repo stringclasses 900
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https://github.com/Qiskit/feedback | Qiskit | import qiskit_alt
qiskit_alt.project.ensure_init(calljulia="juliacall", compile=False)
julia = qiskit_alt.project.julia
Main = julia.Main
julia.Main.zeros(3)
type(julia.Main.zeros(3))
from qiskit_nature.drivers import UnitsType, Molecule
from qiskit_nature.drivers.second_quantization import ElectronicStruc... |
https://github.com/Qiskit/feedback | Qiskit | from qiskit import IBMQ
IBMQ.save_account(token='MY_API_TOKEN')
provider = IBMQ.load_account() # loads saved account from disk
from qiskit_ibm_provider import IBMProvider
IBMProvider.save_account(token='MY_API_TOKEN')
provider = IBMProvider() # loads default saved account from disk
IBMProvider.save_accoun... |
https://github.com/Qiskit/feedback | Qiskit | from qiskit_cold_atom.providers import ColdAtomProvider
provider = ColdAtomProvider()
for backend in provider.backends():
print(backend)
import numpy as np
from qiskit import QuantumCircuit
backend = provider.get_backend("collective_spin_simulator")
circ_x = QuantumCircuit(1)
circ_x.rlx(np.pi/2, 0)
... |
https://github.com/Qiskit/feedback | Qiskit | import os
print(f"Working directory: '{os.getcwd()}'")
from test.python.transpiler.aqc.sample_data import ORIGINAL_CIRCUIT, INITIAL_THETAS
import numpy as np
from scipy.stats import unitary_group
from time import perf_counter
from qiskit import QuantumCircuit
from qiskit.algorithms.optimizers import L_BFGS_B
... |
https://github.com/Qiskit/feedback | Qiskit | from qiskit_nature.problems.sampling.protein_folding.interactions.random_interaction import (
RandomInteraction,
)
from qiskit_nature.problems.sampling.protein_folding.interactions.miyazawa_jernigan_interaction import (
MiyazawaJerniganInteraction,
)
from qiskit_nature.problems.sampling.protein_folding.pe... |
https://github.com/Qiskit/feedback | Qiskit | from qiskit import QuantumCircuit, transpile
from qiskit.opflow import PauliSumOp
from qiskit.circuit.library import PauliEvolutionGate
from qiskit.transpiler import Layout, CouplingMap, PassManager
from qiskit.transpiler.passes import FullAncillaAllocation
from qiskit.transpiler.passes import EnlargeWithAncilla
... |
https://github.com/Qiskit/feedback | Qiskit | from qiskit import Aer
from qiskit.utils import QuantumInstance
from qiskit_nature.algorithms import VQEUCCFactory
from qiskit_nature.algorithms.initial_points.mp2_initial_point import MP2InitialPoint
from qiskit_nature.drivers import Molecule
from qiskit_nature.drivers.second_quantization import (
Electron... |
https://github.com/Qiskit/feedback | Qiskit | import pprint
import numpy as np
import qiskit
from qiskit.transpiler import StagedPassManager, PassManager
from qiskit.transpiler.passes import *
from qiskit.transpiler import CouplingMap
from qiskit.transpiler.preset_passmanagers import common
from qiskit.circuit import QuantumCircuit
# Only available s... |
https://github.com/Qiskit/feedback | Qiskit | # Install qiskit-terra=0.21 to use group_commuting()
# ! pip install qiskit-terra=0.21
from collections import defaultdict
from numbers import Number
from typing import Dict
from collections import defaultdict
import numpy as np
import retworkx as rx
from qiskit.exceptions import QiskitError
from qiski... |
https://github.com/Qiskit/feedback | Qiskit | import sys
from io import BytesIO
from qiskit import pulse, circuit, qpy
my_pulse = pulse.Gaussian(
circuit.Parameter("duration"),
circuit.Parameter("amp"),
circuit.Parameter("sigma"),
)
with pulse.build(name="my_gate") as my_gate:
pulse.shift_phase(1.57, pulse.DriveChannel(0))
pulse.... |
https://github.com/Qiskit/feedback | Qiskit | from qiskit_experiments.library.randomized_benchmarking import RBUtils
# Run old function with num_qubits = 2
num_qubits = 2
t1s = [100 for _ in range(num_qubits)]
t2s = [100 for _ in range(num_qubits)]
gate_length = 5
RBUtils.coherence_limit(
nQ=num_qubits,
T1_list=t1s,
T2_list=t2s,
gatel... |
https://github.com/Qiskit/feedback | Qiskit | from qiskit_nature.drivers.second_quantization import PySCFDriver
from qiskit_nature.transformers.second_quantization.electronic import FreezeCoreTransformer
from qiskit_nature.problems.second_quantization.electronic import ElectronicStructureProblem
driver = PySCFDriver(atom="O 0.0 0.0 0.115; H 0.0 0.754 -0.459; ... |
https://github.com/Qiskit/feedback | Qiskit | %load_ext autoreload
%autoreload 2
import qiskit_metal as metal
from qiskit_metal import designs, draw
from qiskit_metal import MetalGUI, Dict, open_docs
%metal_heading Welcome to Qiskit Metal!
from qiskit_metal.qlibrary.qubits.transmon_pocket_6 import TransmonPocket6
from qiskit_metal.qlibrary.tlines.me... |
https://github.com/Qiskit/feedback | Qiskit | import matplotlib.pyplot as plt
import numpy as np
from IPython.display import clear_output
from qiskit import QuantumCircuit, Aer
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit import ParameterVector
from qiskit.circuit.library import ZFeatureMap
from qiskit.opflow import AerPauliExpectatio... |
https://github.com/Qiskit/feedback | Qiskit | class BaseEstimatorGradient(ABC):
def __init__(
self,
estimator: BaseEstimator,
options: Options | None = None,
):
...
def run(
self,
circuits: Sequence[QuantumCircuit],
observables: Sequence[BaseOperator | PauliSumOp],
param... |
https://github.com/Qiskit/feedback | Qiskit | from qiskit.quantum_info.operators import SparsePauliOp
H2_op = SparsePauliOp(
["II", "IZ", "ZI", "ZZ", "XX"],
coeffs=[
-1.052373245772859,
0.39793742484318045,
-0.39793742484318045,
-0.01128010425623538,
0.18093119978423156,
],
)
aux_op1 = SparsePauliO... |
https://github.com/Qiskit/feedback | Qiskit | from qiskit.utils.deprecation import deprecate_function
from qiskit.utils.deprecation import deprecate_arguments
class DummyClass:
"""This is short description. Let's make it
multiline"""
def __init__(self, arg1: int = None, arg2: [int] = None):
self.arg1 = arg1
self.arg2 = arg2
... |
https://github.com/Qiskit/feedback | Qiskit | import os
from typing import Any, Dict, List, Optional, Union
import numpy as np
import matplotlib.pyplot as plt
from qiskit import IBMQ, QuantumCircuit, QuantumRegister, ClassicalRegister, quantum_info as qi
from qiskit.providers.ibmq import RunnerResult
from qiskit.result import marginal_counts
import qisk... |
https://github.com/Qiskit/feedback | Qiskit | # Step 1: setup
from qiskit import QuantumCircuit
from qiskit_aer import AerSimulator
backend = AerSimulator(method="statevector")
# Step 2: conditional initialisation
qc = QuantumCircuit(1, 2)
qc.h(0) # This is just a stand-in for more complex real-world setup.
qc.measure(0, 0)
# Unlike c_if, we... |
https://github.com/Qiskit/feedback | Qiskit | import qiskit
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister, transpile
from qiskit.transpiler import CouplingMap
from qiskit.converters import circuit_to_dag, dag_to_circuit
from qiskit.visualization.gate_map import plot_gate_map, plot_coupling_map, plot_circuit_layout
import matplotlib.pyp... |
https://github.com/Qiskit/feedback | Qiskit | import matplotlib.pyplot as plt
from qiskit import Aer
from qiskit.algorithms.state_fidelities import ComputeUncompute
from qiskit.circuit.library import ZZFeatureMap
from qiskit.primitives import Sampler
from qiskit.utils import algorithm_globals, QuantumInstance
from sklearn.datasets import make_classification
... |
https://github.com/Qiskit/feedback | Qiskit | %matplotlib inline
import numpy as np
from qiskit_experiments.visualization import (
CurvePlotter,
IQPlotter,
MplDrawer,
PlotStyle,
)
from generate_data import generate_data
data = generate_data()
data_keys = set()
for _, x in data.items():
data_keys.update(x.keys())
print(d... |
https://github.com/Qiskit/feedback | Qiskit | def compose_1q(lhs: Integral, rhs: Integral) -> Integral:
"""Return the composition of 1-qubit clifford integers."""
return _CLIFFORD_COMPOSE_1Q[lhs, rhs]
def compose_2q(lhs: Integral, rhs: Integral) -> Integral:
"""Return the composition of 2-qubit clifford integers."""
num = lhs
for layer,... |
https://github.com/Qiskit/feedback | Qiskit | from qiskit.circuit.random import random_circuit
circuit = random_circuit(2, 2, seed=0).decompose(reps=1)
display(circuit.draw("mpl"))
from qiskit.quantum_info import SparsePauliOp
observable = SparsePauliOp("XZ")
print(f">>> Observable: {observable.paulis}")
from qiskit.primitives import Estimator
est... |
https://github.com/Qiskit/feedback | Qiskit | import os
from qiskit import QuantumCircuit, execute
from qiskit.compiler import transpile
from qiskit.providers.aer import Aer
from qiskit.circuit.random import random_circuit
from rustworkx.visualization import mpl_draw
from qiskit_iqm import IQMProvider
qc_1 = random_circuit(5, 5, measure=True)
qc_2 = ... |
https://github.com/Qiskit/feedback | Qiskit | !git clone --branch qamp-qiskit-demodays https://github.com/qiskit-community/qiskit-qec.git /Users/ruihaoli/qiskit-qec
%cd ../qiskit-qec
!pip install -r requirements.txt
import numpy as np
from qiskit_qec.operators.xp_pauli import XPPauli
from qiskit_qec.operators.xp_pauli_list import XPPauliList
# XPPaul... |
https://github.com/Qiskit/feedback | Qiskit | %load_ext autoreload
%autoreload 2
import warnings
warnings.filterwarnings("ignore")
from qiskit_metal import designs, MetalGUI
from qiskit_metal.qlibrary.sample_shapes.rectangle import Rectangle
from qiskit_metal.qlibrary.qubits.transmon_pocket import TransmonPocket
from qiskit_metal.qlibrary.tlines.straight_... |
https://github.com/Qiskit/feedback | Qiskit | from qiskit import QuantumCircuit
from qiskit.quantum_info import Clifford, random_clifford
from qiskit.synthesis.clifford import synth_clifford_greedy, synth_clifford_layers
clifford = random_clifford(5, seed=0)
print(clifford)
qc = synth_clifford_greedy(clifford)
qc.draw(output='mpl')
qc = synth_clifford... |
https://github.com/Qiskit/feedback | Qiskit | from qiskit.primitives import Estimator
from qiskit.algorithms.gradients import ParamShiftEstimatorGradient
estimator = Estimator()
ps = ParamShiftEstimatorGradient(estimator)
from qiskit.circuit.library import EfficientSU2
from qiskit.quantum_info import SparsePauliOp
def ising_hamiltonian(num_qubits):
... |
https://github.com/Qiskit/feedback | Qiskit | from qiskit_nature.second_q.drivers import PySCFDriver
driver = PySCFDriver()
problem = driver.run()
hamiltonian = problem.hamiltonian.second_q_op()
print(hamiltonian)
from qiskit_nature.second_q.mappers import JordanWignerMapper
mapper = JordanWignerMapper()
print(mapper.map(hamiltonian))
from qisk... |
https://github.com/Qiskit/feedback | Qiskit | from qiskit.circuit import *
qreg, creg = QuantumRegister(5, "q"), ClassicalRegister(2, "c")
body = QuantumCircuit(3, 1)
loop_parameter = Parameter("foo")
indexset = range(0, 10, 2)
body.rx(loop_parameter, [0, 1, 2])
circuit = QuantumCircuit(qreg, creg)
circuit.for_loop(indexset, loop_parameter, body, [1... |
https://github.com/Qiskit/feedback | Qiskit | from qiskit_aer.primitives import Estimator
from qiskit.quantum_info import SparsePauliOp
from qiskit.circuit.library import RealAmplitudes
ansatz = RealAmplitudes(num_qubits=2, reps=2)
observable = SparsePauliOp.from_list([("XX", 1), ("YY", 2), ("ZZ", 3)])
estimator = Estimator()
result = estimator.run(ansat... |
https://github.com/Qiskit/feedback | Qiskit | import qiskit
qiskit.__version__
from qiskit import qasm2
from qiskit.circuit import QuantumCircuit
from qiskit.circuit.random import random_circuit
qc = random_circuit(100, 100)
qasm_string = qc.qasm()
%timeit -n1 qasm2.loads(qasm_string, include_path=qasm2.LEGACY_INCLUDE_PATH, custom_instructions=qasm2.... |
https://github.com/Qiskit/feedback | Qiskit | from qiskit.circuit import Parameter
from qiskit import QuantumCircuit
from qiskit.primitives import Estimator
from qiskit.quantum_info import SparsePauliOp
from qiskit.algorithms import TimeEvolutionProblem
from qiskit.algorithms.time_evolvers import TrotterQRTE
from qiskit.synthesis import LieTrotter
import ... |
https://github.com/Qiskit/feedback | Qiskit | import networkx as nx
from qiskit_optimization.applications import Maxcut
seed = 1
num_nodes = 6
graph = nx.random_regular_graph(d=3, n=num_nodes, seed=seed)
nx.draw(graph, with_labels=True, pos=nx.spring_layout(graph, seed=seed))
maxcut = Maxcut(graph)
problem = maxcut.to_quadratic_program()
print(proble... |
https://github.com/Qiskit/feedback | Qiskit | # pip version of CPLEX is not available for Apple silicon
%pip install cplex
import numpy as np
from qiskit_optimization import QuadraticProgram
from qiskit_optimization.algorithms import GurobiOptimizer
prob = QuadraticProgram()
n = 2001
prob.binary_var_list(n)
prob.minimize(linear=np.ones(n))
print(prob.... |
https://github.com/Qiskit/feedback | Qiskit | from qiskit.circuit import Parameter, QuantumCircuit
x = Parameter('x')
y = Parameter('y')
circuit = QuantumCircuit(2)
circuit.rx(x, 1)
circuit.cx(1, 0)
circuit.ry(-2*y, 0)
circuit.draw('mpl')
from qiskit.quantum_info import Operator
try:
op = Operator(circuit) # not supported in qiskit-terra
e... |
https://github.com/Qiskit/feedback | Qiskit | from numpy import pi
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
qr = QuantumRegister(4, "q")
cr = ClassicalRegister(2, "cr")
qc = QuantumCircuit(qr, cr)
with qc.if_test((cr[1], 1)):
qc.h(0)
qc.cx(0, 1)
qc.draw()
qr = QuantumRegister(4, "q")
cr = ClassicalRegister(2, "c... |
https://github.com/Qiskit/feedback | Qiskit | from qiskit_nature.second_q.operators import BosonicOp, FermionicOp
# Create a creation operator
bosonic_op = BosonicOp({'+_0': 1}, num_modes=1)
# Create an annihilation operator
bosonic_op = BosonicOp({'-_0': 1}, num_modes=1)
bosonic_op1 = BosonicOp({'+_0 -_0 -_1 +_1': 2}, num_modes=2)
bosonic_op2 = BosonicO... |
https://github.com/Qiskit/feedback | Qiskit | import qiskit
qiskit.__version__
from qiskit.circuit.library import XGate
gate = XGate()
print(f"name: {gate.name}\n")
print(f"params: {gate.params}\n")
print(f"matrix:\n{gate.to_matrix()}\n")
print(f"definition:\n{gate.definition}\n")
new_gate = XGate()
print(f"name: {gate.name}\n")
print(f"params: {gate... |
https://github.com/Qiskit/feedback | Qiskit | # Cell 1: simple representation
from qiskit import QuantumCircuit
from qiskit.circuit import QuantumRegister, ClassicalRegister, Clbit
from qiskit.circuit.classical import expr
cr1 = ClassicalRegister(3, "cr1")
cr2 = ClassicalRegister(3, "cr2")
expr.equal(expr.bit_and(cr1, cr2), 5)
# Cell 2: where can ... |
https://github.com/Qiskit/feedback | Qiskit | from qiskit.circuit import QuantumCircuit, QuantumRegister, ClassicalRegister
from qiskit.compiler import transpile
from qiskit.circuit.random import random_circuit
from qiskit_aer import AerSimulator
qc = random_circuit(16, 3, measure=True)
backend = AerSimulator()
t_qc = transpile(qc, backend, optimization_... |
https://github.com/Qiskit/feedback | Qiskit | import qiskit
qiskit.__version__
from qiskit.circuit import QuantumCircuit, QuantumRegister, ClassicalRegister
from qiskit.compiler import transpile
qreg = QuantumRegister(2)
creg = ClassicalRegister(2)
qc = QuantumCircuit(qreg, creg)
qc.h([0, 1])
qc.h([0, 1])
qc.h([0, 1])
qc.measure([0, 1], [0, 1])
... |
https://github.com/Qiskit/feedback | Qiskit | from qiskit import __qiskit_version__
print(__qiskit_version__)
from qiskit.tools.jupyter import *
%qiskit_version_table
from qiskit import __version__
print(__version__)
from qiskit.version import get_version_info |
https://github.com/Qiskit/feedback | Qiskit | import numpy as np
from IPython.display import Latex
from qiskit import QuantumCircuit
from qiskit.quantum_info import Statevector, schmidt_decomposition
ζ = Statevector([1/np.sqrt(2),0,0,1/np.sqrt(2)])
ζ.draw('latex',prefix='|\\zeta\\rangle = ')
ζ_sd = schmidt_decomposition(ζ,[0])
print(ζ_sd)
for i, (s,u... |
https://github.com/Qiskit/feedback | Qiskit | # imports
from qiskit.circuit import QuantumCircuit
from qiskit.circuit.library import PermutationGate
from qiskit.transpiler.passes.synthesis.high_level_synthesis import (
HighLevelSynthesis,
HighLevelSynthesisPluginManager,
HighLevelSynthesisPlugin,
HLSConfig,
)
from qiskit.compiler i... |
https://github.com/Qiskit/feedback | Qiskit | import numpy as np
import qiskit
from qiskit import QuantumCircuit
from qiskit import transpile
from qiskit.providers.fake_provider import FakeManhattanV2
from qiskit.circuit.library import *
from qiskit.synthesis import *
from qiskit.quantum_info import *
from qiskit.synthesis.linear import random_invertible... |
https://github.com/Qiskit/feedback | Qiskit | import qiskit
qiskit.__version__
from qiskit.circuit.library import XGate, CZGate, RZGate
from qiskit.circuit import QuantumCircuit
XGate() is XGate() is XGate() is XGate() is XGate() is XGate() is XGate() is XGate() is XGate()
CZGate() is CZGate() is CZGate() is CZGate() is CZGate() is CZGate() is CZGate() ... |
https://github.com/Qiskit/feedback | Qiskit | !pip install pyRiemann-qiskit
!pip install moabb
from pyriemann_qiskit.pipelines import (
QuantumMDMWithRiemannianPipeline,
)
from moabb.datasets import BI2012
from moabb.paradigms import P300
from sklearn.model_selection import train_test_split
from sklearn.metrics import balanced_accuracy_score
from sk... |
https://github.com/tigerjack/qiskit_grover | tigerjack | from math import sqrt, pi
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
import oracle_simple
import composed_gates
def get_circuit(n, oracles):
"""
Build the circuit composed by the oracle black box and the other quantum gates.
:param n: The number of qubits (not including... |
https://github.com/tigerjack/qiskit_grover | tigerjack | circuit = None
oracle_simple = None
execute = None
Aer = None
IBMQ = None
least_busy = None
def _import_modules():
print("Importing modules")
global circuit, oracle_simple, execute, least_busy
import circuit
import oracle_simple
from qiskit import execute
from qiskit.backends.ibm... |
https://github.com/tigerjack/qiskit_grover | tigerjack | from qiskit import IBMQ
import sys
def get_job_status(backend, job_id):
backend = IBMQ.get_backend(backend)
print("Backend {0} is operational? {1}".format(
backend.name(),
backend.status()['operational']))
print("Backend was last updated in {0}".format(
backend.properties... |
https://github.com/Slimane33/QuantumClassifier | Slimane33 | import numpy as np
from qiskit import *
from qiskit.tools.jupyter import *
import matplotlib.pyplot as plt
from scipy.optimize import minimize
from sklearn.preprocessing import Normalizer
backend = BasicAer.get_backend('qasm_simulator')
def get_angles(x):
beta0 = 2 * np.arcsin(np.sqrt(x[1]) ** 2 / n... |
https://github.com/HQSquantumsimulations/qoqo-qiskit | HQSquantumsimulations | from qiskit import Aer, IBMQ, execute
from qiskit import transpile, assemble
from qiskit.tools.monitor import job_monitor
from qiskit.tools.monitor import job_monitor
import matplotlib.pyplot as plt
from qiskit.visualization import plot_histogram, plot_state_city
"""
Qiskit backends to execute the quantum circ... |
https://github.com/HQSquantumsimulations/qoqo-qiskit | HQSquantumsimulations | # Copyright 2022-2023 Ohad Lev.
# 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,
# or in the root directory of this package("LICE... |
https://github.com/HQSquantumsimulations/qoqo-qiskit | HQSquantumsimulations | # Copyright © 2023 HQS Quantum Simulations GmbH.
#
# 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 ... |
https://github.com/DRA-chaos/Quantum-Classical-Hyrid-Neural-Network-for-binary-image-classification-using-PyTorch-Qiskit-pipeline | DRA-chaos | !pip install qiskit
# check if CUDA is available
import torch
train_on_gpu = torch.cuda.is_available()
if not train_on_gpu:
print('CUDA is not available. Training on CPU ...')
else:
print('CUDA is available! Training on GPU ...')
import numpy as np
import matplotlib.pyplot as plt
import to... |
https://github.com/DRA-chaos/Quantum-Classical-Hyrid-Neural-Network-for-binary-image-classification-using-PyTorch-Qiskit-pipeline | DRA-chaos | !pip install qiskit
import numpy as np
import matplotlib.pyplot as plt
import torch
from torch.autograd import Function
from torchvision import datasets, transforms
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import qiskit
from qiskit import transpile, assemble
fro... |
https://github.com/DRA-chaos/Quantum-Classical-Hyrid-Neural-Network-for-binary-image-classification-using-PyTorch-Qiskit-pipeline | DRA-chaos | !pip install qiskit
import numpy as np
import matplotlib.pyplot as plt
import torch
from torch.autograd import Function
from torchvision import datasets, transforms
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import qiskit
from qiskit import transpile, assemble
fro... |
https://github.com/DRA-chaos/Quantum-Classical-Hyrid-Neural-Network-for-binary-image-classification-using-PyTorch-Qiskit-pipeline | DRA-chaos | !pip install qiskit
import numpy as np
import matplotlib.pyplot as plt
import torch
from torch.autograd import Function
from torchvision import datasets, transforms
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import qiskit
from qiskit import transpile, assemble
fro... |
https://github.com/DRA-chaos/Quantum-Classical-Hyrid-Neural-Network-for-binary-image-classification-using-PyTorch-Qiskit-pipeline | DRA-chaos | !pip install qiskit
import numpy as np
import matplotlib.pyplot as plt
import torch
from torch.autograd import Function
from torchvision import datasets, transforms
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import qiskit
from qiskit import transpile, assemble
fro... |
https://github.com/DRA-chaos/Quantum-Classical-Hyrid-Neural-Network-for-binary-image-classification-using-PyTorch-Qiskit-pipeline | DRA-chaos | !pip install qiskit
import numpy as np
import matplotlib.pyplot as plt
import torch
from torch.autograd import Function
from torchvision import datasets, transforms
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import qiskit
from qiskit import transpile, assemble
fro... |
https://github.com/DRA-chaos/Quantum-Classical-Hyrid-Neural-Network-for-binary-image-classification-using-PyTorch-Qiskit-pipeline | DRA-chaos | !pip install qiskit
import numpy as np
import matplotlib.pyplot as plt
import torch
from torch.autograd import Function
from torchvision import datasets, transforms
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import qiskit
from qiskit import transpile, assemble
fro... |
https://github.com/DRA-chaos/Quantum-Classical-Hyrid-Neural-Network-for-binary-image-classification-using-PyTorch-Qiskit-pipeline | DRA-chaos | !pip install qiskit
import numpy as np
import matplotlib.pyplot as plt
import torch
from torch.autograd import Function
from torchvision import datasets, transforms
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import qiskit
from qiskit import transpile, assemble
fro... |
https://github.com/DRA-chaos/Quantum-Classical-Hyrid-Neural-Network-for-binary-image-classification-using-PyTorch-Qiskit-pipeline | DRA-chaos | !pip install qiskit
import numpy as np
import matplotlib.pyplot as plt
import torch
from torch.autograd import Function
from torchvision import datasets, transforms
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import qiskit
from qiskit import transpile, assemble
fro... |
https://github.com/DRA-chaos/Quantum-Classical-Hyrid-Neural-Network-for-binary-image-classification-using-PyTorch-Qiskit-pipeline | DRA-chaos | !pip install qiskit
import numpy as np
import matplotlib.pyplot as plt
import torch
from torch.autograd import Function
from torchvision import datasets, transforms
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import qiskit
from qiskit import transpile, assemble
fro... |
https://github.com/DRA-chaos/Quantum-Classical-Hyrid-Neural-Network-for-binary-image-classification-using-PyTorch-Qiskit-pipeline | DRA-chaos | !pip install qiskit
# check if CUDA is available
import torch
train_on_gpu = torch.cuda.is_available()
if not train_on_gpu:
print('CUDA is not available. Training on CPU ...')
else:
print('CUDA is available! Training on GPU ...')
import numpy as np
import matplotlib.pyplot as plt
import to... |
https://github.com/DRA-chaos/Quantum-Classical-Hyrid-Neural-Network-for-binary-image-classification-using-PyTorch-Qiskit-pipeline | DRA-chaos | !pip install qiskit
# check if CUDA is available
import torch
train_on_gpu = torch.cuda.is_available()
if not train_on_gpu:
print('CUDA is not available. Training on CPU ...')
else:
print('CUDA is available! Training on GPU ...')
import numpy as np
import matplotlib.pyplot as plt
import to... |
https://github.com/DRA-chaos/Quantum-Classical-Hyrid-Neural-Network-for-binary-image-classification-using-PyTorch-Qiskit-pipeline | DRA-chaos | pip install qiskit
import numpy as np
import torch
from torch.autograd import Function
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from torchvision import datasets, transforms
from qiskit import QuantumRegister, QuantumCircuit, ClassicalRegister, execu... |
https://github.com/DRA-chaos/Quantum-Classical-Hyrid-Neural-Network-for-binary-image-classification-using-PyTorch-Qiskit-pipeline | DRA-chaos | !pip install qiskit
# check if CUDA is available
import torch
train_on_gpu = torch.cuda.is_available()
if not train_on_gpu:
print('CUDA is not available. Training on CPU ...')
else:
print('CUDA is available! Training on GPU ...')
import numpy as np
import matplotlib.pyplot as plt
import to... |
https://github.com/DRA-chaos/Quantum-Classical-Hyrid-Neural-Network-for-binary-image-classification-using-PyTorch-Qiskit-pipeline | DRA-chaos | !pip install qiskit
import numpy as np
import torch
from torch.autograd import Function
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from torchvision import datasets, transforms
from qiskit import QuantumRegister, QuantumCircuit, ClassicalRegister, exec... |
https://github.com/DRA-chaos/Quantum-Classical-Hyrid-Neural-Network-for-binary-image-classification-using-PyTorch-Qiskit-pipeline | DRA-chaos | !pip install qiskit
import numpy as np
import matplotlib.pyplot as plt
import torch
from torch.autograd import Function
from torchvision import datasets, transforms
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import qiskit
from qiskit import transpile, assemble
fro... |
https://github.com/DRA-chaos/Quantum-Classical-Hyrid-Neural-Network-for-binary-image-classification-using-PyTorch-Qiskit-pipeline | DRA-chaos | !pip install qiskit
# check if CUDA is available
import torch
train_on_gpu = torch.cuda.is_available()
if not train_on_gpu:
print('CUDA is not available. Training on CPU ...')
else:
print('CUDA is available! Training on GPU ...')
import numpy as np
import matplotlib.pyplot as plt
import to... |
https://github.com/DRA-chaos/Quantum-Classical-Hyrid-Neural-Network-for-binary-image-classification-using-PyTorch-Qiskit-pipeline | DRA-chaos | !pip install qiskit
# check if CUDA is available
import torch
train_on_gpu = torch.cuda.is_available()
if not train_on_gpu:
print('CUDA is not available. Training on CPU ...')
else:
print('CUDA is available! Training on GPU ...')
import numpy as np
import matplotlib.pyplot as plt
import to... |
https://github.com/DRA-chaos/Quantum-Classical-Hyrid-Neural-Network-for-binary-image-classification-using-PyTorch-Qiskit-pipeline | DRA-chaos | !pip install qiskit
# check if CUDA is available
import torch
train_on_gpu = torch.cuda.is_available()
if not train_on_gpu:
print('CUDA is not available. Training on CPU ...')
else:
print('CUDA is available! Training on GPU ...')
import numpy as np
import matplotlib.pyplot as plt
import to... |
https://github.com/DRA-chaos/Quantum-Classical-Hyrid-Neural-Network-for-binary-image-classification-using-PyTorch-Qiskit-pipeline | DRA-chaos | !pip install qiskit
# check if CUDA is available
import torch
train_on_gpu = torch.cuda.is_available()
if not train_on_gpu:
print('CUDA is not available. Training on CPU ...')
else:
print('CUDA is available! Training on GPU ...')
import numpy as np
import matplotlib.pyplot as plt
import to... |
https://github.com/quantum-melbourne/qiskit-challenge-22 | quantum-melbourne | #Let us begin by importing necessary libraries.
from qiskit import Aer
from qiskit.algorithms import VQE, QAOA, NumPyMinimumEigensolver
from qiskit.algorithms.optimizers import *
from qiskit.circuit.library import TwoLocal
from qiskit.utils import QuantumInstance
from qiskit.utils import algorithm_globals
from q... |
https://github.com/quantum-melbourne/qiskit-challenge-22 | quantum-melbourne | from qiskit_nature.drivers import Molecule
from qiskit_nature.drivers.second_quantization import ElectronicStructureDriverType, ElectronicStructureMoleculeDriver
# PSPCz molecule
geometry = [['C', [ -0.2316640, 1.1348450, 0.6956120]],
['C', [ -0.8886300, 0.3253780, -0.2344140]],
... |
https://github.com/quantum-melbourne/qiskit-challenge-22 | quantum-melbourne | # General imports
import os
import gzip
import numpy as np
import matplotlib.pyplot as plt
from pylab import cm
import warnings
warnings.filterwarnings("ignore")
# scikit-learn imports
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import Stan... |
https://github.com/quantum-melbourne/qiskit-challenge-22 | quantum-melbourne | from qiskit_optimization.algorithms import MinimumEigenOptimizer
from qiskit import Aer
from qiskit.utils import algorithm_globals, QuantumInstance
from qiskit.algorithms import QAOA, NumPyMinimumEigensolver
import numpy as np
val = [5,6,7,8,9]
wt = [4,5,6,7,8]
W = 18
def dp(W, wt, val, n):
k = [[0 for... |
https://github.com/quantum-melbourne/qiskit-challenge-22 | quantum-melbourne | #Let us first import necessary libraries
from qiskit import Aer
from qiskit.algorithms import VQE, QAOA, NumPyMinimumEigensolver
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit.library import TwoLocal
from qiskit.utils import QuantumInstance
from qiskit_finance.applications.optimization import... |
https://github.com/quantum-melbourne/qiskit-challenge-22 | quantum-melbourne | import matplotlib.pyplot as plt
import numpy as np
from qiskit.utils import algorithm_globals
seed = 12345
algorithm_globals.random_seed = seed
from qiskit_machine_learning.datasets import ad_hoc_data
train_features, train_labels, test_features, test_labels, adhoc_total = ad_hoc_data(
training_size=2... |
https://github.com/quantum-melbourne/qiskit-challenge-22 | quantum-melbourne | import warnings
from h5py.h5py_warnings import H5pyDeprecationWarning
warnings.filterwarnings(action="ignore", category=H5pyDeprecationWarning)
from qiskit_nature.drivers import Molecule
molecule = Molecule(
# coordinates are given in Angstrom
geometry=[
["O", [0.0, 0.0, 0.115]],
["H... |
https://github.com/quantum-melbourne/qiskit-challenge-22 | quantum-melbourne | from qiskit_optimization import QuadraticProgram
# Define QuadraticProgram
qp = QuadraticProgram()
# Add variables
qp.binary_var('x')
qp.binary_var('y')
qp.integer_var(lowerbound=0, upperbound=7, name='z')
# Add an objective function
qp.maximize(linear={'x': 2, 'y': 1, 'z': 1})
# Add a constraint
qp.linear_c... |
https://github.com/entropicalabs/OpenQAOA-Challenge---Qiskit-Fall-Fest-2022-Mexico | entropicalabs | from openqaoa.problems.problem import NumberPartition
np = NumberPartition(numbers=[1,2,3])
np_qubo = np.get_qubo_problem()
# visualize the QUBO form on a graph
from openqaoa.utilities import plot_graph, graph_from_hamiltonian
#extract Hamiltonain
cost_hamil = np_qubo.hamiltonian
#convert Hamiltonian to gr... |
https://github.com/entropicalabs/OpenQAOA-Challenge---Qiskit-Fall-Fest-2022-Mexico | entropicalabs | %load_ext autoreload
%autoreload 2
from IPython.display import clear_output
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import numpy as np
import networkx as nx
# Design the graph for the maxcut problem
g = nx.Graph()
g.add_edge(0, 1)
g.add_edge(0, 2)
g.add_edge(1, 3)
g.add_edg... |
https://github.com/entropicalabs/OpenQAOA-Challenge---Qiskit-Fall-Fest-2022-Mexico | entropicalabs | # Methods to use in the notebook
%load_ext autoreload
%autoreload 2
from IPython.display import clear_output
# Libaries to manipulate, print, plot, and save the data.
import matplotlib.pyplot as plt
import numpy as np
# Random generator for the problem
from random import seed,randrange, randint
# OpenQA... |
https://github.com/entropicalabs/OpenQAOA-Challenge---Qiskit-Fall-Fest-2022-Mexico | entropicalabs | # Methods to use in the notebook
%load_ext autoreload
%autoreload 2
from IPython.display import clear_output
# Libaries to manipulate, print, plot, and save the data.
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# Random generator for the problem
from random import seed,randrange... |
https://github.com/QuCO-CSAM/Solving-Combinatorial-Optimisation-Problems-Using-Quantum-Algorithms | QuCO-CSAM | #In case you don't have qiskit, install it now
%pip install qiskit --quiet
#Installing/upgrading pylatexenc seems to have fixed my mpl issue
#If you try this and it doesn't work, try also restarting the runtime/kernel
%pip install pylatexenc --quiet
!pip install -Uqq ipdb
!pip install qiskit_optimization
imp... |
https://github.com/QuCO-CSAM/Solving-Combinatorial-Optimisation-Problems-Using-Quantum-Algorithms | QuCO-CSAM | #Assign these values as per your requirements.
global min_qubits,max_qubits,skip_qubits,max_circuits,num_shots,Noise_Inclusion
min_qubits=4
max_qubits=15 #reference files are upto 12 Qubits only
skip_qubits=2
max_circuits=3
num_shots=4092
gate_counts_plots = True
Noise_Inclusion = False
saveplots = False... |
https://github.com/QuCO-CSAM/Solving-Combinatorial-Optimisation-Problems-Using-Quantum-Algorithms | QuCO-CSAM | import numpy as np
from itertools import permutations
import gzip
from qiskit import*
import time
from qiskit.aqua.algorithms import VQE
from qiskit.aqua.algorithms import QAOA
from qiskit.aqua.components.optimizers import SPSA
# from qiskit.aqua.components.variational_forms import RY
from qiskit.aqu... |
https://github.com/QuCO-CSAM/Solving-Combinatorial-Optimisation-Problems-Using-Quantum-Algorithms | QuCO-CSAM | # Important libraries and modules
import numpy as np
from itertools import permutations
import gzip
from qiskit import*
import time
from qiskit.aqua.algorithms import VQE
from qiskit.aqua.algorithms import QAOA
from qiskit.aqua.components.optimizers import SPSA
from qiskit.aqua import QuantumInst... |
https://github.com/QuCO-CSAM/Solving-Combinatorial-Optimisation-Problems-Using-Quantum-Algorithms | QuCO-CSAM | import numpy as np
from itertools import permutations
import gzip
from qiskit import*
import time
from qiskit.aqua.algorithms import VQE
from qiskit.aqua.algorithms import QAOA
from qiskit.aqua.components.optimizers import SPSA
# from qiskit.aqua.components.variational_forms import RY
from qiskit.aqu... |
https://github.com/QuCO-CSAM/Solving-Combinatorial-Optimisation-Problems-Using-Quantum-Algorithms | QuCO-CSAM | # Important libraries and modules
import numpy as np
from itertools import permutations
import gzip
from qiskit import*
import time
from qiskit.aqua.algorithms import VQE
from qiskit.aqua.algorithms import QAOA
from qiskit.aqua.components.optimizers import SPSA
from qiskit.aqua import QuantumInst... |
https://github.com/MuhammadMiqdadKhan/Solution-of-IBM-s-Global-Quantum-Challenge-2020 | MuhammadMiqdadKhan | # Cell 1
import numpy as np
from qiskit import Aer, QuantumCircuit, execute
from qiskit.visualization import plot_histogram
from IPython.display import display, Math, Latex
from may4_challenge import plot_state_qsphere
from may4_challenge.ex1 import minicomposer
from may4_challenge.ex1 import check1, check2,... |
https://github.com/MuhammadMiqdadKhan/Solution-of-IBM-s-Global-Quantum-Challenge-2020 | MuhammadMiqdadKhan | #initialization
%matplotlib inline
# Importing standard Qiskit libraries and configuring account
from qiskit import IBMQ
from qiskit.compiler import transpile, assemble
from qiskit.providers.ibmq import least_busy
from qiskit.tools.jupyter import *
from qiskit.tools.monitor import job_monitor
from qiskit.visu... |
https://github.com/MuhammadMiqdadKhan/Solution-of-IBM-s-Global-Quantum-Challenge-2020 | MuhammadMiqdadKhan | %matplotlib inline
# Importing standard Qiskit libraries
import random
from qiskit import execute, Aer, IBMQ
from qiskit.tools.jupyter import *
from qiskit.visualization import *
from may4_challenge.ex3 import alice_prepare_qubit, check_bits, check_key, check_decrypted, show_message
# Configuring account
pr... |
https://github.com/MuhammadMiqdadKhan/Solution-of-IBM-s-Global-Quantum-Challenge-2020 | MuhammadMiqdadKhan | from may4_challenge.ex4 import get_unitary
U = get_unitary()
from may4_challenge.ex4 import get_unitary
from qiskit import QuantumCircuit, transpile, execute, BasicAer, extensions
from qiskit.visualization import *
from qiskit import QuantumCircuit
from may4_challenge.ex4 import check_circuit, submit_circuit
imp... |
https://github.com/ctuning/ck-qiskit | ctuning | import numpy as np
import IPython
import ipywidgets as widgets
import colorsys
import matplotlib.pyplot as plt
from qiskit import QuantumCircuit,QuantumRegister,ClassicalRegister
from qiskit import execute, Aer, BasicAer
from qiskit.visualization import plot_bloch_multivector
from qiskit.tools.jupyter import *
... |
https://github.com/ctuning/ck-qiskit | ctuning | # 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 deriv... |
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