): The number of terms to plot and rest
is made into a single bar called 'rest'.
sort (string): Could be 'asc', 'desc', or 'hamming'.
target_string (str): Target string if 'sort' is a distance measure.
legend(list): A list of strings to use for labels of the data.
The number of entries must match the length of data (if data is a
list or 1 if it's a dict)
bar_labels (bool): Label each bar in histogram with probability value.
state_labels_kind (str): 'bits' (default) or 'ints'.
state_labels (list): A list of custom state labels.
title (str): A string to use for the plot title.
plot_background_color (str): Set the background color behind histogram bars.
background_color (str): Set the background color for axes.
as_widget (bool): Return figure as an ipywidget.
Returns:
PlotlyFigure or PlotlyWidget:
A figure for the rendered histogram.
Raises:
ValueError: When legend is provided and the length doesn't
match the input data.
ImportError: When failed to load plotly.
KaleidoscopeError: When state labels is not correct length.
Example:
.. jupyter-execute::
from qiskit import *
import kaleidoscope.qiskit
from kaleidoscope.qiskit.services import Simulators
from kaleidoscope.interactive import probability_distribution
sim = Simulators.aer_vigo_simulator
qc = QuantumCircuit(3, 3) >> sim
qc.h(1)
qc.cx(1,0)
qc.cx(1,2)
qc.measure(range(3), range(3))
counts = qc.transpile().sample().result_when_done()
probability_distribution(counts)
"""
if sort not in VALID_SORTS:
raise ValueError("Value of sort option, %s, isn't a "
"valid choice. Must be 'asc', "
"'desc', or 'hamming'")
if sort in DIST_MEAS and target_string is None:
err_msg = 'Must define target_string when using distance measure.'
raise ValueError(err_msg)
if isinstance(data, dict):
data = [data]
if legend and len(legend) != len(data):
raise ValueError("Length of legendL (%s) doesn't match "
"number of input executions: %s" %
(len(legend), len(data)))
text_color = find_text_color(background_color)
labels = list(sorted(
functools.reduce(lambda x, y: x.union(y.keys()), data, set())))
if number_to_keep is not None:
labels.append('rest')
if sort in DIST_MEAS:
dist = []
for item in labels:
dist.append(DIST_MEAS[sort](item, target_string))
labels = [list(x) for x in zip(*sorted(zip(dist, labels),
key=lambda pair: pair[0]))][1]
# Set bar colors
if colors is None:
if len(data) == 1:
colors = COLORS1
elif len(data) == 2:
colors = COLORS2
elif len(data) == 3:
colors = COLORS3
elif len(data) == 4:
colors = COLORS4
elif len(data) == 5:
colors = COLORS5
else:
colors = COLORS14
elif isinstance(colors, str):
colors = [colors]
width = 1/(len(data)+1) # the width of the bars
labels_dict, all_pvalues, _ = _plot_histogram_data(data,
labels,
number_to_keep)
fig = go.Figure()
for item, _ in enumerate(data):
xvals = []
yvals = []
for idx, val in enumerate(all_pvalues[item]):
xvals.append(idx+item*width)
yvals.append(val)
labels = list(labels_dict.keys())
if state_labels_kind == 'ints':
labels = [int(label, 2) for label in labels]
if state_labels:
if len(state_labels) != len(labels):
raise KaleidoscopeError('Number of input state labels does not match data.')
labels = state_labels
hover_template = "<b>{x}</b><br>P = {y}"
hover_text = [hover_template.format(x=labels[kk],
y=np.round(yvals[kk], 3)) for kk in range(len(yvals))]
fig.add_trace(go.Bar(x=xvals,
y=yvals,
width=width,
hoverinfo="text",
hovertext=hover_text,
marker_color=colors[item % len(colors)],
name=legend[item] if legend else '',
text=np.round(yvals, 3) if bar_labels else None,
textposition='auto'
))
xaxes_labels = list(labels_dict.keys())
if state_labels_kind == 'ints':
xaxes_labels = [int(label, 2) for label in xaxes_labels]
if state_labels:
if len(state_labels) != len(xaxes_labels):
raise KaleidoscopeError('Number of input state labels does not match data.')
xaxes_labels = state_labels
fig.update_xaxes(title='Basis state',
titlefont_size=16,
tickvals=list(range(len(labels_dict.keys()))),
ticktext=xaxes_labels,
tickfont_size=14,
showline=True, linewidth=1,
linecolor=text_color if text_color == 'white' else None,
)
fig.update_yaxes(title='Probability',
titlefont_size=16,
tickfont_size=14,
showline=True, linewidth=1,
linecolor=text_color if text_color == 'white' else None,
)
if scale == 'log':
lower = np.min([min(item.values())/sum(item.values()) for item in data])
lower = int(np.floor(np.log10(lower)))
fig.update_yaxes(type="log", range=[lower, 0])
fig.update_layout(yaxis=dict(tickmode='array',
tickvals=[10**k for k in range(lower, 1)],
ticktext=["10<sup>{}</sup>".format(k) for k in range(lower, 1)]
))
fig.update_layout(xaxis_tickangle=-70,
showlegend=(legend is not None),
width=figsize[0],
height=figsize[1],
plot_bgcolor=plot_background_color,
paper_bgcolor=background_color,
title=dict(text=title, x=0.5),
title_font_size=18,
font=dict(color=text_color),
)
if as_widget:
return PlotlyWidget(fig)
return PlotlyFigure(fig)
def _plot_histogram_data(data, labels, number_to_keep):
"""Generate the data needed for plotting counts.
Parameters:
data (list or dict): This is either a list of dictionaries or a single
dict containing the values to represent (ex {'001': 130})
labels (list): The list of bitstring labels for the plot.
number_to_keep (int): The number of terms to plot and rest
is made into a single bar called 'rest'.
Returns:
tuple: tuple containing:
(dict): The labels actually used in the plotting.
(list): List of ndarrays for the bars in each experiment.
(list): Indices for the locations of the bars for each
experiment.
"""
labels_dict = OrderedDict()
all_pvalues = []
all_inds = []
for execution in data:
if number_to_keep is not None:
data_temp = dict(Counter(execution).most_common(number_to_keep))
data_temp["rest"] = sum(execution.values()) - sum(data_temp.values())
execution = data_temp
values = []
for key in labels:
if key not in execution:
if number_to_keep is None:
labels_dict[key] = 1
values.append(0)
else:
values.append(-1)
else:
labels_dict[key] = 1
values.append(execution[key])
values = np.array(values, dtype=float)
where_idx = np.where(values >= 0)[0]
pvalues = values[where_idx] / sum(values[where_idx])
all_pvalues.append(pvalues)
numelem = len(values[where_idx])
ind = np.arange(numelem) # the x locations for the groups
all_inds.append(ind)
return labels_dict, all_pvalues, all_inds
|
https://github.com/QuSTaR/kaleidoscope
|
QuSTaR
|
#!/usr/bin/env python
#
# Copyright 2019 the original author or authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import pygame
from qiskit import BasicAer, execute
from qiskit.tools.visualization import plot_state_qsphere
from utils import load_image
class QSphere(pygame.sprite.Sprite):
"""Displays a qsphere"""
def __init__(self, circuit):
pygame.sprite.Sprite.__init__(self)
self.image = None
self.rect = None
self.set_circuit(circuit)
# def update(self):
# # Nothing yet
# a = 1
def set_circuit(self, circuit):
backend_sv_sim = BasicAer.get_backend('statevector_simulator')
job_sim = execute(circuit, backend_sv_sim)
result_sim = job_sim.result()
quantum_state = result_sim.get_statevector(circuit, decimals=3)
qsphere = plot_state_qsphere(quantum_state)
qsphere.savefig("utils/data/bell_qsphere.png")
self.image, self.rect = load_image('bell_qsphere.png', -1)
self.rect.inflate_ip(-100, -100)
self.image.convert()
|
https://github.com/QuSTaR/kaleidoscope
|
QuSTaR
|
# -*- coding: utf-8 -*-
# This code is part of Kaleidoscope.
#
# (C) Copyright IBM 2020.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""Interactive Bloch discs"""
import math
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import matplotlib.pyplot as plt
import matplotlib.colors
from kaleidoscope.utils import pi_check
from kaleidoscope.interactive.plotly_wrapper import PlotlyFigure, PlotlyWidget
from kaleidoscope.interactive.bloch.utils import bloch_components
from kaleidoscope.colors.utils import hex_to_rgb
from kaleidoscope.colors import BJY
from kaleidoscope.colors.cmap import cmap_to_plotly
NORM = plt.Normalize(-1, 1)
def bloch_sunburst(vec, colormap):
"""Create a Bloch disc using a Plotly sunburst.
Parameters:
vec (ndarray): A vector of Bloch components.
colormap (Colormap): A matplotlib colormap.
Returns:
go.Figure: A Plotly figure instance,
Raises:
ValueError: Input vector is not normalized.
"""
eps = 1e-6
vec = np.asarray(vec)
vec_norm = np.linalg.norm(vec)
if vec_norm > 1.0 + eps:
raise ValueError('Input vector has length {} greater than 1.0'.format(vec_norm))
for idx, val in enumerate(vec):
if abs(val) < 1e-15:
vec[idx] = 0
th = math.atan2(vec[1], vec[0])
if th < 0:
th = 2*np.pi+th
z_hex = matplotlib.colors.rgb2hex(colormap(NORM(vec[2])))
z_color = "rgba({},{},{},{})".format(*hex_to_rgb(z_hex), 0.95*vec_norm+0.05)
ring_color = "rgba({},{},{},{})".format(*hex_to_rgb('#000000'), 0.95*vec_norm+0.05)
wedge_str = "\u2329X\u232A= {x}<br>"
wedge_str += "\u2329Y\u232A= {y}<br>"
wedge_str += "\u2329Z\u232A= {z}<br>"
wedge_str += " \u03B8 = {th}<br>"
wedge_str += "|\u03C8| = {pur}"
th_str = pi_check(th, ndigits=3)
th_str = th_str.replace('pi', '\u03C0')
hover_text = [wedge_str.format(x=round(vec[0], 3),
y=round(vec[1], 3),
z=round(vec[2], 3),
th=th_str,
pur=np.round(vec_norm, 3))] + [None]
bloch = go.Sunburst(labels=[" ", " "],
parents=["", " "],
values=[2*np.pi-th, th],
hoverinfo="text",
hovertext=hover_text,
marker=dict(colors=[z_color, ring_color]))
return bloch
def bloch_disc(rho, figsize=None, title=None, colormap=None, as_widget=False):
"""Plot a Bloch disc for a single qubit.
Parameters:
rho (list or ndarray or Statevector or DensityMatrix): Input statevector, density matrix,
or Bloch components.
figsize (tuple): Figure size in pixels, default=(200,275).
title (str): Plot title.
colormap (Colormap): A matplotlib colormap.
as_widget (bool): Return plot as a widget.
Returns:
PlotlyFigure: A Plotly figure instance
PlotlyWidget : A Plotly widget if `as_widget=True`.
Example:
.. jupyter-execute::
import numpy as np
from qiskit import *
from qiskit.quantum_info import Statevector
from kaleidoscope.interactive import bloch_disc
qc = QuantumCircuit(1)
qc.ry(np.pi*np.random.random(), 0)
qc.rz(np.pi*np.random.random(), 0)
state = Statevector.from_instruction(qc)
bloch_disc(state)
"""
# A hack so I do not have to import the actual instances from Qiskit.
if rho.__class__.__name__ in ['Statevector', 'DensityMatrix'] \
and 'qiskit' in rho.__class__.__module__:
rho = rho.data
if len(rho) != 3:
rho = np.asarray(rho, dtype=complex)
comp = bloch_components(rho)
else:
comp = [rho]
if title:
title = [title] + ["\u2329Z\u232A"]
else:
title = [""] + ["\u2329Z\u232A"]
if colormap is None:
colormap = BJY
if figsize is None:
figsize = (200, 275)
fig = make_subplots(rows=1, cols=2,
specs=[[{'type': 'domain'}]+[{'type': 'xy'}]],
subplot_titles=title,
column_widths=[0.93]+[0.07])
fig.add_trace(bloch_sunburst(comp[0], colormap), row=1, col=1)
zval = comp[0][2]
zrange = [k*np.ones(1) for k in np.linspace(-1, 1, 100)]
idx = (np.abs(np.linspace(-1, 1, 100) - zval)).argmin()
tickvals = np.array([0, 49, 99, idx])
idx_sort = np.argsort(tickvals)
tickvals = tickvals[idx_sort]
ticktext = [-1, 0, 1, "\u25C0"+str(np.round(zval, 3))]
if zval <= -0.95:
ticktext[0] = ''
elif abs(zval) <= 0.05:
ticktext[1] = ''
elif zval >= 0.95:
ticktext[2] = ''
ticktext = [ticktext[kk] for kk in idx_sort]
PLOTLY_CMAP = cmap_to_plotly(colormap)
fig.append_trace(go.Heatmap(z=zrange,
colorscale=PLOTLY_CMAP,
showscale=False,
hoverinfo='none',
),
row=1, col=2
)
fig.update_yaxes(row=1, col=2, tickvals=tickvals,
ticktext=ticktext)
fig.update_yaxes(row=1, col=2, side="right")
fig.update_xaxes(row=1, col=2, visible=False)
fig.update_layout(margin=dict(t=30, l=10, r=0, b=0),
height=figsize[0],
width=figsize[1],
hoverlabel=dict(font_size=16,
font_family="courier,monospace",
align='left'
)
)
for ann in fig['layout']['annotations']:
ann['font'] = dict(size=14)
if as_widget:
return PlotlyWidget(fig)
return PlotlyFigure(fig)
def bloch_multi_disc(rho, figsize=None, titles=True, colormap=None, as_widget=False):
"""Plot Bloch discs for a multi-qubit state.
Parameters:
rho (list or ndarray or Statevector or DensityMatrix): Input statevector, density matrix.
figsize (tuple): Figure size in pixels, default=(125*num_qubits, 150).
titles (bool): Display titles.
colormap (Colormap): A matplotlib colormap.
as_widget (bool): Return plot as a widget.
Returns:
PlotlyFigure: A Plotly figure instance
PlotlyWidget : A Plotly widget if `as_widget=True`.
Example:
.. jupyter-execute::
import numpy as np
from qiskit import *
from qiskit.quantum_info import Statevector
from kaleidoscope.interactive import bloch_multi_disc
N = 4
qc = QuantumCircuit(N)
qc.h(range(N))
for kk in range(N):
qc.ry(2*np.pi*np.random.random(), kk)
for kk in range(N-1):
qc.cx(kk,kk+1)
for kk in range(N):
qc.rz(2*np.pi*np.random.random(), kk)
state = Statevector.from_instruction(qc)
bloch_multi_disc(state)
"""
# A hack so I do not have to import the actual instances from Qiskit.
if rho.__class__.__name__ in ['Statevector', 'DensityMatrix'] \
and 'qiskit' in rho.__class__.__module__:
rho = rho.data
rho = np.asarray(rho, dtype=complex)
comp = bloch_components(rho)
num = int(np.log2(rho.shape[0]))
nrows = 1
ncols = num
if figsize is None:
figsize = (ncols*125, 150)
if titles:
titles = ["Qubit {}".format(k) for k in range(num)] + ["\u2329Z\u232A"]
else:
titles = ["" for k in range(num)] + ["\u2329Z\u232A"]
if colormap is None:
colormap = BJY
fig = make_subplots(rows=nrows, cols=ncols+1,
specs=[[{'type': 'domain'}]*ncols+[{'type': 'xy'}]],
subplot_titles=titles,
column_widths=[0.95/num]*num+[0.05])
for jj in range(num):
fig.add_trace(bloch_sunburst(comp[jj], colormap), row=1, col=jj+1)
zrange = [k*np.ones(1) for k in np.linspace(-1, 1, 100)]
PLOTLY_CMAP = cmap_to_plotly(colormap)
fig.append_trace(go.Heatmap(z=zrange,
colorscale=PLOTLY_CMAP,
showscale=False,
hoverinfo='none',
),
row=1, col=num+1)
fig.update_yaxes(row=1, col=num+1, tickvals=[0, 49, 99],
ticktext=[-1, 0, 1])
fig.update_yaxes(row=1, col=num+1, side="right")
fig.update_xaxes(row=1, col=num+1, visible=False)
fig.update_layout(margin=dict(t=50, l=0, r=15, b=30),
width=figsize[0],
height=figsize[1],
hoverlabel=dict(font_size=14,
font_family="monospace",
align='left'
)
)
# Makes the subplot titles smaller than the 16pt default
for ann in fig['layout']['annotations']:
ann['font'] = dict(size=16)
if as_widget:
return PlotlyWidget(fig)
return PlotlyFigure(fig)
|
https://github.com/QuSTaR/kaleidoscope
|
QuSTaR
|
# This code is part of Qiskit.
#
# (C) Copyright IBM 2017, 2019.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
# pylint: disable=invalid-name
"""Interactive error map for IBM Quantum Experience devices."""
import math
from typing import Tuple, Union
import numpy as np
from plotly.subplots import make_subplots
import plotly.graph_objects as go
import matplotlib as mpl
from qiskit.providers.ibmq.ibmqbackend import IBMQBackend
from .plotly_wrapper import PlotlyWidget, PlotlyFigure
from ..device_layouts import DEVICE_LAYOUTS
from ..colormaps import (HELIX_LIGHT, HELIX_LIGHT_CMAP,
HELIX_DARK, HELIX_DARK_CMAP)
from ..exceptions import VisualizationValueError, VisualizationTypeError
def iplot_error_map(
backend: IBMQBackend,
figsize: Tuple[int] = (800, 500),
show_title: bool = True,
remove_badcal_edges: bool = True,
background_color: str = 'white',
as_widget: bool = False
) -> Union[PlotlyFigure, PlotlyWidget]:
"""Plot the error map of a device.
Args:
backend: Plot the error map for this backend.
figsize: Figure size in pixels.
show_title: Whether to show figure title.
remove_badcal_edges: Whether to remove bad CX gate calibration data.
background_color: Background color, either 'white' or 'black'.
as_widget: ``True`` if the figure is to be returned as a ``PlotlyWidget``.
Otherwise the figure is to be returned as a ``PlotlyFigure``.
Returns:
The error map figure.
Raises:
VisualizationValueError: If an invalid input is received.
VisualizationTypeError: If the specified `backend` is a simulator.
Example:
.. jupyter-execute::
:hide-code:
:hide-output:
from qiskit.test.ibmq_mock import mock_get_backend
mock_get_backend('FakeVigo')
.. jupyter-execute::
from qiskit import IBMQ
from qiskit.providers.ibmq.visualization import iplot_error_map
IBMQ.load_account()
provider = IBMQ.get_provider(group='open', project='main')
backend = provider.get_backend('ibmq_vigo')
iplot_error_map(backend, as_widget=True)
"""
meas_text_color = '#000000'
if background_color == 'white':
color_map = HELIX_LIGHT_CMAP
text_color = '#000000'
plotly_cmap = HELIX_LIGHT
elif background_color == 'black':
color_map = HELIX_DARK_CMAP
text_color = '#FFFFFF'
plotly_cmap = HELIX_DARK
else:
raise VisualizationValueError(
'"{}" is not a valid background_color selection.'.format(background_color))
if backend.configuration().simulator:
raise VisualizationTypeError('Requires a device backend, not a simulator.')
config = backend.configuration()
n_qubits = config.n_qubits
cmap = config.coupling_map
if n_qubits in DEVICE_LAYOUTS.keys():
grid_data = DEVICE_LAYOUTS[n_qubits]
else:
fig = go.Figure()
fig.update_layout(showlegend=False,
plot_bgcolor=background_color,
paper_bgcolor=background_color,
width=figsize[0], height=figsize[1],
margin=dict(t=60, l=0, r=0, b=0)
)
out = PlotlyWidget(fig)
return out
props = backend.properties().to_dict()
t1s = []
t2s = []
for qubit_props in props['qubits']:
count = 0
for item in qubit_props:
if item['name'] == 'T1':
t1s.append(item['value'])
count += 1
elif item['name'] == 'T2':
t2s.append(item['value'])
count += 1
if count == 2:
break
# U2 error rates
single_gate_errors = [0]*n_qubits
for gate in props['gates']:
if gate['gate'] == 'u2':
_qubit = gate['qubits'][0]
single_gate_errors[_qubit] = gate['parameters'][0]['value']
# Convert to percent
single_gate_errors = 100 * np.asarray(single_gate_errors)
avg_1q_err = np.mean(single_gate_errors)
max_1q_err = max(single_gate_errors)
single_norm = mpl.colors.Normalize(
vmin=min(single_gate_errors), vmax=max_1q_err)
q_colors = [mpl.colors.rgb2hex(color_map(single_norm(err))) for err in single_gate_errors]
line_colors = []
cx_idx = []
if n_qubits > 1 and cmap:
cx_errors = []
for cmap_qubits in cmap:
for gate in props['gates']:
if gate['qubits'] == cmap_qubits:
cx_errors.append(gate['parameters'][0]['value'])
break
else:
continue
# Convert to percent
cx_errors = 100 * np.asarray(cx_errors)
# remove bad cx edges
if remove_badcal_edges:
cx_idx = np.where(cx_errors != 100.0)[0]
else:
cx_idx = np.arange(len(cx_errors))
avg_cx_err = np.mean(cx_errors[cx_idx])
for err in cx_errors:
if err != 100.0 or not remove_badcal_edges:
cx_norm = mpl.colors.Normalize(
vmin=min(cx_errors[cx_idx]), vmax=max(cx_errors[cx_idx]))
line_colors.append(mpl.colors.rgb2hex(color_map(cx_norm(err))))
else:
line_colors.append("#ff0000")
# Measurement errors
read_err = []
for qubit in range(n_qubits):
for item in props['qubits'][qubit]:
if item['name'] == 'readout_error':
read_err.append(item['value'])
read_err = 100 * np.asarray(read_err)
avg_read_err = np.mean(read_err)
max_read_err = np.max(read_err)
if n_qubits < 10:
num_left = n_qubits
num_right = 0
else:
num_left = math.ceil(n_qubits / 2)
num_right = n_qubits - num_left
x_max = max([d[1] for d in grid_data])
y_max = max([d[0] for d in grid_data])
max_dim = max(x_max, y_max)
qubit_size = 32
font_size = 14
offset = 0
if cmap:
if y_max / max_dim < 0.33:
qubit_size = 24
font_size = 10
offset = 1
if n_qubits > 5:
right_meas_title = "Readout Error (%)"
else:
right_meas_title = None
if cmap and cx_idx.size > 0:
cx_title = "CNOT Error Rate [Avg. {}%]".format(np.round(avg_cx_err, 3))
else:
cx_title = None
fig = make_subplots(rows=2, cols=11, row_heights=[0.95, 0.05],
vertical_spacing=0.15,
specs=[[{"colspan": 2}, None, {"colspan": 6},
None, None, None,
None, None, {"colspan": 2},
None, None],
[{"colspan": 4}, None, None,
None, None, None,
{"colspan": 4}, None, None,
None, None]],
subplot_titles=("Readout Error (%)", None, right_meas_title,
"Hadamard Error Rate [Avg. {}%]".format(
np.round(avg_1q_err, 3)),
cx_title)
)
# Add lines for couplings
if cmap and n_qubits > 1 and cx_idx.size > 0:
for ind, edge in enumerate(cmap):
is_symmetric = False
if edge[::-1] in cmap:
is_symmetric = True
y_start = grid_data[edge[0]][0] + offset
x_start = grid_data[edge[0]][1]
y_end = grid_data[edge[1]][0] + offset
x_end = grid_data[edge[1]][1]
if is_symmetric:
if y_start == y_end:
x_end = (x_end - x_start) / 2 + x_start
x_mid = x_end
y_mid = y_start
elif x_start == x_end:
y_end = (y_end - y_start) / 2 + y_start
x_mid = x_start
y_mid = y_end
else:
x_end = (x_end - x_start) / 2 + x_start
y_end = (y_end - y_start) / 2 + y_start
x_mid = x_end
y_mid = y_end
else:
if y_start == y_end:
x_mid = (x_end - x_start) / 2 + x_start
y_mid = y_end
elif x_start == x_end:
x_mid = x_end
y_mid = (y_end - y_start) / 2 + y_start
else:
x_mid = (x_end - x_start) / 2 + x_start
y_mid = (y_end - y_start) / 2 + y_start
fig.add_trace(
go.Scatter(x=[x_start, x_mid, x_end],
y=[-y_start, -y_mid, -y_end],
mode="lines",
line=dict(width=6,
color=line_colors[ind]),
hoverinfo='text',
hovertext='CX<sub>err</sub>{B}_{A} = {err} %'.format(
A=edge[0], B=edge[1], err=np.round(cx_errors[ind], 3))
),
row=1, col=3)
# Add the qubits themselves
qubit_text = []
qubit_str = "<b>Qubit {}</b><br>H<sub>err</sub> = {} %"
qubit_str += "<br>T1 = {} \u03BCs<br>T2 = {} \u03BCs"
for kk in range(n_qubits):
qubit_text.append(qubit_str.format(kk,
np.round(single_gate_errors[kk], 3),
np.round(t1s[kk], 2),
np.round(t2s[kk], 2)))
if n_qubits > 20:
qubit_size = 23
font_size = 11
if n_qubits > 50:
qubit_size = 20
font_size = 9
qtext_color = []
for ii in range(n_qubits):
if background_color == 'black':
if single_gate_errors[ii] > 0.8*max_1q_err:
qtext_color.append('black')
else:
qtext_color.append('white')
else:
qtext_color.append('white')
fig.add_trace(go.Scatter(
x=[d[1] for d in grid_data],
y=[-d[0]-offset for d in grid_data],
mode="markers+text",
marker=go.scatter.Marker(size=qubit_size,
color=q_colors,
opacity=1),
text=[str(ii) for ii in range(n_qubits)],
textposition="middle center",
textfont=dict(size=font_size, color=qtext_color),
hoverinfo="text",
hovertext=qubit_text), row=1, col=3)
fig.update_xaxes(row=1, col=3, visible=False)
_range = None
if offset:
_range = [-3.5, 0.5]
fig.update_yaxes(row=1,
col=3,
visible=False,
range=_range)
# H error rate colorbar
min_1q_err = min(single_gate_errors)
max_1q_err = max(single_gate_errors)
if n_qubits > 1:
fig.add_trace(go.Heatmap(z=[np.linspace(min_1q_err,
max_1q_err, 100),
np.linspace(min_1q_err,
max_1q_err, 100)],
colorscale=plotly_cmap,
showscale=False,
hoverinfo='none'), row=2, col=1)
fig.update_yaxes(row=2,
col=1,
visible=False)
fig.update_xaxes(row=2,
col=1,
tickvals=[0, 49, 99],
ticktext=[np.round(min_1q_err, 3),
np.round((max_1q_err-min_1q_err)/2+min_1q_err, 3),
np.round(max_1q_err, 3)])
# CX error rate colorbar
if cmap and n_qubits > 1 and cx_idx.size > 0:
min_cx_err = min(cx_errors)
max_cx_err = max(cx_errors)
if min_cx_err == max_cx_err:
min_cx_err = 0 # Force more than 1 color.
fig.add_trace(go.Heatmap(z=[np.linspace(min_cx_err,
max_cx_err, 100),
np.linspace(min_cx_err,
max_cx_err, 100)],
colorscale=plotly_cmap,
showscale=False,
hoverinfo='none'), row=2, col=7)
fig.update_yaxes(row=2, col=7, visible=False)
min_cx_idx_err = min(cx_errors[cx_idx])
max_cx_idx_err = max(cx_errors[cx_idx])
fig.update_xaxes(row=2, col=7,
tickvals=[0, 49, 99],
ticktext=[np.round(min_cx_idx_err, 3),
np.round((max_cx_idx_err-min_cx_idx_err)/2+min_cx_idx_err, 3),
np.round(max_cx_idx_err, 3)])
hover_text = "<b>Qubit {}</b><br>M<sub>err</sub> = {} %"
# Add the left side meas errors
for kk in range(num_left-1, -1, -1):
fig.add_trace(go.Bar(x=[read_err[kk]], y=[kk],
orientation='h',
marker=dict(color='#eedccb'),
hoverinfo="text",
hoverlabel=dict(font=dict(color=meas_text_color)),
hovertext=[hover_text.format(kk,
np.round(read_err[kk], 3)
)]
),
row=1, col=1)
fig.add_trace(go.Scatter(x=[avg_read_err, avg_read_err],
y=[-0.25, num_left-1+0.25],
mode='lines',
hoverinfo='none',
line=dict(color=text_color,
width=2,
dash='dot')), row=1, col=1)
fig.update_yaxes(row=1, col=1,
tickvals=list(range(num_left)),
autorange="reversed")
fig.update_xaxes(row=1, col=1,
range=[0, 1.1*max_read_err],
tickvals=[0, np.round(avg_read_err, 2),
np.round(max_read_err, 2)],
showline=True, linewidth=1, linecolor=text_color,
tickcolor=text_color,
ticks="outside",
showgrid=False,
zeroline=False)
# Add the right side meas errors, if any
if num_right:
for kk in range(n_qubits-1, num_left-1, -1):
fig.add_trace(go.Bar(x=[-read_err[kk]],
y=[kk],
orientation='h',
marker=dict(color='#eedccb'),
hoverinfo="text",
hoverlabel=dict(font=dict(color=meas_text_color)),
hovertext=[hover_text.format(kk,
np.round(read_err[kk], 3))]
),
row=1, col=9)
fig.add_trace(go.Scatter(x=[-avg_read_err, -avg_read_err],
y=[num_left-0.25, n_qubits-1+0.25],
mode='lines',
hoverinfo='none',
line=dict(color=text_color,
width=2,
dash='dot')
), row=1, col=9)
fig.update_yaxes(row=1,
col=9,
tickvals=list(range(n_qubits-1, num_left-1, -1)),
side='right',
autorange="reversed",
)
fig.update_xaxes(row=1,
col=9,
range=[-1.1*max_read_err, 0],
tickvals=[0, -np.round(avg_read_err, 2), -np.round(max_read_err, 2)],
ticktext=[0, np.round(avg_read_err, 2), np.round(max_read_err, 2)],
showline=True, linewidth=1, linecolor=text_color,
tickcolor=text_color,
ticks="outside",
showgrid=False,
zeroline=False)
# Makes the subplot titles smaller than the 16pt default
for ann in fig['layout']['annotations']:
ann['font'] = dict(size=13)
title_text = "{} Error Map".format(backend.name()) if show_title else ''
fig.update_layout(showlegend=False,
plot_bgcolor=background_color,
paper_bgcolor=background_color,
width=figsize[0], height=figsize[1],
title=dict(text=title_text, x=0.452),
title_font_size=20,
font=dict(color=text_color),
margin=dict(t=60, l=0, r=40, b=0)
)
if as_widget:
return PlotlyWidget(fig)
return PlotlyFigure(fig)
|
https://github.com/QuSTaR/kaleidoscope
|
QuSTaR
|
# This code is part of Qiskit.
#
# (C) Copyright IBM 2017, 2018.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""A module for visualizing device coupling maps."""
import math
from typing import List
import numpy as np
from qiskit.exceptions import MissingOptionalLibraryError, QiskitError
from qiskit.providers.backend import BackendV2
from qiskit.tools.visualization import HAS_MATPLOTLIB, VisualizationError
from qiskit.visualization.utils import matplotlib_close_if_inline
def plot_gate_map(
backend,
figsize=None,
plot_directed=False,
label_qubits=True,
qubit_size=None,
line_width=4,
font_size=None,
qubit_color=None,
qubit_labels=None,
line_color=None,
font_color="w",
ax=None,
filename=None,
qubit_coordinates=None,
):
"""Plots the gate map of a device.
Args:
backend (BaseBackend): The backend instance that will be used to plot the device
gate map.
figsize (tuple): Output figure size (wxh) in inches.
plot_directed (bool): Plot directed coupling map.
label_qubits (bool): Label the qubits.
qubit_size (float): Size of qubit marker.
line_width (float): Width of lines.
font_size (int): Font size of qubit labels.
qubit_color (list): A list of colors for the qubits
qubit_labels (list): A list of qubit labels
line_color (list): A list of colors for each line from coupling_map.
font_color (str): The font color for the qubit labels.
ax (Axes): A Matplotlib axes instance.
filename (str): file path to save image to.
Returns:
Figure: A Matplotlib figure instance.
Raises:
QiskitError: if tried to pass a simulator, or if the backend is None,
but one of num_qubits, mpl_data, or cmap is None.
MissingOptionalLibraryError: if matplotlib not installed.
Example:
.. jupyter-execute::
:hide-code:
:hide-output:
from qiskit.test.ibmq_mock import mock_get_backend
mock_get_backend('FakeVigo')
.. jupyter-execute::
from qiskit import QuantumCircuit, execute, IBMQ
from qiskit.visualization import plot_gate_map
%matplotlib inline
provider = IBMQ.load_account()
accountProvider = IBMQ.get_provider(hub='ibm-q')
backend = accountProvider.get_backend('ibmq_vigo')
plot_gate_map(backend)
"""
if not HAS_MATPLOTLIB:
raise MissingOptionalLibraryError(
libname="Matplotlib",
name="plot_gate_map",
pip_install="pip install matplotlib",
)
if isinstance(backend, BackendV2):
pass
elif backend.configuration().simulator:
raise QiskitError("Requires a device backend, not simulator.")
qubit_coordinates_map = {}
qubit_coordinates_map[1] = [[0, 0]]
qubit_coordinates_map[5] = [[1, 0], [0, 1], [1, 1], [1, 2], [2, 1]]
qubit_coordinates_map[7] = [[0, 0], [0, 1], [0, 2], [1, 1], [2, 0], [2, 1], [2, 2]]
qubit_coordinates_map[20] = [
[0, 0],
[0, 1],
[0, 2],
[0, 3],
[0, 4],
[1, 0],
[1, 1],
[1, 2],
[1, 3],
[1, 4],
[2, 0],
[2, 1],
[2, 2],
[2, 3],
[2, 4],
[3, 0],
[3, 1],
[3, 2],
[3, 3],
[3, 4],
]
qubit_coordinates_map[15] = [
[0, 0],
[0, 1],
[0, 2],
[0, 3],
[0, 4],
[0, 5],
[0, 6],
[1, 7],
[1, 6],
[1, 5],
[1, 4],
[1, 3],
[1, 2],
[1, 1],
[1, 0],
]
qubit_coordinates_map[16] = [
[1, 0],
[1, 1],
[2, 1],
[3, 1],
[1, 2],
[3, 2],
[0, 3],
[1, 3],
[3, 3],
[4, 3],
[1, 4],
[3, 4],
[1, 5],
[2, 5],
[3, 5],
[1, 6],
]
qubit_coordinates_map[27] = [
[1, 0],
[1, 1],
[2, 1],
[3, 1],
[1, 2],
[3, 2],
[0, 3],
[1, 3],
[3, 3],
[4, 3],
[1, 4],
[3, 4],
[1, 5],
[2, 5],
[3, 5],
[1, 6],
[3, 6],
[0, 7],
[1, 7],
[3, 7],
[4, 7],
[1, 8],
[3, 8],
[1, 9],
[2, 9],
[3, 9],
[3, 10],
]
qubit_coordinates_map[28] = [
[0, 2],
[0, 3],
[0, 4],
[0, 5],
[0, 6],
[1, 2],
[1, 6],
[2, 0],
[2, 1],
[2, 2],
[2, 3],
[2, 4],
[2, 5],
[2, 6],
[2, 7],
[2, 8],
[3, 0],
[3, 4],
[3, 8],
[4, 0],
[4, 1],
[4, 2],
[4, 3],
[4, 4],
[4, 5],
[4, 6],
[4, 7],
[4, 8],
]
qubit_coordinates_map[53] = [
[0, 2],
[0, 3],
[0, 4],
[0, 5],
[0, 6],
[1, 2],
[1, 6],
[2, 0],
[2, 1],
[2, 2],
[2, 3],
[2, 4],
[2, 5],
[2, 6],
[2, 7],
[2, 8],
[3, 0],
[3, 4],
[3, 8],
[4, 0],
[4, 1],
[4, 2],
[4, 3],
[4, 4],
[4, 5],
[4, 6],
[4, 7],
[4, 8],
[5, 2],
[5, 6],
[6, 0],
[6, 1],
[6, 2],
[6, 3],
[6, 4],
[6, 5],
[6, 6],
[6, 7],
[6, 8],
[7, 0],
[7, 4],
[7, 8],
[8, 0],
[8, 1],
[8, 2],
[8, 3],
[8, 4],
[8, 5],
[8, 6],
[8, 7],
[8, 8],
[9, 2],
[9, 6],
]
qubit_coordinates_map[65] = [
[0, 0],
[0, 1],
[0, 2],
[0, 3],
[0, 4],
[0, 5],
[0, 6],
[0, 7],
[0, 8],
[0, 9],
[1, 0],
[1, 4],
[1, 8],
[2, 0],
[2, 1],
[2, 2],
[2, 3],
[2, 4],
[2, 5],
[2, 6],
[2, 7],
[2, 8],
[2, 9],
[2, 10],
[3, 2],
[3, 6],
[3, 10],
[4, 0],
[4, 1],
[4, 2],
[4, 3],
[4, 4],
[4, 5],
[4, 6],
[4, 7],
[4, 8],
[4, 9],
[4, 10],
[5, 0],
[5, 4],
[5, 8],
[6, 0],
[6, 1],
[6, 2],
[6, 3],
[6, 4],
[6, 5],
[6, 6],
[6, 7],
[6, 8],
[6, 9],
[6, 10],
[7, 2],
[7, 6],
[7, 10],
[8, 1],
[8, 2],
[8, 3],
[8, 4],
[8, 5],
[8, 6],
[8, 7],
[8, 8],
[8, 9],
[8, 10],
]
if isinstance(backend, BackendV2):
num_qubits = backend.num_qubits
coupling_map = backend.plot_coupling_map
else:
config = backend.configuration()
num_qubits = config.n_qubits
coupling_map = config.coupling_map
# don't reference dictionary if provided as a parameter
if qubit_coordinates is None:
qubit_coordinates = qubit_coordinates_map.get(num_qubits)
# try to adjust num_qubits to match the next highest hardcoded grid size
if qubit_coordinates is None:
if any([num_qubits < key for key in qubit_coordinates_map.keys()]):
num_qubits_roundup = max(qubit_coordinates_map.keys())
for key in qubit_coordinates_map.keys():
if key < num_qubits_roundup and key > num_qubits:
num_qubits_roundup = key
qubit_coordinates = qubit_coordinates_map.get(num_qubits_roundup)
return plot_coupling_map(
num_qubits,
qubit_coordinates,
coupling_map,
figsize,
plot_directed,
label_qubits,
qubit_size,
line_width,
font_size,
qubit_color,
qubit_labels,
line_color,
font_color,
ax,
filename,
)
def plot_coupling_map(
num_qubits: int,
qubit_coordinates: List[List[int]],
coupling_map: List[List[int]],
figsize=None,
plot_directed=False,
label_qubits=True,
qubit_size=None,
line_width=4,
font_size=None,
qubit_color=None,
qubit_labels=None,
line_color=None,
font_color="w",
ax=None,
filename=None,
):
"""Plots an arbitrary coupling map of qubits (embedded in a plane).
Args:
num_qubits (int): The number of qubits defined and plotted.
qubit_coordinates (List[List[int]]): A list of two-element lists, with entries of each nested
list being the planar coordinates in a 0-based square grid where each qubit is located.
coupling_map (List[List[int]]): A list of two-element lists, with entries of each nested
list being the qubit numbers of the bonds to be plotted.
figsize (tuple): Output figure size (wxh) in inches.
plot_directed (bool): Plot directed coupling map.
label_qubits (bool): Label the qubits.
qubit_size (float): Size of qubit marker.
line_width (float): Width of lines.
font_size (int): Font size of qubit labels.
qubit_color (list): A list of colors for the qubits
qubit_labels (list): A list of qubit labels
line_color (list): A list of colors for each line from coupling_map.
font_color (str): The font color for the qubit labels.
ax (Axes): A Matplotlib axes instance.
filename (str): file path to save image to.
Returns:
Figure: A Matplotlib figure instance.
Raises:
MissingOptionalLibraryError: if matplotlib not installed.
QiskitError: If length of qubit labels does not match number of qubits.
Example:
.. jupyter-execute::
from qiskit.visualization import plot_coupling_map
%matplotlib inline
num_qubits = 8
coupling_map = [[0, 1], [1, 2], [2, 3], [3, 5], [4, 5], [5, 6], [2, 4], [6, 7]]
qubit_coordinates = [[0, 1], [1, 1], [1, 0], [1, 2], [2, 0], [2, 2], [2, 1], [3, 1]]
plot_coupling_map(num_qubits, coupling_map, qubit_coordinates)
"""
if not HAS_MATPLOTLIB:
raise MissingOptionalLibraryError(
libname="Matplotlib",
name="plot_coupling_map",
pip_install="pip install matplotlib",
)
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
input_axes = False
if ax:
input_axes = True
if font_size is None:
font_size = 12
if qubit_size is None:
qubit_size = 24
if num_qubits > 20:
qubit_size = 28
font_size = 10
if qubit_labels is None:
qubit_labels = list(range(num_qubits))
else:
if len(qubit_labels) != num_qubits:
raise QiskitError("Length of qubit labels does not equal number of qubits.")
if qubit_coordinates is not None:
grid_data = qubit_coordinates
else:
if not input_axes:
fig, ax = plt.subplots(figsize=(5, 5))
ax.axis("off")
if filename:
fig.savefig(filename)
return fig
x_max = max(d[1] for d in grid_data)
y_max = max(d[0] for d in grid_data)
max_dim = max(x_max, y_max)
if figsize is None:
if num_qubits == 1 or (x_max / max_dim > 0.33 and y_max / max_dim > 0.33):
figsize = (5, 5)
else:
figsize = (9, 3)
if ax is None:
fig, ax = plt.subplots(figsize=figsize)
ax.axis("off")
# set coloring
if qubit_color is None:
qubit_color = ["#648fff"] * num_qubits
if line_color is None:
line_color = ["#648fff"] * len(coupling_map) if coupling_map else []
# Add lines for couplings
if num_qubits != 1:
for ind, edge in enumerate(coupling_map):
is_symmetric = False
if edge[::-1] in coupling_map:
is_symmetric = True
y_start = grid_data[edge[0]][0]
x_start = grid_data[edge[0]][1]
y_end = grid_data[edge[1]][0]
x_end = grid_data[edge[1]][1]
if is_symmetric:
if y_start == y_end:
x_end = (x_end - x_start) / 2 + x_start
elif x_start == x_end:
y_end = (y_end - y_start) / 2 + y_start
else:
x_end = (x_end - x_start) / 2 + x_start
y_end = (y_end - y_start) / 2 + y_start
ax.add_artist(
plt.Line2D(
[x_start, x_end],
[-y_start, -y_end],
color=line_color[ind],
linewidth=line_width,
zorder=0,
)
)
if plot_directed:
dx = x_end - x_start
dy = y_end - y_start
if is_symmetric:
x_arrow = x_start + dx * 0.95
y_arrow = -y_start - dy * 0.95
dx_arrow = dx * 0.01
dy_arrow = -dy * 0.01
head_width = 0.15
else:
x_arrow = x_start + dx * 0.5
y_arrow = -y_start - dy * 0.5
dx_arrow = dx * 0.2
dy_arrow = -dy * 0.2
head_width = 0.2
ax.add_patch(
mpatches.FancyArrow(
x_arrow,
y_arrow,
dx_arrow,
dy_arrow,
head_width=head_width,
length_includes_head=True,
edgecolor=None,
linewidth=0,
facecolor=line_color[ind],
zorder=1,
)
)
# Add circles for qubits
for var, idx in enumerate(grid_data):
# add check if num_qubits had been rounded up
if var >= num_qubits:
break
_idx = [idx[1], -idx[0]]
ax.add_artist(
mpatches.Ellipse(
_idx,
qubit_size / 48,
qubit_size / 48, # This is here so that the changes
color=qubit_color[var],
zorder=1,
)
) # to how qubits are plotted does
if label_qubits: # not affect qubit size kwarg.
ax.text(
*_idx,
s=qubit_labels[var],
horizontalalignment="center",
verticalalignment="center",
color=font_color,
size=font_size,
weight="bold",
)
ax.set_xlim([-1, x_max + 1])
ax.set_ylim([-(y_max + 1), 1])
ax.set_aspect("equal")
if not input_axes:
matplotlib_close_if_inline(fig)
if filename:
fig.savefig(filename)
return fig
return None
def plot_circuit_layout(circuit, backend, view="virtual", qubit_coordinates=None):
"""Plot the layout of a circuit transpiled for a given target backend.
Args:
circuit (QuantumCircuit): Input quantum circuit.
backend (BaseBackend): Target backend.
view (str): Layout view: either 'virtual' or 'physical'.
Returns:
Figure: A matplotlib figure showing layout.
Raises:
QiskitError: Invalid view type given.
VisualizationError: Circuit has no layout attribute.
Example:
.. jupyter-execute::
:hide-code:
:hide-output:
from qiskit.test.ibmq_mock import mock_get_backend
mock_get_backend('FakeVigo')
.. jupyter-execute::
import numpy as np
from qiskit import QuantumCircuit, IBMQ, transpile
from qiskit.visualization import plot_histogram, plot_gate_map, plot_circuit_layout
from qiskit.tools.monitor import job_monitor
import matplotlib.pyplot as plt
%matplotlib inline
IBMQ.load_account()
ghz = QuantumCircuit(3, 3)
ghz.h(0)
for idx in range(1,3):
ghz.cx(0,idx)
ghz.measure(range(3), range(3))
provider = IBMQ.get_provider(hub='ibm-q')
backend = provider.get_backend('ibmq_vigo')
new_circ_lv3 = transpile(ghz, backend=backend, optimization_level=3)
plot_circuit_layout(new_circ_lv3, backend)
"""
if circuit._layout is None:
raise QiskitError("Circuit has no layout. Perhaps it has not been transpiled.")
if isinstance(backend, BackendV2):
num_qubits = backend.num_qubits
else:
num_qubits = backend.configuration().n_qubits
qubits = []
qubit_labels = [None] * num_qubits
bit_locations = {
bit: {"register": register, "index": index}
for register in circuit._layout.get_registers()
for index, bit in enumerate(register)
}
for index, qubit in enumerate(circuit._layout.get_virtual_bits()):
if qubit not in bit_locations:
bit_locations[qubit] = {"register": None, "index": index}
if view == "virtual":
for key, val in circuit._layout.get_virtual_bits().items():
bit_register = bit_locations[key]["register"]
if bit_register is None or bit_register.name != "ancilla":
qubits.append(val)
qubit_labels[val] = bit_locations[key]["index"]
elif view == "physical":
for key, val in circuit._layout.get_physical_bits().items():
bit_register = bit_locations[val]["register"]
if bit_register is None or bit_register.name != "ancilla":
qubits.append(key)
qubit_labels[key] = key
else:
raise VisualizationError("Layout view must be 'virtual' or 'physical'.")
qcolors = ["#648fff"] * num_qubits
for k in qubits:
qcolors[k] = "k"
if isinstance(backend, BackendV2):
cmap = backend.plot_coupling_map
else:
cmap = backend.configuration().coupling_map
lcolors = ["#648fff"] * len(cmap)
for idx, edge in enumerate(cmap):
if edge[0] in qubits and edge[1] in qubits:
lcolors[idx] = "k"
fig = plot_gate_map(
backend,
qubit_color=qcolors,
qubit_labels=qubit_labels,
line_color=lcolors,
qubit_coordinates=qubit_coordinates,
)
return fig
def plot_error_map(backend, figsize=(12, 9), show_title=True):
"""Plots the error map of a given backend.
Args:
backend (IBMQBackend): Given backend.
figsize (tuple): Figure size in inches.
show_title (bool): Show the title or not.
Returns:
Figure: A matplotlib figure showing error map.
Raises:
VisualizationError: Input is not IBMQ backend.
VisualizationError: The backend does not provide gate errors for the 'sx' gate.
MissingOptionalLibraryError: If seaborn is not installed
Example:
.. jupyter-execute::
:hide-code:
:hide-output:
from qiskit.test.ibmq_mock import mock_get_backend
mock_get_backend('FakeVigo')
.. jupyter-execute::
from qiskit import QuantumCircuit, execute, IBMQ
from qiskit.visualization import plot_error_map
%matplotlib inline
IBMQ.load_account()
provider = IBMQ.get_provider(hub='ibm-q')
backend = provider.get_backend('ibmq_vigo')
plot_error_map(backend)
"""
try:
import seaborn as sns
except ImportError as ex:
raise MissingOptionalLibraryError(
libname="seaborn",
name="plot_error_map",
pip_install="pip install seaborn",
) from ex
if not HAS_MATPLOTLIB:
raise MissingOptionalLibraryError(
libname="Matplotlib",
name="plot_error_map",
pip_install="pip install matplotlib",
)
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import gridspec, ticker
color_map = sns.cubehelix_palette(reverse=True, as_cmap=True)
props = backend.properties().to_dict()
config = backend.configuration().to_dict()
num_qubits = config["n_qubits"]
# sx error rates
single_gate_errors = [0] * num_qubits
for gate in props["gates"]:
if gate["gate"] == "sx":
_qubit = gate["qubits"][0]
for param in gate["parameters"]:
if param["name"] == "gate_error":
single_gate_errors[_qubit] = param["value"]
break
else:
raise VisualizationError(
f"Backend '{backend}' did not supply an error for the 'sx' gate."
)
# Convert to percent
single_gate_errors = 100 * np.asarray(single_gate_errors)
avg_1q_err = np.mean(single_gate_errors)
single_norm = matplotlib.colors.Normalize(
vmin=min(single_gate_errors), vmax=max(single_gate_errors)
)
q_colors = [color_map(single_norm(err)) for err in single_gate_errors]
cmap = config["coupling_map"]
directed = False
line_colors = []
if cmap:
directed = False
if num_qubits < 20:
for edge in cmap:
if [edge[1], edge[0]] not in cmap:
directed = True
break
cx_errors = []
for line in cmap:
for item in props["gates"]:
if item["qubits"] == line:
cx_errors.append(item["parameters"][0]["value"])
break
else:
continue
# Convert to percent
cx_errors = 100 * np.asarray(cx_errors)
avg_cx_err = np.mean(cx_errors)
cx_norm = matplotlib.colors.Normalize(vmin=min(cx_errors), vmax=max(cx_errors))
line_colors = [color_map(cx_norm(err)) for err in cx_errors]
# Measurement errors
read_err = []
for qubit in range(num_qubits):
for item in props["qubits"][qubit]:
if item["name"] == "readout_error":
read_err.append(item["value"])
read_err = 100 * np.asarray(read_err)
avg_read_err = np.mean(read_err)
max_read_err = np.max(read_err)
fig = plt.figure(figsize=figsize)
gridspec.GridSpec(nrows=2, ncols=3)
grid_spec = gridspec.GridSpec(
12,
12,
height_ratios=[1] * 11 + [0.5],
width_ratios=[2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2],
)
left_ax = plt.subplot(grid_spec[2:10, :1])
main_ax = plt.subplot(grid_spec[:11, 1:11])
right_ax = plt.subplot(grid_spec[2:10, 11:])
bleft_ax = plt.subplot(grid_spec[-1, :5])
if cmap:
bright_ax = plt.subplot(grid_spec[-1, 7:])
qubit_size = 28
if num_qubits <= 5:
qubit_size = 20
plot_gate_map(
backend,
qubit_color=q_colors,
line_color=line_colors,
qubit_size=qubit_size,
line_width=5,
plot_directed=directed,
ax=main_ax,
)
main_ax.axis("off")
main_ax.set_aspect(1)
if cmap:
single_cb = matplotlib.colorbar.ColorbarBase(
bleft_ax, cmap=color_map, norm=single_norm, orientation="horizontal"
)
tick_locator = ticker.MaxNLocator(nbins=5)
single_cb.locator = tick_locator
single_cb.update_ticks()
single_cb.update_ticks()
bleft_ax.set_title(f"H error rate (%) [Avg. = {round(avg_1q_err, 3)}]")
if cmap is None:
bleft_ax.axis("off")
bleft_ax.set_title(f"H error rate (%) = {round(avg_1q_err, 3)}")
if cmap:
cx_cb = matplotlib.colorbar.ColorbarBase(
bright_ax, cmap=color_map, norm=cx_norm, orientation="horizontal"
)
tick_locator = ticker.MaxNLocator(nbins=5)
cx_cb.locator = tick_locator
cx_cb.update_ticks()
bright_ax.set_title(f"CNOT error rate (%) [Avg. = {round(avg_cx_err, 3)}]")
if num_qubits < 10:
num_left = num_qubits
num_right = 0
else:
num_left = math.ceil(num_qubits / 2)
num_right = num_qubits - num_left
left_ax.barh(range(num_left), read_err[:num_left], align="center", color="#DDBBBA")
left_ax.axvline(avg_read_err, linestyle="--", color="#212121")
left_ax.set_yticks(range(num_left))
left_ax.set_xticks([0, round(avg_read_err, 2), round(max_read_err, 2)])
left_ax.set_yticklabels([str(kk) for kk in range(num_left)], fontsize=12)
left_ax.invert_yaxis()
left_ax.set_title("Readout Error (%)", fontsize=12)
for spine in left_ax.spines.values():
spine.set_visible(False)
if num_right:
right_ax.barh(
range(num_left, num_qubits),
read_err[num_left:],
align="center",
color="#DDBBBA",
)
right_ax.axvline(avg_read_err, linestyle="--", color="#212121")
right_ax.set_yticks(range(num_left, num_qubits))
right_ax.set_xticks([0, round(avg_read_err, 2), round(max_read_err, 2)])
right_ax.set_yticklabels(
[str(kk) for kk in range(num_left, num_qubits)], fontsize=12
)
right_ax.invert_yaxis()
right_ax.invert_xaxis()
right_ax.yaxis.set_label_position("right")
right_ax.yaxis.tick_right()
right_ax.set_title("Readout Error (%)", fontsize=12)
else:
right_ax.axis("off")
for spine in right_ax.spines.values():
spine.set_visible(False)
if show_title:
fig.suptitle(f"{backend.name()} Error Map", fontsize=24, y=0.9)
matplotlib_close_if_inline(fig)
return fig
|
https://github.com/QuSTaR/kaleidoscope
|
QuSTaR
|
# -*- coding: utf-8 -*-
# This file is part of Kaleidoscope.
#
# (C) Copyright IBM 2020.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
# pylint: disable=invalid-unary-operand-type, consider-using-generator
"""CNOT error density plot"""
import numpy as np
from scipy.stats import gaussian_kde
import matplotlib as mpl
import matplotlib.pyplot as plt
from qiskit.providers.models.backendproperties import BackendProperties
from kaleidoscope.colors import COLORS1, COLORS2, COLORS3, COLORS4, COLORS5, COLORS14
from kaleidoscope.errors import KaleidoscopeError
from kaleidoscope.qiskit.backends.pseudobackend import properties_to_pseudobackend
def cnot_error_density(backends,
figsize=None,
colors=None,
offset=None,
scale='log',
covariance_factor=0.1,
xlim=None,
text_xval=None,
xticks=None):
"""Plot CNOT error distribution for one or more IBMQ backends.
Parameters:
backends (list or IBMQBackend or BackendProperties): A single or ist of IBMQBackend
instances or properties.
figsize (tuple): Optional figure size in inches.
colors (list): A list of Matplotlib compatible colors to plot with.
offset (float): Positive offset for spacing out the backends.
scale (str): 'linear' or 'log' scaling of x-axis.
covariance_factor (float): Sets the width of the gaussian for each point.
xlim (list or tuple): Optional lower and upper limits of cnot error values.
text_xval (float): Optional xaxis value at which to start the backend text.
xticks (list): Optional list of xaxis ticks to plot.
Returns:
Figure: A matplotlib Figure instance.
Raises:
KaleidoscopeError: A backend with < 2 qubits was passed.
KaleidoscopeError: Number of colors did not match number of backends.
Example:
.. jupyter-execute::
from qiskit import *
from kaleidoscope.qiskit.backends import cnot_error_density
provider = IBMQ.load_account()
backends = []
backends.append(provider.backends.ibmq_vigo)
backends.append(provider.backends.ibmq_ourense)
backends.append(provider.backends.ibmq_valencia)
backends.append(provider.backends.ibmq_santiago)
cnot_error_density(backends)
"""
if not isinstance(backends, list):
backends = [backends]
for idx, back in enumerate(backends):
if isinstance(back, BackendProperties):
backends[idx] = properties_to_pseudobackend(back)
for back in backends:
if back.configuration().n_qubits < 2:
raise KaleidoscopeError('Number of backend qubits must be > 1')
if scale not in ['linear', 'log']:
raise KaleidoscopeError("scale must be 'linear' or 'log'.")
# Attempt to autosize if figsize=None
if figsize is None:
if len(backends) > 1:
fig = plt.figure(figsize=(12, len(backends)*1.5))
else:
fig = plt.figure(figsize=(12, 2))
else:
fig = plt.figure(figsize=figsize)
text_color = 'k'
if offset is None:
offset = 100 if len(backends) > 3 else 200
offset = -offset
if colors is None:
if len(backends) == 1:
colors = COLORS1
elif len(backends) == 2:
colors = COLORS2
elif len(backends) == 3:
colors = COLORS3
elif len(backends) == 4:
colors = COLORS4
elif len(backends) == 5:
colors = COLORS5
else:
colors = [COLORS14[kk % 14] for kk in range(len(backends))]
else:
if len(colors) != len(backends):
raise KaleidoscopeError('Number of colors does not match number of backends.')
cx_errors = []
for idx, back in enumerate(backends):
back_props = back.properties().to_dict()
cx_errs = []
meas_errs = []
for gate in back_props['gates']:
if len(gate['qubits']) == 2:
# Ignore cx gates with values of 1.0
if gate['parameters'][0]['value'] != 1.0:
cx_errs.append(gate['parameters'][0]['value'])
for qubit in back_props['qubits']:
for item in qubit:
if item['name'] == 'readout_error':
meas_errs.append(item['value'])
cx_errors.append(100*np.asarray(cx_errs))
max_cx_err = max([cerr.max() for cerr in cx_errors])
min_cx_err = min([cerr.min() for cerr in cx_errors])
if xlim is None:
if scale == 'linear':
xlim = [0, 1.5*max_cx_err]
else:
xlim = [10**np.floor(np.log10(min_cx_err)),
10**np.ceil(np.log10(max_cx_err))]
if text_xval is None:
if scale == 'linear':
text_xval = 0.8*xlim[1]
else:
text_xval = 0.6*xlim[1]
for idx, back in enumerate(backends):
cx_density = gaussian_kde(cx_errors[idx])
xs = np.linspace(xlim[0], xlim[1], 2500)
cx_density.covariance_factor = lambda: covariance_factor
cx_density._compute_covariance()
if scale == 'linear':
plt.plot(xs, 100*cx_density(xs)+offset*idx, zorder=idx, color=colors[idx])
else:
plt.semilogx(xs, 100*cx_density(xs)+offset*idx, zorder=idx, color=colors[idx])
plt.fill_between(xs, offset*idx,
100*cx_density(xs)+offset*idx, zorder=idx, color=colors[idx])
qv_val = back.configuration().quantum_volume
if qv_val:
qv = "(QV"+str(qv_val)+")"
else:
qv = ''
bname = back.name().split('_')[-1].title()+" {}".format(qv)
plt.text(text_xval, offset*idx+0.2*(-offset), bname, fontsize=20, color=colors[idx])
fig.axes[0].get_yaxis().set_visible(False)
# get rid of the frame
for spine in plt.gca().spines.values():
spine.set_visible(False)
if xticks is None:
if scale == 'linear':
xticks = np.round(np.linspace(xlim[0], xlim[1], 4), 2)
else:
xticks = np.asarray(xticks)
if xticks is not None:
plt.xticks(np.floor(xticks), labels=np.floor(xticks), color=text_color)
plt.xticks(fontsize=18)
plt.xlim(xlim)
plt.tick_params(axis='x', colors=text_color)
plt.xlabel('Gate Error', fontsize=18, color=text_color)
plt.title('CNOT Error Distributions', fontsize=18, color=text_color)
fig.tight_layout()
if mpl.get_backend() in ["module://ipykernel.pylab.backend_inline",
"module://matplotlib_inline.backend_inline",
"nbAgg"]:
plt.close(fig)
return fig
|
https://github.com/QuSTaR/kaleidoscope
|
QuSTaR
|
# -*- coding: utf-8 -*-
# This code is part of Kaleidoscope.
#
# (C) Copyright IBM 2020.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""Tests for Bloch routines"""
import pytest
from qiskit import QuantumCircuit
from qiskit. quantum_info import DensityMatrix, partial_trace
from kaleidoscope import qsphere
from kaleidoscope.errors import KaleidoscopeError
def test_qsphere_bad_dm_input():
"""Tests the qsphere raises when passed impure dm"""
qc = QuantumCircuit(3)
qc.h(0)
qc.cx(0, 1)
qc.cx(1, 2)
dm = DensityMatrix.from_instruction(qc)
pdm = partial_trace(dm, [0, 1])
with pytest.raises(KaleidoscopeError):
assert qsphere(pdm)
|
https://github.com/QuSTaR/kaleidoscope
|
QuSTaR
|
# -*- coding: utf-8 -*-
# This code is part of Kaleidoscope.
#
# (C) Copyright IBM 2020.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
# pylint: disable=unused-import
"""Tests for Bloch routines"""
from qiskit import IBMQ, QuantumCircuit
import kaleidoscope.qiskit
PROVIDER = IBMQ.load_account()
backend = PROVIDER.backends.ibmq_vigo
def test_target_backend_attach():
"""Tests attaching target backend"""
qc = QuantumCircuit(5, 5) >> backend
qc.h(0)
qc.cx(0, range(1, 5))
qc.measure(range(5), range(5))
assert qc.target_backend == backend
def test_target_backend_transpile():
"""Tests target backend attached to transpiled circuit"""
qc = QuantumCircuit(5, 5) >> backend
qc.h(0)
qc.cx(0, range(1, 5))
qc.measure(range(5), range(5))
new_qc = qc.transpile()
assert new_qc.target_backend == backend
|
https://github.com/kdk/qiskit-timeline-debugger
|
kdk
|
#initialization
import matplotlib.pyplot as plt
import numpy as np
# importing Qiskit
from qiskit import IBMQ, Aer, assemble, transpile
from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister
from qiskit.providers.ibmq import least_busy
# import basic plot tools
from qiskit.visualization import plot_histogram
n = 2
grover_circuit = QuantumCircuit(n)
def initialize_s(qc, qubits):
"""Apply a H-gate to 'qubits' in qc"""
for q in qubits:
qc.h(q)
return qc
def diffuser(nqubits):
qc = QuantumCircuit(nqubits)
# Apply transformation |s> -> |00..0> (H-gates)
for qubit in range(nqubits):
qc.h(qubit)
# Apply transformation |00..0> -> |11..1> (X-gates)
for qubit in range(nqubits):
qc.x(qubit)
# Do multi-controlled-Z gate
qc.h(nqubits-1)
qc.mct(list(range(nqubits-1)), nqubits-1) # multi-controlled-toffoli
qc.h(nqubits-1)
# Apply transformation |11..1> -> |00..0>
for qubit in range(nqubits):
qc.x(qubit)
# Apply transformation |00..0> -> |s>
for qubit in range(nqubits):
qc.h(qubit)
# We will return the diffuser as a gate
U_s = qc.to_gate()
U_s.name = "U$_s$"
return U_s
grover_circuit = initialize_s(grover_circuit, [0,1])
grover_circuit.draw(output="mpl")
grover_circuit.cz(0,1) # Oracle
grover_circuit.draw(output="mpl")
# Diffusion operator (U_s)
grover_circuit.append(diffuser(n),[0,1])
grover_circuit.draw(output="mpl")
sim = Aer.get_backend('aer_simulator')
# we need to make a copy of the circuit with the 'save_statevector'
# instruction to run on the Aer simulator
grover_circuit_sim = grover_circuit.copy()
grover_circuit_sim.save_statevector()
qobj = assemble(grover_circuit_sim)
result = sim.run(qobj).result()
statevec = result.get_statevector()
from qiskit_textbook.tools import vector2latex
vector2latex(statevec, pretext="|\\psi\\rangle =")
grover_circuit.measure_all()
aer_sim = Aer.get_backend('aer_simulator')
qobj = assemble(grover_circuit)
result = aer_sim.run(qobj).result()
counts = result.get_counts()
plot_histogram(counts)
nqubits = 4
qc = QuantumCircuit(nqubits)
# Apply transformation |s> -> |00..0> (H-gates)
for qubit in range(nqubits):
qc.h(qubit)
# Apply transformation |00..0> -> |11..1> (X-gates)
for qubit in range(nqubits):
qc.x(qubit)
qc.barrier()
# Do multi-controlled-Z gate
qc.h(nqubits-1)
qc.mct(list(range(nqubits-1)), nqubits-1) # multi-controlled-toffoli
qc.h(nqubits-1)
qc.barrier()
# Apply transformation |11..1> -> |00..0>
for qubit in range(nqubits):
qc.x(qubit)
# Apply transformation |00..0> -> |s>
for qubit in range(nqubits):
qc.h(qubit)
qc.draw(output="mpl")
from qiskit.circuit import classical_function, Int1
# define a classical function f(x): this returns 1 for the solutions of the problem
# in this case, the solutions are 1010 and 1100
@classical_function
def f(x1: Int1, x2: Int1, x3: Int1, x4: Int1) -> Int1:
return (x1 and not x2 and x3 and not x4) or (x1 and x2 and not x3 and not x4)
nqubits = 4
Uf = f.synth() # turn it into a circuit
oracle = QuantumCircuit(nqubits+1)
oracle.compose(Uf, inplace=True)
oracle.draw(output="mpl")
# We will return the diffuser as a gate
#U_f = oracle.to_gate()
# U_f.name = "U$_f$"
# return U_f
|
https://github.com/kdk/qiskit-timeline-debugger
|
kdk
|
from qiskit import QuantumCircuit, transpile, __qiskit_version__
from qiskit.providers import BaseBackend
from qiskit.providers.backend import Backend
from qiskit.transpiler.basepasses import AnalysisPass, TransformationPass
from typing import Optional, Union
import logging
import warnings
from IPython.display import display
from qiskit_trebugger.model import TranspilerLoggingHandler
from qiskit_trebugger.model import TranspilerDataCollector
from qiskit_trebugger.model import TranspilationSequence
from qiskit_trebugger.views.widget.timeline_view import TimelineView
from .debugger_error import DebuggerError
class Debugger:
@classmethod
def debug(
cls,
circuit: QuantumCircuit,
backend: Optional[Union[Backend, BaseBackend]] = None,
optimization_level: Optional[int] = None,
**kwargs
):
if not isinstance(circuit, QuantumCircuit):
raise DebuggerError(
"Debugger currently supports single QuantumCircuit only!"
)
# Create the view:
view = TimelineView()
def on_step_callback(step):
view.add_step(step)
# Prepare the model:
transpilation_sequence = TranspilationSequence(on_step_callback)
warnings.simplefilter("ignore")
transpilation_sequence.general_info = {
"Backend": backend.name(),
"optimization_level": optimization_level,
"Qiskit version": __qiskit_version__["qiskit"],
"Terra version": __qiskit_version__["qiskit-terra"],
}
transpilation_sequence.original_circuit = circuit
warnings.simplefilter("default")
Debugger._register_logging_handler(transpilation_sequence)
transpiler_callback = Debugger._get_data_collector(transpilation_sequence)
# Pass the model to the view:
view.transpilation_sequence = transpilation_sequence
view.update_params(**kwargs)
display(view)
transpile(
circuit,
backend,
optimization_level=optimization_level,
callback=transpiler_callback,
**kwargs
)
view.update_summary()
view.add_class("done")
@classmethod
def _register_logging_handler(cls, transpilation_sequence):
# TODO: Do not depend on loggerDict
all_loggers = logging.Logger.manager.loggerDict
passes_loggers = {
key: value
for (key, value) in all_loggers.items()
if key.startswith("qiskit.transpiler.passes.")
}
loggers_map = {}
for _pass in AnalysisPass.__subclasses__():
if _pass.__module__ in passes_loggers.keys():
loggers_map[_pass.__module__] = _pass.__name__
for _pass in TransformationPass.__subclasses__():
if _pass.__module__ in passes_loggers.keys():
loggers_map[_pass.__module__] = _pass.__name__
handler = TranspilerLoggingHandler(
transpilation_sequence=transpilation_sequence, loggers_map=loggers_map
)
logger = logging.getLogger("qiskit.transpiler.passes")
logger.setLevel(logging.DEBUG)
logger.addHandler(handler)
@classmethod
def _get_data_collector(cls, transpilation_sequence):
return TranspilerDataCollector(transpilation_sequence).transpiler_callback
|
https://github.com/kdk/qiskit-timeline-debugger
|
kdk
|
from qiskit import QuantumCircuit
from qiskit.converters import dag_to_circuit, circuit_to_dag
from numpy import zeros, uint16
# make the global DP array
LCS_DP = zeros((2000, 2000), dtype=uint16)
class CircuitComparator:
@staticmethod
def get_moments(dag):
moments = [l["graph"] for l in list(dag.layers())]
return moments
@staticmethod
def make_LCS(moments1, moments2) -> None:
# moments1 : list of lists
# m2 : list of lists
# clear for the base cases of dp
for i in range(2000):
LCS_DP[i][0], LCS_DP[0][i] = 0, 0
n, m = len(moments1), len(moments2)
for i in range(1, n + 1):
for j in range(1, m + 1):
# if the layers are isomorphic then okay
if moments1[i - 1] == moments2[j - 1]:
LCS_DP[i][j] = 1 + LCS_DP[i - 1][j - 1]
else:
LCS_DP[i][j] = max(LCS_DP[i - 1][j], LCS_DP[i][j - 1])
@staticmethod
def compare(prev_circ, curr_circ) -> QuantumCircuit:
if prev_circ is None:
return (False, curr_circ)
# update by reference as there is no qasm now
prev_dag = circuit_to_dag(prev_circ)
curr_dag = circuit_to_dag(curr_circ)
moments1 = CircuitComparator.get_moments(prev_dag)
moments2 = CircuitComparator.get_moments(curr_dag)
CircuitComparator.make_LCS(moments1, moments2)
(n, m) = (len(moments1), len(moments2))
id_set = set()
i = n
j = m
while i > 0 and j > 0:
if moments1[i - 1] == moments2[j - 1]:
# just want diff for second one
id_set.add(j - 1)
i -= 1
j -= 1
else:
if LCS_DP[i - 1][j] > LCS_DP[i][j - 1]:
# means the graph came from the
# first circuit , go up
i -= 1
else:
# if equal or small, go left
j -= 1
# if the whole circuit has not changed
fully_changed = len(id_set) == 0
if not fully_changed:
for id2, l in enumerate(list(curr_dag.layers())):
if id2 not in id_set:
# this is not an LCS node -> highlight it
for node in l["graph"].front_layer():
node.name = node.name + " "
return (fully_changed, dag_to_circuit(curr_dag))
|
https://github.com/kdk/qiskit-timeline-debugger
|
kdk
|
from qiskit import QuantumCircuit
from qiskit.test.mock import FakeCasablanca
from qiskit_trebugger import Debugger
import warnings
warnings.simplefilter('ignore')
debugger = Debugger()
backend = FakeCasablanca()
circuit = QuantumCircuit(2)
circuit.h(0)
circuit.cx(0,1)
circuit.measure_all()
# replace transpile call
debugger.debug(circuit, backend = backend, initial_layout = [0,1])
|
https://github.com/matheusmtta/Quantum-Computing
|
matheusmtta
|
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
from qiskit import IBMQ, BasicAer
from qiskit.providers.ibmq import least_busy
from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister, execute
from qiskit.quantum_info import Statevector
from qiskit.visualization import plot_state_qsphere
from qiskit.visualization import plot_histogram
from qiskit.tools.monitor import job_monitor
from qiskit.tools.visualization import plot_histogram
from IPython.display import display, Math, Latex
#define a 1 qubit state |0>
sv = Statevector.from_label('0')
print('State_0 |x> = ', sv.data)
#build a circuit with the H gate
mycircuit = QuantumCircuit(1)
mycircuit.h(0)
#apply the circuit into |x>
sv = sv.evolve(mycircuit)
print('State_1 H|x> = ', sv.data)
mycircuit.draw('mpl')
'''
OUR FUNCTION F IS DEFINED AS
n = 2
f(0, 0) = 0
f(0, 1) = 1
f(1, 0) = 1
f(1, 1) = 1
'''
#input size
n = 2
#initialize qubits and measurement bits
input_qubits = QuantumRegister(n+1)
output_bits = ClassicalRegister(n)
#create circuit
my_circuit = QuantumCircuit(input_qubits, output_bits)
#apply X on the last qubit
my_circuit.x(n)
#apply Hadamard on all qubits
my_circuit.h(range(n+1))
my_circuit.barrier()
#Apply Uf, these CNOT gates will mark the desired |x_i>
my_circuit.cx(0, 1)
my_circuit.cx(1, 2)
my_circuit.cx(0, 1)
my_circuit.barrier()
#apply Hadamard on all qubits
my_circuit.h(range(n+1))
my_circuit.measure(range(n), range(n))
#Backend classical simulation
backend = BasicAer.get_backend('qasm_simulator')
atp = 1024
res = execute(my_circuit, backend=backend, shots=atp).result()
ans = res.get_counts()
my_circuit.draw(output='mpl')
#Quantum Backend
#SOON#
plot_histogram(ans)
from qiskit import IBMQ
IBMQ.load_account()
provider = IBMQ.get_provider(group='open', project='main')
backend = provider.get_backend('ibmq_vigo')
job = execute(my_circuit, backend=backend)
result = job.result().get_counts()
plot_histogram(result)
|
https://github.com/matheusmtta/Quantum-Computing
|
matheusmtta
|
import numpy as np
# Importing standard Qiskit libraries
from qiskit import QuantumCircuit, transpile, Aer, IBMQ
from qiskit.tools.jupyter import *
from qiskit.visualization import *
from ibm_quantum_widgets import *
from qiskit.providers.aer import QasmSimulator
# Loading your IBM Quantum account(s)
provider = IBMQ.load_account()
from qiskit.circuit.library.standard_gates import XGate, HGate
from operator import *
n = 3
N = 8 #2**n
index_colour_table = {}
colour_hash_map = {}
index_colour_table = {'000':"yellow", '001':"red", '010':"blue", '011':"red", '100':"green", '101':"blue", '110':"orange", '111':"red"}
colour_hash_map = {"yellow":'100', "red":'011', "blue":'000', "green":'001', "orange":'010'}
def database_oracle(index_colour_table, colour_hash_map):
circ_database = QuantumCircuit(n + n)
for i in range(N):
circ_data = QuantumCircuit(n)
idx = bin(i)[2:].zfill(n) # removing the "0b" prefix appended by the bin() funtion
colour = index_colour_table[idx]
colour_hash = colour_hash_map[colour][::-1]
for j in range(n):
if colour_hash[j] == '1':
circ_data.x(j)
# qiskit maps the rightmost bit as the 0th qubit -> qn, ..., q0
# we therefore reverse the index string -> q0, ..., qn
data_gate = circ_data.to_gate(label=colour).control(num_ctrl_qubits=n, ctrl_state=idx, label="index-"+colour)
circ_database.append(data_gate, list(range(n+n)))
return circ_database
# drawing the database oracle circuit
print("Database Encoding")
database_oracle(index_colour_table, colour_hash_map).draw()
circ_data = QuantumCircuit(n)
m = 4
idx = bin(m)[2:].zfill(n) # removing the "0b" prefix appended by the bin() funtion
colour = index_colour_table[idx]
colour_hash = colour_hash_map[colour][::-1]
for j in range(n):
if colour_hash[j] == '1':
circ_data.x(j)
print("Internal colour encoding for the colour green (as an example)");
circ_data.draw()
def oracle_grover(database, data_entry):
circ_grover = QuantumCircuit(n + n + 1)
circ_grover.append(database, list(range(n+n)))
target_reflection_gate = XGate().control(num_ctrl_qubits=n, ctrl_state=colour_hash_map[data_entry], label="Reflection of " + "\"" + data_entry + "\" Target")
# control() missing 1 required positional argument: 'self' .... if only 'XGate' used instead of 'XGate()'
# The “missing 1 required positional argument: 'self'” error is raised when you do not instantiate an object of a class before calling a class method. This error is also raised when you incorrectly instantiate a class.
circ_grover.append(target_reflection_gate, list(range(n, n+n+1)))
circ_grover.append(database, list(range(n+n)))
return circ_grover
print("Grover Oracle (target example: orange)")
oracle_grover(database_oracle(index_colour_table, colour_hash_map).to_gate(label="Database Encoding"), "orange").decompose().draw()
def mcz_gate(num_qubits):
num_controls = num_qubits - 1
mcz_gate = QuantumCircuit(num_qubits)
target_mcz = QuantumCircuit(1)
target_mcz.z(0)
target_mcz = target_mcz.to_gate(label="Z_Gate").control(num_ctrl_qubits=num_controls, ctrl_state=None, label="MCZ")
mcz_gate.append(target_mcz, list(range(num_qubits)))
return mcz_gate.reverse_bits()
print("Multi-controlled Z (MCZ) Gate")
mcz_gate(n).decompose().draw()
def diffusion_operator(num_qubits):
circ_diffusion = QuantumCircuit(num_qubits)
qubits_list = list(range(num_qubits))
# Layer of H^n gates
circ_diffusion.h(qubits_list)
# Layer of X^n gates
circ_diffusion.x(qubits_list)
# Layer of Multi-controlled Z (MCZ) Gate
circ_diffusion = circ_diffusion.compose(mcz_gate(num_qubits), qubits_list)
# Layer of X^n gates
circ_diffusion.x(qubits_list)
# Layer of H^n gates
circ_diffusion.h(qubits_list)
return circ_diffusion
print("Diffusion Circuit")
diffusion_operator(n).draw()
# Putting it all together ... !!!
item = "green"
print("Searching for the index of the colour", item)
circuit = QuantumCircuit(n + n + 1, n)
circuit.x(n + n)
circuit.barrier()
circuit.h(list(range(n)))
circuit.h(n+n)
circuit.barrier()
unitary_oracle = oracle_grover(database_oracle(index_colour_table, colour_hash_map).to_gate(label="Database Encoding"), item).to_gate(label="Oracle Operator")
unitary_diffuser = diffusion_operator(n).to_gate(label="Diffusion Operator")
M = countOf(index_colour_table.values(), item)
Q = int(np.pi * np.sqrt(N/M) / 4)
for i in range(Q):
circuit.append(unitary_oracle, list(range(n + n + 1)))
circuit.append(unitary_diffuser, list(range(n)))
circuit.barrier()
circuit.measure(list(range(n)), list(range(n)))
circuit.draw()
backend_sim = Aer.get_backend('qasm_simulator')
job_sim = backend_sim.run(transpile(circuit, backend_sim), shots=1024)
result_sim = job_sim.result()
counts = result_sim.get_counts(circuit)
if M==1:
print("Index of the colour", item, "is the index with most probable outcome")
else:
print("Indices of the colour", item, "are the indices the most probable outcomes")
from qiskit.visualization import plot_histogram
plot_histogram(counts)
|
https://github.com/matheusmtta/Quantum-Computing
|
matheusmtta
|
import numpy as np
from numpy import pi
# importing Qiskit
from qiskit import QuantumCircuit, transpile, assemble, Aer
from qiskit.visualization import plot_histogram, plot_bloch_multivector
qc = QuantumCircuit(3)
qc.h(2)
qc.cp(pi/2, 1, 2)
qc.cp(pi/4, 0, 2)
qc.h(1)
qc.cp(pi/2, 0, 1)
qc.h(0)
qc.swap(0, 2)
qc.draw()
def qft_rotations(circuit, n):
if n == 0:
return circuit
n -= 1
circuit.h(n)
for qubit in range(n):
circuit.cp(pi/2**(n-qubit), qubit, n)
qft_rotations(circuit, n)
def swap_registers(circuit, n):
for qubit in range(n//2):
circuit.swap(qubit, n-qubit-1)
return circuit
def qft(circuit, n):
qft_rotations(circuit, n)
swap_registers(circuit, n)
return circuit
qc = QuantumCircuit(4)
qft(qc,4)
qc.draw()
# Create the circuit
qc = QuantumCircuit(3)
# Encode the state 5 (101 in binary)
qc.x(0)
qc.x(2)
qc.draw()
sim = Aer.get_backend("aer_simulator")
qc_init = qc.copy()
qc_init.save_statevector()
statevector = sim.run(qc_init).result().get_statevector()
plot_bloch_multivector(statevector)
qft(qc, 3)
qc.draw()
qc.save_statevector()
statevector = sim.run(qc).result().get_statevector()
plot_bloch_multivector(statevector)
|
https://github.com/matheusmtta/Quantum-Computing
|
matheusmtta
|
#initialization
import matplotlib.pyplot as plt
import numpy as np
import math
# importing Qiskit
from qiskit import IBMQ, Aer, transpile, assemble
from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister
# import basic plot tools
from qiskit.visualization import plot_histogram
qpe = QuantumCircuit(4, 3)
qpe.x(3)
#Apply Hadamard gate
for qubit in range(3):
qpe.h(qubit)
qpe.draw()
repetitions = 1
for counting_qubit in range(3):
for i in range(repetitions):
qpe.cp(math.pi/4, counting_qubit, 3); # This is CU # we use 2*pi*(1/theta)
repetitions *= 2
qpe.draw()
def qft_dagger(qc, n):
"""n-qubit QFTdagger the first n qubits in circ"""
# Don't forget the Swaps!
for qubit in range(n//2):
qc.swap(qubit, n-qubit-1)
for j in range(n):
for m in range(j):
qc.cp(-math.pi/float(2**(j-m)), m, j)
qc.h(j)
qpe.barrier()
# Apply inverse QFT
qft_dagger(qpe, 3)
# Measure
qpe.barrier()
for n in range(3):
qpe.measure(n,n)
qpe.draw()
aer_sim = Aer.get_backend('aer_simulator')
shots = 2048
t_qpe = transpile(qpe, aer_sim)
qobj = assemble(t_qpe, shots=shots)
results = aer_sim.run(qobj).result()
answer = results.get_counts()
plot_histogram(answer)
# Create and set up circuit
qpe2 = QuantumCircuit(4, 3)
# Apply H-Gates to counting qubits:
for qubit in range(3):
qpe2.h(qubit)
# Prepare our eigenstate |psi>:
qpe2.x(3)
# Do the controlled-U operations:
angle = 2*math.pi/3
repetitions = 1
for counting_qubit in range(3):
for i in range(repetitions):
qpe2.cp(angle, counting_qubit, 3);
repetitions *= 2
# Do the inverse QFT:
qft_dagger(qpe2, 3)
# Measure of course!
for n in range(3):
qpe2.measure(n,n)
qpe2.draw()
# Let's see the results!
aer_sim = Aer.get_backend('aer_simulator')
shots = 4096
t_qpe2 = transpile(qpe2, aer_sim)
qobj = assemble(t_qpe2, shots=shots)
results = aer_sim.run(qobj).result()
answer = results.get_counts()
plot_histogram(answer)
# Create and set up circuit
qpe3 = QuantumCircuit(6, 5)
# Apply H-Gates to counting qubits:
for qubit in range(5):
qpe3.h(qubit)
# Prepare our eigenstate |psi>:
qpe3.x(5)
# Do the controlled-U operations:
angle = 2*math.pi/3
repetitions = 1
for counting_qubit in range(5):
for i in range(repetitions):
qpe3.cp(angle, counting_qubit, 5);
repetitions *= 2
# Do the inverse QFT:
qft_dagger(qpe3, 5)
# Measure of course!
qpe3.barrier()
for n in range(5):
qpe3.measure(n,n)
qpe3.draw()
# Let's see the results!
aer_sim = Aer.get_backend('aer_simulator')
shots = 4096
t_qpe3 = transpile(qpe3, aer_sim)
qobj = assemble(t_qpe3, shots=shots)
results = aer_sim.run(qobj).result()
answer = results.get_counts()
plot_histogram(answer)
|
https://github.com/matheusmtta/Quantum-Computing
|
matheusmtta
|
from qiskit import QuantumCircuit, Aer, execute, IBMQ
from qiskit.utils import QuantumInstance
import numpy as np
from qiskit.algorithms import Shor
IBMQ.enable_account('ENTER API TOKEN HERE') # Enter your API token here
provider = IBMQ.get_provider(hub='ibm-q')
backend = Aer.get_backend('qasm_simulator')
quantum_instance = QuantumInstance(backend, shots=1000)
my_shor = Shor(quantum_instance)
result_dict = my_shor.factor(15)
print(result_dict)
|
https://github.com/matheusmtta/Quantum-Computing
|
matheusmtta
|
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
from qiskit import *
from qiskit.tools.visualization import plot_histogram
from IPython.display import display, Math, Latex
from qiskit.visualization import plot_state_qsphere
def BuildBell(x, y):
U = QuantumCircuit(2)
U.h(0)
U.cx(0, 1)
if x == 1:
U.cz(0, 1)
if y == 1:
U.x(1)
U = U.to_gate()
U.name = 'Build Bell'
#ctl_U = U.control() make it a controlled gate
return U
backend = BasicAer.get_backend('statevector_simulator')
n = 2
#bell state 00
[x, y] = [0, 0]
b00 = QuantumCircuit(n, n)
b00.append(BuildBell(x, y), range(n))
#bell state 01
[x, y] = [0, 1]
b01 = QuantumCircuit(n, n)
b01.append(BuildBell(x, y), range(n))
#bell state 10
[x, y] = [1, 0]
b10 = QuantumCircuit(n, n)
b10.append(BuildBell(x, y), range(n))
#bell state 11
[x, y] = [1, 1]
b11 = QuantumCircuit(n, n)
b11.append(BuildBell(x, y), range(n))
bqs00 = execute(b00, backend).result()
bqs01 = execute(b01, backend).result()
bqs10 = execute(b10, backend).result()
bqs11 = execute(b11, backend).result()
#GENERAL CIRCUIT b_00
bxy = QuantumCircuit(n, n)
bxy.h(0)
bxy.cx(0, 1)
bxy.measure(range(n), range(n))
backend = BasicAer.get_backend('qasm_simulator')
atp = 1024
res = execute(bxy, backend=backend, shots=atp).result()
ans = res.get_counts()
bxy.draw('mpl')
plot_histogram(ans)
plot_state_qsphere(bqs00.get_statevector(b00)) #bell state 00 qsphere
plot_state_qsphere(bqs01.get_statevector(b01)) #bell state 01 qsphere
plot_state_qsphere(bqs10.get_statevector(b10)) #bell state 10 qsphere
plot_state_qsphere(bqs11.get_statevector(b11)) #bell state 11 qsphere
|
https://github.com/matheusmtta/Quantum-Computing
|
matheusmtta
|
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
from qiskit import *
from qiskit.tools.visualization import plot_histogram
from IPython.display import display, Math, Latex
def BuildGHZ(n):
U = QuantumCircuit(n)
U.h(0)
for i in range(1, n):
U.cx(0, i)
U = U.to_gate()
U.name = 'Build GHZ'
#ctl_U = U.control() make it a controlled gate
return U
n = 5
mc = QuantumCircuit(n, n)
U = BuildGHZ(n)
mc.append(U, range(n))
mc.measure(range(n), range(n))
backend = BasicAer.get_backend('qasm_simulator')
atp = 1024
res = execute(mc, backend=backend, shots=atp).result()
ans = res.get_counts()
mc.draw('mpl')
plot_histogram(ans)
|
https://github.com/matheusmtta/Quantum-Computing
|
matheusmtta
|
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
from qiskit import *
from qiskit.tools.visualization import plot_histogram
from IPython.display import display, Math, Latex
def Decrement(n):
U = QuantumCircuit(n)
control = [x for x in range(n-1)]
for k in range(n-1):
U.x(control)
U.mcx(control, control[-1]+1)
U.x(control)
control.pop()
U.x(0)
U = U.to_gate()
U.name = 'Decrement'
#ctl_U = U.control() make it a controlled gate
return U
n = 3
mc = QuantumCircuit(n, n)
U = Decrement(n)
mc.append(U, range(n))
mc.measure(range(n), range(n))
backend = BasicAer.get_backend('qasm_simulator')
atp = 1024
res = execute(mc, backend=backend, shots=atp).result()
ans = res.get_counts()
mc.draw('mpl')
plot_histogram(ans)
|
https://github.com/matheusmtta/Quantum-Computing
|
matheusmtta
|
from qiskit import BasicAer, execute
from qiskit.circuit import QuantumRegister, ClassicalRegister, QuantumCircuit
from qiskit.circuit.library.standard_gates import PhaseGate
from qiskit.circuit.library.basis_change import QFT
import math
from src.util.util import run_qc
q = QuantumRegister(3)
b = QuantumRegister(1)
c = ClassicalRegister(3)
qc = QuantumCircuit(q, b, c)
qc.x(q[0])
qc.draw(output='mpl')
def increment(circuit, register, apply_QFT=True):
q = register
num = len(q)
qc = circuit
if apply_QFT == True:
qc.barrier()
qc = qc.compose(QFT(num_qubits=num, approximation_degree=0, do_swaps=True, \
inverse=False, insert_barriers=True, name='qft'))
qc.barrier()
for i, qubit in enumerate(q):
qc.rz(math.pi/2**(num-1-i), qubit)
if apply_QFT == True:
qc.barrier()
qc = qc.compose(QFT(num_qubits=num, approximation_degree=0, do_swaps=True, \
inverse=True, insert_barriers=True, name='iqft'))
qc.barrier()
return qc
test = increment(qc, q)
test.draw(output='mpl')
def control_increment(circuit, qregister, cregister, apply_QFT=True):
q = qregister
c = cregister
numq = len(q)
numc = len(c)
qc = circuit
if apply_QFT == True:
qc.barrier()
qc = qc.compose(QFT(num_qubits=numq, approximation_degree=0, do_swaps=True, \
inverse=False, insert_barriers=True, name='qft'))
qc.barrier()
for i, qubit in enumerate(q):
ncp = PhaseGate(math.pi/2**(numq-i-1)).control(numc)
qc.append(ncp, [*c, qubit])
if apply_QFT == True:
qc.barrier()
qc = qc.compose(QFT(num_qubits=numq, approximation_degree=0, do_swaps=True, \
inverse=True, insert_barriers=True, name='iqft'))
qc.barrier()
return qc
test2 = control_increment(qc, q, b)
test2.draw(output='mpl')
## Test increment without control
test = increment(qc, q)
test.measure(q[:], c[:])
run_qc(qc)
test.draw(output='mpl')
# Test control increment
q = QuantumRegister(3)
b = QuantumRegister(1)
c = ClassicalRegister(3)
qc = QuantumCircuit(q, b, c)
qc.x(q[0])
qc = control_increment(qc, q, b)
# Flipping control qubit to 1
qc.x(b[0])
qc = control_increment(qc, q, b)
# Flipping control qubit to 1
qc.x(b[0])
qc = control_increment(qc, q, b)
# Should equal 010
qc.measure(q[:], c[:])
run_qc(qc)
qc.draw(output="mpl")
|
https://github.com/matheusmtta/Quantum-Computing
|
matheusmtta
|
import numpy as np
from numpy import pi
# importing Qiskit
from qiskit import QuantumCircuit, transpile, assemble, Aer
from qiskit.visualization import plot_histogram, plot_bloch_multivector
qc = QuantumCircuit(3)
qc.h(2)
qc.cp(pi/2, 1, 2)
qc.cp(pi/4, 0, 2)
qc.h(1)
qc.cp(pi/2, 0, 1)
qc.h(0)
qc.swap(0, 2)
qc.draw()
def qft_rotations(circuit, n):
if n == 0:
return circuit
n -= 1
circuit.h(n)
for qubit in range(n):
circuit.cp(pi/2**(n-qubit), qubit, n)
qft_rotations(circuit, n)
def swap_registers(circuit, n):
for qubit in range(n//2):
circuit.swap(qubit, n-qubit-1)
return circuit
def qft(circuit, n):
qft_rotations(circuit, n)
swap_registers(circuit, n)
return circuit
qc = QuantumCircuit(4)
qft(qc,4)
qc.draw()
# Create the circuit
qc = QuantumCircuit(3)
# Encode the state 5 (101 in binary)
qc.x(0)
qc.x(2)
qc.draw()
sim = Aer.get_backend("aer_simulator")
qc_init = qc.copy()
qc_init.save_statevector()
statevector = sim.run(qc_init).result().get_statevector()
plot_bloch_multivector(statevector)
qft(qc, 3)
qc.draw()
qc.save_statevector()
statevector = sim.run(qc).result().get_statevector()
plot_bloch_multivector(statevector)
|
https://github.com/matheusmtta/Quantum-Computing
|
matheusmtta
|
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
from qiskit import *
from qiskit.tools.visualization import plot_histogram
from IPython.display import display, Math, Latex
from qiskit.visualization import plot_state_qsphere
def BuildBell(x, y):
U = QuantumCircuit(2)
U.h(0)
U.cx(0, 1)
if x == 1:
U.cz(0, 1)
if y == 1:
U.x(1)
U = U.to_gate()
U.name = 'Build Bell'
#ctl_U = U.control() make it a controlled gate
return U
backend = BasicAer.get_backend('statevector_simulator')
n = 2
#bell state 00
[x, y] = [0, 0]
b00 = QuantumCircuit(n, n)
b00.append(BuildBell(x, y), range(n))
#bell state 01
[x, y] = [0, 1]
b01 = QuantumCircuit(n, n)
b01.append(BuildBell(x, y), range(n))
#bell state 10
[x, y] = [1, 0]
b10 = QuantumCircuit(n, n)
b10.append(BuildBell(x, y), range(n))
#bell state 11
[x, y] = [1, 1]
b11 = QuantumCircuit(n, n)
b11.append(BuildBell(x, y), range(n))
bqs00 = execute(b00, backend).result()
bqs01 = execute(b01, backend).result()
bqs10 = execute(b10, backend).result()
bqs11 = execute(b11, backend).result()
#GENERAL CIRCUIT b_00
bxy = QuantumCircuit(n, n)
bxy.h(0)
bxy.cx(0, 1)
bxy.measure(range(n), range(n))
backend = BasicAer.get_backend('qasm_simulator')
atp = 1024
res = execute(bxy, backend=backend, shots=atp).result()
ans = res.get_counts()
bxy.draw('mpl')
plot_histogram(ans)
plot_state_qsphere(bqs00.get_statevector(b00)) #bell state 00 qsphere
plot_state_qsphere(bqs01.get_statevector(b01)) #bell state 01 qsphere
plot_state_qsphere(bqs10.get_statevector(b10)) #bell state 10 qsphere
plot_state_qsphere(bqs11.get_statevector(b11)) #bell state 11 qsphere
|
https://github.com/matheusmtta/Quantum-Computing
|
matheusmtta
|
open Microsoft.Quantum.Intrinsic;
open Microsoft.Quantum.Canon;
open Microsoft.Quantum.Math;
open Microsoft.Quantum.Diagnostics;
open Microsoft.Quantum.Measurement;
open Microsoft.Quantum.Convert;
open Microsoft.Quantum.Preparation;
open Microsoft.Quantum.Arithmetic;
//If all entries of x are One, the Oracle returns |y> = |1>
operation andOracle (x : Qubit[], y : Qubit) : Unit {
body {
(Controlled X)(x, y);
}
adjoint auto;
}
//If all entries of x are Zero, the Oracle returns |y> = |0>
operation orOracle (x : Qubit[], y : Qubit) : Unit {
body {
ApplyToEachA(X, x);
Controlled X (x, y);
X(y);
ApplyToEachA(X, x);
}
adjoint auto;
}
//Given two qubits, XOR gate if 01 or 10 |y> = |1>
operation xorOracle2 (x : Qubit[], y : Qubit) : Unit {
X(y);
Controlled X(x, y);
X(x[0]);
X(x[1]);
Controlled X(x, y);
X(x[0]);
X(x[1]);
}
//Puts a state |000...0> into the GHZ state |000..0> + |111..1>/sqrt2
operation buildGHZ (qs : Qubit[]) : Unit{
body {
H(qs[0]);
for (i in 1..Length(qs)-1){
CNOT(qs[0], qs[i]);
}
}
adjoint auto;
}
//Puts a state |000...0> into the GHZ state |100..0> + |010..0> + ... |000..1>/sqrtN
operation buildW (qs : Qubit[]) : Unit is Adj + Ctl {
body {
let N = Length(qs);
Ry(2.0*ArcSin(Sqrt(1.0/(IntAsDouble(N)))), qs[0]);
if (N > 1){
((ControlledOnInt)(0, buildW))([qs[0]], qs[1...]);
}
}
adjoint auto;
}
operation isAllZeros (qs : Qubit[]) : Int {
return MeasureInteger(LittleEndian(qs)) == 0 ? 0 | 1;
}
//Generate Bell State 0 : |00>, 1 : |01>, 2 : |10>, 3 |11>
operation buildBell (qs : Qubit[], index : Int) : Unit {
H(qs[0]);
CNOT(qs[0], qs[1]);
if (index%2 == 1){
Z(qs[1]);
}
if (index/2 == 1){
X(qs[0]);
}
}
//Distinguish Bell State 0 : |00>, 1 : |01>, 2 : |10>, 3 : |11>
operation distinguishBell (qs : Qubit[]) : Int {
CNOT(qs[0], qs[1]);
H(qs[0]);
let m1 = M(qs[0]) == One ? 1 | 0;
let m2 = M(qs[1]) == One ? 1 | 0;
return 2*m2 + m1;
}
//Create a copy of a the given state
operation stateCopy (qs : Qubit[], copy : Qubit[]) : Unit {
for (i in 0..Length(qs)-1){
CNOT(qs[i], copy[i]);
}
}
//takes a integer array of 0/1s and creates an equivalent qubit string
operation arrToQbit (bits : Int[], qs : Qubit[]) : Unit {
for (i in 0..Length(qs)-1){
if (bits[i] == 1){
X(qs[i]);
}
}
}
//Teleport 2 qubits + message
operation Teleport (qAlice : Qubit, qBob : Qubit, qMessage : Qubit) : Unit {
H(qAlice);
CNOT(qAlice, qBob);
CNOT(qMessage, qAlice);
H(qMessage);
(Controlled Z)([qMessage], qBob);
(Controlled X)([qAlice], qBob);
}
//Superdensecoding protocol
operation SuperdenseCodingProtocol (b1 : Bool, b2 : Bool) : (Bool, Bool) {
using ((qAlice, qBob) = (Qubit(), Qubit())){
H(qAlice);
CNOT(qAlice, qBob);
//MCMAHON BOOK P236
if (b1){
Z(qAlice);
}
if (b2){
X(qAlice);
}
CNOT(qAlice, qBob);
H(qAlice);
//Measure the qubit at Z basis, and reset it
return ((MResetZ(qAlice) == One, MResetZ(qBob) == One));
}
}
//Destruct state
//Given a superposition state and a integer representing
//the state to be destroied we build a uniform superposition
//with the remaining ones.
operation Destruct(qs : Qubit[], state : Int) : Unit {
using (a = Qubit()) {
repeat {
(ControlledOnInt(state, X)) (qs, a);
let r = MResetZ(a);
}
until (r == Zero)
fixup {
ResetAll(qs);
}
}
}
//Increment, sum two qubits
operation Increment(register : LittleEndian) : Unit is Adj + Ctl {
using (q = Qubit[Length(register!)]){
X(q[0]);
RippleCarryAdderNoCarryTTK(LittleEndian(q), register);
X(q[0]);
}
}
//Decrement,
operation Decrement(register : LittleEndian) : Unit is Adj + Ctl {
using (q = Qubit[Length(register!)]){
ApplyToEachCA(X, q);
RippleCarryAdderNoCarryTTK(LittleEndian(q), register);
ApplyToEachCA(X, q);
}
}
//QUANTUM FOURIER TRANSFORM
operation Reverse(x : Qubit[]) : Unit is Adj+Ctl {
let N = Length(x);
for (i in 0 .. N/2-1) {
SWAP(x[i], x[N-1-i]);
}
}
operation myQFT(x : Qubit[]) : Unit is Adj+Ctl {
let n = Length(x);
for (i in n-1..-1..0) {
H(x[i]);
for (j in i-1..-1..0) {
let theta = (2.0*PI())/(IntAsDouble(1 <<< i-j+1));
Controlled R1([x[j]], (theta, x[i]));
}
}
Reverse (x);
}
|
https://github.com/matheusmtta/Quantum-Computing
|
matheusmtta
|
import numpy as np
from numpy import linalg as LA
from scipy.linalg import expm, sinm, cosm
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import math
from scipy import stats
%matplotlib inline
from IPython.display import Image, display, Math, Latex
sns.set(color_codes=True)
#number of vertices
n = 4
#Define adjacency matrix A_Cn
A = np.zeros((n, n))
for i in range(n):
j1 = (i - 1)%n
j2 = (i + 1)%n
A[i][j1] = 1
A[i][j2] = 1
#Define our initial state Psi_a
psi_a = np.zeros(n)
psi_a[3] = 1
#Define the time t >= 0
t = math.pi/2
#Exponentiate or hamiltonian
U_t = expm(1j*t*A)
U_mt = expm(1j*(-t)*A)
#Compute Psi_t
psi_t = U_t @ psi_a
#Compute the probabilities
prob_t = abs(psi_t)**2
M_t = U_t*U_mt
M_t = np.around(M_t, decimals = 3)
M_t
x = M_t[:, 0].real
plt.bar(range(len(x)), x, tick_label=[0, 1, 2, 3])
plt.xlabel('Vertices')
plt.ylabel('Probability')
|
https://github.com/matheusmtta/Quantum-Computing
|
matheusmtta
|
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
from qiskit import *
from qiskit.providers.ibmq import least_busy
from qiskit.tools.visualization import plot_histogram
from IPython.display import display, Math, Latex
def Increment(size):
U = QuantumCircuit(size)
control = [x for x in range(size-1)]
for k in range(size-1):
U.mcx(control, control[-1]+1)
control.pop()
U.x(0)
U = U.to_gate()
U.name = '--->'
ctl_U = U.control()
return ctl_U
def Decrement(size):
U = QuantumCircuit(size)
control = [x for x in range(size-1)]
for k in range(size-1):
U.x(control)
U.mcx(control, control[-1]+1)
U.x(control)
control.pop()
U.x(0)
U = U.to_gate()
U.name = '<---'
ctl_U = U.control()
return ctl_U
n = 2
steps = 2
graph = QuantumRegister(n+1)
mes = ClassicalRegister(n)
mcq = QuantumCircuit(graph, mes)
#define U(t)
for i in range(steps):
mcq.h(n)
mcq.append(Increment(n), [n]+list(range(0, n)))
mcq.x(n)
mcq.append(Decrement(n), [n]+list(range(0, n)))
mcq.x(n)
mcq.measure(range(n), range(n))
#mcq = transpile(mcq, basis_gates=['cx','u3'],optimization_level=3)
mcq.draw('mpl')
backend = BasicAer.get_backend('qasm_simulator')
atp = 1024
res = execute(mcq, backend=backend, shots=atp).result()
ans = res.get_counts()
plot_histogram(ans)
IBMQ.load_account()
provider = IBMQ.get_provider(group='open', project='main')
backend = provider.get_backend('ibmq_16_melbourne')
job = execute(mcq, backend=backend)
ans_quantum = job.result().get_counts()
legend = ['QASM','ibmq_16_melbourne']
plot_histogram([ans,ans_quantum], legend=legend)
|
https://github.com/matheusmtta/Quantum-Computing
|
matheusmtta
|
import numpy as np
from scipy.linalg import expm, sinm, cosm
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import math
from scipy import stats
%matplotlib inline
from IPython.display import Image, display, Math, Latex
sns.set(color_codes=True)
|
https://github.com/matteoacrossi/oqs-jupyterbook
|
matteoacrossi
|
#############################
# Pauli channel on IBMQX2 #
#############################
from qiskit import QuantumRegister, QuantumCircuit
# Quantum register
q = QuantumRegister(5, name="q")
# Quantum circuit
pauli = QuantumCircuit(q)
# Pauli channel acting on q_2
## Qubit identification
system = 2
a_0 = 3
a_1 = 4
## Define rotation angles
theta_1 = 0.0
theta_2 = 0.0
theta_3 = 0.0
## Construct circuit
pauli.ry(theta_1, q[a_0])
pauli.cx(q[a_0], q[a_1])
pauli.ry(theta_2, q[a_0])
pauli.ry(theta_3, q[a_1])
pauli.cx(q[a_0], q[system])
pauli.cy(q[a_1], q[system])
# Draw circuit
pauli.draw(output='mpl')
####################################
# Depolarizing channel on IBMQX2 #
####################################
# Quantum register
q = QuantumRegister(5, name="q")
# Quantum circuit
depolarizing = QuantumCircuit(q)
# Depolarizing channel acting on q_2
## Qubit identification
system = 2
a_0 = 1
a_1 = 3
a_2 = 4
## Define rotation angles
theta = 0.0
## Construct circuit
depolarizing.ry(theta, q[a_0])
depolarizing.ry(theta, q[a_1])
depolarizing.ry(theta, q[a_2])
depolarizing.cx(q[a_0], q[system])
depolarizing.cy(q[a_1], q[system])
depolarizing.cz(q[a_2], q[system])
# Draw circuit
depolarizing.draw(output='mpl')
#######################
# ZZ pump on IBMQX2 #
#######################
# Quantum register
q = QuantumRegister(5, name='q')
# Quantum circuit
zz = QuantumCircuit(q)
# ZZ pump acting on system qubits
## Qubit identification
system = [2, 1]
a_zz = 0
## Define pump efficiency
## and corresponding rotation
p = 0.5
theta = 2 * np.arcsin(np.sqrt(p))
## Construct circuit
### Map information to ancilla
zz.cx(q[system[0]], q[system[1]])
zz.x(q[a_zz])
zz.cx(q[system[1]], q[a_zz])
### Conditional rotation
zz.cu3(theta, 0.0, 0.0, q[a_zz], q[system[1]])
### Inverse mapping
zz.cx(q[system[1]], q[a_zz])
zz.cx(q[system[0]], q[system[1]])
# Draw circuit
zz.draw(output='mpl')
#######################
# XX pump on IBMQX2 #
#######################
# Quantum register
q = QuantumRegister(5, name='q')
# Quantum circuit
xx = QuantumCircuit(q)
# XX pump acting on system qubits
## Qubit identification
system = [2, 1]
a_xx = 4
## Define pump efficiency
## and corresponding rotation
p = 0.5
theta = 2 * np.arcsin(np.sqrt(p))
## Construct circuit
### Map information to ancilla
xx.cx(q[system[0]], q[system[1]])
xx.h(q[system[0]])
xx.x(q[a_xx])
xx.cx(q[system[0]], q[a_xx])
### Conditional rotation
xx.cu3(theta, 0.0, 0.0, q[a_xx], q[system[0]])
### Inverse mapping
xx.cx(q[system[0]], q[a_xx])
xx.h(q[system[0]])
xx.cx(q[system[0]], q[system[1]])
# Draw circuit
xx.draw(output='mpl')
###########################
# ZZ-XX pumps on IBMQX2 #
###########################
# Quantum register
q = QuantumRegister(5, name='q')
# Quantum circuit
zz_xx = QuantumCircuit(q)
# ZZ and XX pumps acting on system qubits
## Qubit identification
system = [2, 1]
a_zz = 0
a_xx = 4
## Define pump efficiency
## and corresponding rotation
p = 0.5
theta = 2 * np.arcsin(np.sqrt(p))
## Construct circuit
## ZZ pump
### Map information to ancilla
zz_xx.cx(q[system[0]], q[system[1]])
zz_xx.x(q[a_zz])
zz_xx.cx(q[system[1]], q[a_zz])
### Conditional rotation
zz_xx.cu3(theta, 0.0, 0.0, q[a_zz], q[system[1]])
### Inverse mapping
zz_xx.cx(q[system[1]], q[a_zz])
#zz_xx.cx(q[system[0]], q[system[1]])
## XX pump
### Map information to ancilla
#zz_xx.cx(q[system[0]], q[system[1]])
zz_xx.h(q[system[0]])
zz_xx.x(q[a_xx])
zz_xx.cx(q[system[0]], q[a_xx])
### Conditional rotation
zz_xx.cu3(theta, 0.0, 0.0, q[a_xx], q[system[0]])
### Inverse mapping
zz_xx.cx(q[system[0]], q[a_xx])
zz_xx.h(q[system[0]])
zz_xx.cx(q[system[0]], q[system[1]])
# Draw circuit
zz_xx.draw(output='mpl')
|
https://github.com/matteoacrossi/oqs-jupyterbook
|
matteoacrossi
|
from IPython.display import IFrame
IFrame(src='./gen_plots/jaynescummings.html', width=900, height=350)
###########################################
# Amplitude damping channel on IBMQ_VIGO #
###########################################
from qiskit import QuantumRegister, QuantumCircuit
# Quantum register
q = QuantumRegister(5, name='q')
# Quantum circuit
ad = QuantumCircuit(q)
# Amplitude damping channel acting on system qubit
## Qubit identification
system = 1
environment = 2
# Define rotation angle
theta = 0.0
# Construct circuit
ad.x(q[system])
ad.cu3(theta, 0.0, 0.0, q[system], q[environment])
ad.cx(q[environment], q[system])
# Draw circuit
ad.draw(output='mpl')
|
https://github.com/matteoacrossi/oqs-jupyterbook
|
matteoacrossi
|
from IPython.display import IFrame
IFrame(src='./gen_plots/channel_cap.html', width=450, height=500)
from IPython.display import IFrame
IFrame(src='./gen_plots/nonmark_witness.html', width=700, height=350)
#######################################
# Amplitude damping channel #
# with non-M. witness on IBMQ_VIGO #
#######################################
from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit
# Quantum and classical register
q = QuantumRegister(5, name='q')
c = ClassicalRegister(2, name='c')
# Quantum circuit
ad = QuantumCircuit(q, c)
# Amplitude damping channel acting on system qubit
# with non-Markovianity witness
## Qubit identification
system = 1
environment = 2
ancilla = 3
# Define rotation angle
theta = 0.0
# Construct circuit
## Bell state between system and ancilla
ad.h(q[system])
ad.cx(q[system], q[ancilla])
## Channel acting on system qubit
ad.cu3(theta, 0.0, 0.0, q[system], q[environment])
ad.cx(q[environment], q[system])
## Local measurement for the witness
### Choose observable
observable = 'YY'
### Change to the corresponding basis
if observable == 'XX':
ad.h(q[system])
ad.h(q[ancilla])
elif observable == 'YY':
ad.sdg(q[system])
ad.h(q[system])
ad.sdg(q[ancilla])
ad.h(q[ancilla])
### Measure
ad.measure(q[system], c[0])
ad.measure(q[ancilla], c[1])
# Draw circuit
ad.draw(output='mpl')
|
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