repo stringclasses 900
values | file stringclasses 754
values | content stringlengths 4 215k |
|---|---|---|
https://github.com/2lambda123/Qiskit-qiskit | 2lambda123 | # 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... |
https://github.com/2lambda123/Qiskit-qiskit | 2lambda123 | # This code is part of Qiskit.
#
# (C) Copyright IBM 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 deriv... |
https://github.com/2lambda123/Qiskit-qiskit | 2lambda123 | # This code is part of Qiskit.
#
# (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 deriv... |
https://github.com/2lambda123/Qiskit-qiskit | 2lambda123 | # This code is part of Qiskit.
#
# (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 deriv... |
https://github.com/2lambda123/Qiskit-qiskit | 2lambda123 | # This code is part of Qiskit.
#
# (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 deriv... |
https://github.com/2lambda123/Qiskit-qiskit | 2lambda123 | # This code is part of Qiskit.
#
# (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 deriv... |
https://github.com/2lambda123/Qiskit-qiskit | 2lambda123 | # This code is part of Qiskit.
#
# (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 deriv... |
https://github.com/2lambda123/Qiskit-qiskit | 2lambda123 | # This code is part of Qiskit.
#
# (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 deriv... |
https://github.com/2lambda123/Qiskit-qiskit | 2lambda123 | # This code is part of Qiskit.
#
# (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 deriv... |
https://github.com/2lambda123/Qiskit-qiskit | 2lambda123 | # This code is part of Qiskit.
#
# (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 deriv... |
https://github.com/2lambda123/Qiskit-qiskit | 2lambda123 | # This code is part of Qiskit.
#
# (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 deriv... |
https://github.com/2lambda123/Qiskit-qiskit | 2lambda123 | # This code is part of Qiskit.
#
# (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 deriv... |
https://github.com/2lambda123/Qiskit-qiskit | 2lambda123 | #!/usr/bin/env python3
# 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.
#
# An... |
https://github.com/2lambda123/Qiskit-qiskit | 2lambda123 | # 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... |
https://github.com/2lambda123/Qiskit-qiskit | 2lambda123 | # Testing Circuits
import warnings
warnings.simplefilter("ignore")
%run "mpl/circuit/test_circuit_matplotlib_drawer.py"
# Testing Graphs
%run "mpl/graph/test_graph_matplotlib_drawer.py"
%run -i "results.py"
RESULTS_CIRCUIT
RESULTS_GRAPH |
https://github.com/2lambda123/Qiskit-qiskit | 2lambda123 | # This code is part of Qiskit.
#
# (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 deriv... |
https://github.com/2lambda123/Qiskit-qiskit | 2lambda123 | # This code is part of Qiskit.
#
# (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 deriv... |
https://github.com/2lambda123/Qiskit-qiskit | 2lambda123 | # This code is part of Qiskit.
#
# (C) Copyright IBM 2017, 2023.
#
# 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... |
https://github.com/2lambda123/Qiskit-qiskit | 2lambda123 | #!/usr/bin/env python3
# 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... |
https://github.com/miamico/Quantum_Generative_Adversarial_Networks | miamico | """Simple example on how to log scalars and images to tensorboard without tensor ops.
License: Copyleft
"""
__author__ = "Michael Gygli"
import tensorflow as tf
# from StringIO import StringIO
import matplotlib.pyplot as plt
import numpy as np
class Logger(object):
"""Logging in tensorboard without t... |
https://github.com/miamico/Quantum_Generative_Adversarial_Networks | miamico | %%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... |
https://github.com/shantomborah/Quantum-Algorithms | shantomborah | import numpy as np
import matplotlib.pyplot as plt
from qiskit import QuantumCircuit, QuantumRegister
from qiskit.extensions import HamiltonianGate
from qiskit.circuit.library.standard_gates import CRYGate
def quantum_fourier_transform(t, vis=False):
reg = QuantumRegister(t, name='c')
circ = Quantu... |
https://github.com/shantomborah/Quantum-Algorithms | shantomborah | import numpy as np
import hhl_components as cmp
import matplotlib.pyplot as plt
from qiskit import QuantumCircuit
from qiskit import Aer, execute
from qiskit.visualization import plot_histogram
# Initialize parameters
t0 = 2 * np.pi
r = 6
A = [[3.75, 2.25, 1.25, -0.75],
[2.25, 3.75, 0.75, -1.25],
... |
https://github.com/shantomborah/Quantum-Algorithms | shantomborah | import numpy as np
import matplotlib.pyplot as plt
from qiskit import QuantumCircuit, Aer, execute
from qiskit.circuit import ParameterVector
from qiskit.extensions import HamiltonianGate
from qiskit.visualization import plot_histogram
from qiskit.aqua.components.optimizers import COBYLA
class QAOA:
... |
https://github.com/shantomborah/Quantum-Algorithms | shantomborah | import numpy as np
import matplotlib.pyplot as plt
from qiskit import QuantumCircuit
from qiskit import QuantumRegister
from qiskit.quantum_info import Operator
from qiskit.circuit.library.standard_gates import XGate, YGate, ZGate
class FiveQubitCode:
def __init__(self):
# Initialize Registe... |
https://github.com/shantomborah/Quantum-Algorithms | shantomborah | """Performance Analysis of 5 Qubit Code under Depolarizing Error"""
import noise
import numpy as np
import matplotlib.pyplot as plt
from qiskit import QuantumCircuit, ClassicalRegister
from qiskit import Aer, execute
from qiskit.visualization import plot_histogram, plot_bloch_vector
from five_qubit_code impo... |
https://github.com/shantomborah/Quantum-Algorithms | shantomborah | import numpy as np
import matplotlib.pyplot as plt
from qiskit import QuantumCircuit
from qiskit import QuantumRegister
from qiskit.quantum_info import Operator
from qiskit.circuit.library.standard_gates import XGate
class ThreeQubitCode:
def __init__(self):
# Initialize Registers
... |
https://github.com/shantomborah/Quantum-Algorithms | shantomborah | """Performance Analysis of 3 Qubit Code under Bit Flip Error"""
import noise
import numpy as np
import matplotlib.pyplot as plt
from qiskit import QuantumCircuit, ClassicalRegister
from qiskit import Aer, execute
from qiskit.quantum_info import partial_trace, state_fidelity
from qiskit.quantum_info import De... |
https://github.com/shantomborah/Quantum-Algorithms | shantomborah | import random
import numpy as np
import matplotlib.pyplot as plt
from qiskit import QuantumCircuit, Aer, execute
from qiskit.circuit import ParameterVector
from qiskit.quantum_info import state_fidelity
from qiskit.quantum_info import Statevector, DensityMatrix, Operator
class Compressor:
def __init... |
https://github.com/shantomborah/Quantum-Algorithms | shantomborah | import numpy as np
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
from qiskit import Aer, execute
from qiskit.circuit import Gate
# Define no. of message qubits
n = 10
# Alice's end
alice_prepare = np.random.choice([0,1], size=(n,), p=[0.5, 0.5])
alice_hadamard = np.random.choice([0,1... |
https://github.com/shantomborah/Quantum-Algorithms | shantomborah | from qiskit import *
from qiskit.tools.visualization import plot_histogram
%matplotlib inline
secretnumber = '11100011'
circuit = QuantumCircuit(len(secretnumber)+1,len(secretnumber))
circuit.h(range(len(secretnumber)))
circuit.x(len(secretnumber))
circuit.h(len(secretnumber))
circuit.barrier()
for i... |
https://github.com/shantomborah/Quantum-Algorithms | shantomborah | import numpy as np
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
from qiskit import Aer, execute
from qiskit.circuit import Gate
from qiskit.visualization import plot_histogram, plot_bloch_multivector
circ = QuantumCircuit(2, 1)
circ.reset([0, 1])
circ.x(0)
circ.barrier()
circ.h([0, 1... |
https://github.com/shantomborah/Quantum-Algorithms | shantomborah | import numpy as np
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
from qiskit import Aer, execute
from qiskit.circuit import Gate
from qiskit.visualization import plot_histogram, plot_bloch_multivector
# Define no. of input qubits
n = 4
circ = QuantumCircuit(n+1, n)
circ.reset(range(n... |
https://github.com/shantomborah/Quantum-Algorithms | shantomborah | my_list = [1,3,5,2,4,2,5,8,0,7,6]
#classical computation method
def oracle(my_input):
winner =7
if my_input is winner:
response = True
else:
response = False
return response
for index, trial_number in enumerate(my_list):
if oracle(trial_number) is True:
pri... |
https://github.com/shantomborah/Quantum-Algorithms | shantomborah | import numpy as np
from math import pi
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
from qiskit import Aer, execute
from qiskit.circuit import Gate
from qiskit.visualization import plot_histogram
from qiskit.circuit.library.standard_gates import ZGate, XGate
# Define circuit parameters... |
https://github.com/shantomborah/Quantum-Algorithms | shantomborah | from qiskit import *
from qiskit.visualization import plot_bloch_multivector, plot_bloch_vector, plot_histogram
from qiskit.compiler import transpile, assemble
from qiskit.tools.monitor import job_monitor
from qiskit.providers.ibmq import least_busy
import matplotlib
import numpy as np
%pylab inline
qc =... |
https://github.com/shantomborah/Quantum-Algorithms | shantomborah | # initialization
import numpy as np
# importing Qiskit
from qiskit import IBMQ, BasicAer
from qiskit.providers.ibmq import least_busy
from qiskit import QuantumCircuit, execute, Aer
from qiskit.tools.jupyter import *
provider = IBMQ.load_account()
# import basic plot tools
from qiskit.visualization import ... |
https://github.com/shantomborah/Quantum-Algorithms | shantomborah | # Importing Packages
from qiskit import IBMQ, Aer
from qiskit.providers.ibmq import least_busy
from qiskit import QuantumCircuit, transpile, assemble, QuantumRegister, ClassicalRegister
from qiskit.visualization import plot_histogram
from qiskit_textbook.tools import simon_oracle
bb = input("Enter the input st... |
https://github.com/shantomborah/Quantum-Algorithms | shantomborah | import numpy as np
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
from qiskit import Aer, execute
from qiskit.circuit import Gate
from qiskit.visualization import plot_bloch_multivector
from math import pi
q = QuantumRegister(3, name='q')
c = ClassicalRegister(2, name='c')
circ = Quantu... |
https://github.com/LohitPotnuru/TransverseIsingModelQiskit | LohitPotnuru | from qiskit import *
from scipy.optimize import minimize
import numpy as np
from pylab import *
#2^4 possible states of four qubits stored in dic
bit = ['0','1']
dic = []
for i in bit:
for j in bit:
for k in bit:
for l in bit:
dic.append(i+j+l+k)
def calcEJ(thet... |
https://github.com/sebasmos/QuantumVE | sebasmos | import numpy as np
import matplotlib.pyplot as plt
def initial_state(num_positions):
return np.ones(num_positions) / num_positions
def predict_state(prior_prob, velocity):
num_positions = len(prior_prob)
predicted_prob = np.zeros(num_positions)
for i in range(num_positions):
predicte... |
https://github.com/sebasmos/QuantumVE | sebasmos | %reset -f
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import numpy as np
import pickle
import datetime
import os
name_data = "sample_trajectories.pkl"
os.makedirs("data")
Fn = os.path.join(os.getcwd(),"data",name_data)
# initialize random number generator
rng = np.random.d... |
https://github.com/sebasmos/QuantumVE | sebasmos | #Assign these values as per your requirements.
global min_qubits,max_qubits,skip_qubits,max_circuits,num_shots,Noise_Inclusion
min_qubits=1
max_qubits=3
skip_qubits=1
max_circuits=3
num_shots=1000
use_XX_YY_ZZ_gates = True
Noise_Inclusion = False
saveplots = False
Memory_utilization_plot = True
Type_... |
https://github.com/sebasmos/QuantumVE | sebasmos | import numpy as np
import qiskit.pulse as pulse
from qiskit.circuit import Parameter
from qiskit_experiments.calibration_management.backend_calibrations import BackendCalibrations
from qiskit import IBMQ, schedule
API_KEY = "5bd4ecfdc74e6680da7c79998259781431661e5326ae2f88eea95dee8f74b87530ba63fbca8105404d... |
https://github.com/sebasmos/QuantumVE | sebasmos | API_KEY = "5bd4ecfdc74e6680da7c79998259781431661e5326ae2f88eea95dee8f74b87530ba63fbca8105404de4ffd36e4b484631907acff73c805580928218a5ccf0b3"
import qiskit as q
from qiskit import IBMQ,schedule
import numpy as np
import qiskit.pulse as pulse
from qiskit.circuit import Parameter
%matplotlib inline
import s... |
https://github.com/sebasmos/QuantumVE | sebasmos | import qiskit as q
from qiskit import IBMQ
%matplotlib inline
IBMQ.save_account("5bd4ecfdc74e6680da7c79998259781431661e5326ae2f88eea95dee8f74b87530ba63fbca8105404de4ffd36e4b484631907acff73c805580928218a5ccf0b3")
# Details in: https://qiskit.org/documentation/install.html
# https://quantumcomputing.stackexchange... |
https://github.com/sebasmos/QuantumVE | sebasmos | API_KEY = "5bd4ecfdc74e6680da7c79998259781431661e5326ae2f88eea95dee8f74b87530ba63fbca8105404de4ffd36e4b484631907acff73c805580928218a5ccf0b3"
import qiskit as q
from qiskit import IBMQ,schedule
import numpy as np
import qiskit.pulse as pulse
from qiskit.circuit import Parameter
%matplotlib inline
import s... |
https://github.com/sebasmos/QuantumVE | sebasmos | # Quantum Kernel Alighment
[Reference](https://quantumcomputing.com/Havry/projects/qiskit-runtime-quantum-kernel-alignment/files/main.py)
API_KEY = "5bd4ecfdc74e6680da7c79998259781431661e5326ae2f88eea95dee8f74b87530ba63fbca8105404de4ffd36e4b484631907acff73c805580928218a5ccf0b3"
import qiskit as q
from qiskit... |
https://github.com/sebasmos/QuantumVE | sebasmos | # This code is part of qiskit-runtime.
#
# (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 ... |
https://github.com/sebasmos/QuantumVE | sebasmos | # This code is part of qiskit-runtime.
#
# (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 ... |
https://github.com/sebasmos/QuantumVE | sebasmos | import sys
sys.path.insert(0,'../')
from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR
from torch.utils.data import random... |
https://github.com/sebasmos/QuantumVE | sebasmos | import sys
sys.path.insert(0,'../')
from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR
from torch.utils.data import random... |
https://github.com/sebasmos/QuantumVE | sebasmos | !pwd
from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR
from torch.utils.data import random_split
from torch.utils.data i... |
https://github.com/sebasmos/QuantumVE | sebasmos | from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR
from torch.utils.data import random_split
from torch.utils.data import Su... |
https://github.com/sebasmos/QuantumVE | sebasmos | from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR
from torch.utils.data import random_split
from torch.utils.data import Su... |
https://github.com/sebasmos/QuantumVE | sebasmos | from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR
from torch.utils.data import random_split
from torch.utils.data import Su... |
https://github.com/sebasmos/QuantumVE | sebasmos | import sys
import os
import torch
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import models_mae
import torch; print(f'numpy version: {np.__version__}\nCUDA version: {torch.version.cuda} - Torch versteion: {torch.__version__} - device count: {torch.cuda.device_count()}')
# define... |
https://github.com/sebasmos/QuantumVE | sebasmos | import os
MODEL_METADATA = "SVM"
model_name = "efficientnet_b3_embeddings_finetuning"
EXPERIMENT_NAME = "efficientnet_b3_embeddings_"#"efficientnet_v2_m"#"convnext_base"#"efficientnet_b3"#"mobileNet"
results_path = f"{model_name}/{MODEL_METADATA}"
os.makedirs(results_path, exist_ok = True)
train_path = f"{model_n... |
https://github.com/sebasmos/QuantumVE | sebasmos | # Define the model name
model_name = "efficientnet_b3" #EfficientNet_B7_Weights.IMAGENET1K_V1
feat_space = 8
!pwd
%cd qubico
!pwd
import torchvision.models as models
import torch
MODEL_CONSTRUCTORS = {
'alexnet': models.alexnet,
'convnext_base': models.convnext_base,
'convnext_large': models.... |
https://github.com/sebasmos/QuantumVE | sebasmos | # !pip install qiskit torch torchvision matplotlib
# !pip install qiskit-machine-learning
# !pip install torchviz
# !pip install qiskit[all]
# !pip install qiskit == 0.45.2
# !pip install qiskit_algorithms == 0.7.1
# !pip install qiskit-ibm-runtime == 0.17.0
# !pip install qiskit-aer == 0.13.2
# #Quentum ne... |
https://github.com/sebasmos/QuantumVE | sebasmos | # !pip install qiskit torch torchvision matplotlib
# !pip install qiskit-machine-learning
# !pip install torchviz
# !pip install qiskit[all]
# !pip install qiskit == 0.45.2
# !pip install qiskit_algorithms == 0.7.1
# !pip install qiskit-ibm-runtime == 0.17.0
# !pip install qiskit-aer == 0.13.2
# #Quentum ne... |
https://github.com/sebasmos/QuantumVE | sebasmos | # !pip install qiskit torch torchvision matplotlib
# !pip install qiskit-machine-learning
# !pip install torchviz
# !pip install qiskit[all]
# !pip install qiskit == 0.45.2
# !pip install qiskit_algorithms == 0.7.1
# !pip install qiskit-ibm-runtime == 0.17.0
# !pip install qiskit-aer == 0.13.2
# #Quentum ne... |
https://github.com/sebasmos/QuantumVE | sebasmos | import sys
sys.path.insert(0,'../')
# from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.optim.lr_scheduler import StepLR
from torch.utils.data import random_split
from torch.utils.data import Subset,... |
https://github.com/sebasmos/QuantumVE | sebasmos | import os
MODEL_METADATA = "RF"
model_name = "efficientnet_v2_m"#"efficientnet_b7"## "mobilenet_v3_large"#"convnext_base"
EXPERIMENT_NAME = f"{model_name}_embeddings"
results_path = f"{EXPERIMENT_NAME}/{MODEL_METADATA}"
os.makedirs(results_path, exist_ok = True)
os.makedirs(EXPERIMENT_NAME, exist_ok = True)
tr... |
https://github.com/sebasmos/QuantumVE | sebasmos | import os
MODEL_METADATA = "SVM"
model_name = "efficientnet_b3_embeddings_finetuning"
EXPERIMENT_NAME = "efficientnet_b3_embeddings_"#"efficientnet_v2_m"#"convnext_base"#"efficientnet_b3"#"mobileNet"
results_path = f"{model_name}/{MODEL_METADATA}"
os.makedirs(results_path, exist_ok = True)
train_path = f"{model_n... |
https://github.com/sebasmos/QuantumVE | sebasmos | import os
MODEL_METADATA = "XGBoost"
model_name = "convnext_base"
EXPERIMENT_NAME = f"{model_name}_embeddings"
results_path = f"{EXPERIMENT_NAME}/{MODEL_METADATA}"
os.makedirs(results_path, exist_ok = True)
os.makedirs(EXPERIMENT_NAME, exist_ok = True)
train_path = f"{EXPERIMENT_NAME}/train"
val_path = f"{EXP... |
https://github.com/sebasmos/QuantumVE | sebasmos | # Define the model name
model_name = "efficientnet_v2_m" #EfficientNet_B7_Weights.IMAGENET1K_V1
!pwd
%cd Vector_Embeddings
!pwd
import torchvision.models as models
import torch
MODEL_CONSTRUCTORS = {
'alexnet': models.alexnet,
'convnext_base': models.convnext_base,
'convnext_large': models.con... |
https://github.com/sebasmos/QuantumVE | sebasmos | !pip install tensorflow==2.4.1
!pip install tensorflow-quantum
import tensorflow as tf
import tensorflow_quantum as tfq
import cirq
import sympy
import numpy as np
# visualization tools
%matplotlib inline
import matplotlib.pyplot as plt
from cirq.contrib.svg import SVGCircuit
a, b = sympy.symbols('... |
https://github.com/derivation/ThinkQuantum | derivation | from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister, execute, Aer
from qiskit.tools.visualization import circuit_drawer, plot_histogram
import numpy as np
from numpy import pi
num_measurements = 5
q = QuantumRegister(1)
c = ClassicalRegister(num_measurements)
circ = QuantumCircuit(q,c)
... |
https://github.com/derivation/ThinkQuantum | derivation | from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister, execute, Aer
from qiskit.tools.visualization import circuit_drawer, plot_histogram
import numpy as np
from numpy import pi
num_measurements = 5
q = QuantumRegister(1)
c = ClassicalRegister(num_measurements)
circ = QuantumCircuit(q,c)
... |
https://github.com/derivation/ThinkQuantum | derivation | import numpy as np
from numpy import pi
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
filename = './schrodin_yang.png'
im = mpimg.imread(filename)
fig, ax = plt.subplots()
ax.imshow(im)
from skimage.transform import resize
n_pixels = 2**5
im = resize(im, (n_pixels, n_pixels))
fig, a... |
https://github.com/derivation/ThinkQuantum | derivation | import numpy as np
from numpy import pi
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from skimage.transform import resize
filename = './schrodin_yang.png'
im = mpimg.imread(filename)
n_pixels = 2**5
im = resize(im, (n_pixels, n_pixels))
data = im[:,:,0].ravel()
fig, ax = plt.subpl... |
https://github.com/derivation/ThinkQuantum | derivation | import numpy as np
from numpy import pi
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
filename = './schrodin_yang.png'
im = mpimg.imread(filename)
fig, ax = plt.subplots()
ax.imshow(im)
from skimage.transform import resize
n_pixels = 2**5
im = resize(im, (n_pixels, n_pixels))
fig, a... |
https://github.com/derivation/ThinkQuantum | derivation | import numpy as np
from numpy import pi
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from skimage.transform import resize
filename = './schrodin_yang.png'
im = mpimg.imread(filename)
n_pixels = 2**5
im = resize(im, (n_pixels, n_pixels))
data = im[:,:,0].ravel()
fig, ax = plt.subpl... |
https://github.com/ronitd2002/IBM-Quantum-challenge-2024 | ronitd2002 | %pip install git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git --upgrade
# qc-grader should be 0.18.12 (or higher)
import qc_grader
qc_grader.__version__
### Install Qiskit and relevant packages, if needed
### IMPORTANT: Make sure you are on 3.10 > python < 3.12
%pip install qiskit[visual... |
https://github.com/ronitd2002/IBM-Quantum-challenge-2024 | ronitd2002 | from qiskit import QuantumCircuit
# Create a new circuit with a single qubit
qc = QuantumCircuit(1)
# Add a Not gate to qubit 0
qc.x(0)
# Return a drawing of the circuit using MatPlotLib ("mpl"). This is the
# last line of the cell, so the drawing appears in the cell output.
qc.draw("mpl")
### CHECK QIS... |
https://github.com/ronitd2002/IBM-Quantum-challenge-2024 | ronitd2002 | import numpy as np
# Importing standard Qiskit libraries
from qiskit import QuantumCircuit, transpile, assemble, Aer, IBMQ, execute
from qiskit.quantum_info import Statevector
from qiskit.visualization import plot_bloch_multivector, plot_histogram
from qiskit_textbook.problems import dj_problem_oracle
def lab... |
https://github.com/ronitd2002/IBM-Quantum-challenge-2024 | ronitd2002 | import networkx as nx
import numpy as np
import plotly.graph_objects as go
import matplotlib as mpl
import pandas as pd
from IPython.display import clear_output
from plotly.subplots import make_subplots
from matplotlib import pyplot as plt
from qiskit import Aer
from qiskit import QuantumCircuit
from qiskit.v... |
https://github.com/ronitd2002/IBM-Quantum-challenge-2024 | ronitd2002 | # qc-grader should be 0.18.10 (or higher)
import qc_grader
qc_grader.__version__
# Imports
import numpy as np
import matplotlib.pyplot as plt
from qiskit.circuit.library import EfficientSU2
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
from qiskit_ibm_runtime import Qiskit... |
https://github.com/ronitd2002/IBM-Quantum-challenge-2024 | ronitd2002 | # please create a 3 qubit GHZ circuit
# please create a 2 qubit ch gate with only cx and ry gate
# Enter your prompt here - Delete this line |
https://github.com/ronitd2002/IBM-Quantum-challenge-2024 | ronitd2002 | from qiskit.circuit.library import RealAmplitudes
ansatz = RealAmplitudes(num_qubits=2, reps=1, entanglement='linear')
ansatz.draw('mpl', style='iqx')
from qiskit.opflow import Z, I
hamiltonian = Z ^ Z
from qiskit.opflow import StateFn
expectation = StateFn(hamiltonian, is_measurement=True) @ StateFn(an... |
https://github.com/ronitd2002/IBM-Quantum-challenge-2024 | ronitd2002 | ### Install Qiskit and relevant packages, if needed
### IMPORTANT: Make sure you are on 3.10 > python < 3.12
'''
%pip install qiskit[visualization]==1.0.2
%pip install qiskit-ibm-runtime
%pip install qiskit-aer
%pip install graphviz
%pip install qiskit-serverless -U
%pip install qiskit-transpiler-service -U
''... |
https://github.com/ronitd2002/IBM-Quantum-challenge-2024 | ronitd2002 | # transpile_parallel.py
from qiskit import QuantumCircuit
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
from qiskit_transpiler_service.transpiler_service import TranspilerService
from qiskit_serverless import get_arguments, save_result, distribute_task, get
from qiskit_ibm_runtim... |
https://github.com/ronitd2002/IBM-Quantum-challenge-2024 | ronitd2002 | import json
import logging
import numpy as np
import warnings
from functools import wraps
from typing import Any, Callable, Optional, Tuple, Union
from qiskit import IBMQ, QuantumCircuit, assemble
from qiskit.circuit import Barrier, Gate, Instruction, Measure
from qiskit.circuit.library import UGate, U3Ga... |
https://github.com/ronitd2002/IBM-Quantum-challenge-2024 | ronitd2002 | # -*- coding: utf-8 -*-
# This code is part of Qiskit.
#
# (C) Copyright IBM 2018, 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.... |
https://github.com/ronitd2002/IBM-Quantum-challenge-2024 | ronitd2002 | from qiskit import QuantumCircuit
# Create a new circuit with a single qubit
qc = QuantumCircuit(1)
# Add a Not gate to qubit 0
qc.x(0)
# Return a drawing of the circuit using MatPlotLib ("mpl"). This is the
# last line of the cell, so the drawing appears in the cell output.
qc.draw("mpl")
### CHECK QIS... |
https://github.com/ronitd2002/IBM-Quantum-challenge-2024 | ronitd2002 | # imports
import numpy as np
from typing import List, Callable
from scipy.optimize import minimize
from scipy.optimize._optimize import OptimizeResult
import matplotlib.pyplot as plt
from qiskit import QuantumCircuit
from qiskit.quantum_info import Statevector, Operator, SparsePauliOp
from qiskit.primitives i... |
https://github.com/ronitd2002/IBM-Quantum-challenge-2024 | ronitd2002 | # Imports
from qiskit.circuit.random import random_circuit
from qiskit.circuit.library import XGate, YGate
from qiskit_ibm_runtime.fake_provider import FakeTorino, FakeOsaka
from qiskit.transpiler import InstructionProperties, PassManager
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_man... |
https://github.com/ronitd2002/IBM-Quantum-challenge-2024 | ronitd2002 | # qc-grader should be 0.18.10 (or higher)
import qc_grader
qc_grader.__version__
# Imports
import numpy as np
import matplotlib.pyplot as plt
from qiskit.circuit.library import EfficientSU2
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
from qiskit_ibm_runtime import Qiskit... |
https://github.com/ronitd2002/IBM-Quantum-challenge-2024 | ronitd2002 | # qc-grader should be 0.18.11 (or higher)
import qc_grader
qc_grader.__version__
# Imports
import numpy as np
import matplotlib.pyplot as plt
from qiskit import QuantumCircuit
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
from qiskit.visualization import plot_gate_map
fro... |
https://github.com/ronitd2002/IBM-Quantum-challenge-2024 | ronitd2002 | # please create a 3 qubit GHZ circuit
# please create a 2 qubit ch gate with only cx and ry gate
# Enter your prompt here - Delete this line |
https://github.com/ronitd2002/IBM-Quantum-challenge-2024 | ronitd2002 | # qc-grader should be 0.18.11 (or higher)
import qc_grader
qc_grader.__version__
# Import all in one cell
import numpy as np
import matplotlib.pyplot as plt
from timeit import default_timer as timer
import warnings
from qiskit import QuantumCircuit
from qiskit.transpiler.preset_passmanagers import gene... |
https://github.com/ronitd2002/IBM-Quantum-challenge-2024 | ronitd2002 | # Import all in one cell
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.optimize import minimize
from qiskit import QuantumCircuit
from qiskit.quantum_info import SparsePauliOp
from qiskit.circuit.library import RealAmplitudes
from qiskit.transpiler.preset_passmanagers imp... |
https://github.com/ronitd2002/IBM-Quantum-challenge-2024 | ronitd2002 | ### Install Qiskit and relevant packages, if needed
### IMPORTANT: Make sure you are on 3.10 > python < 3.12
'''
%pip install qiskit[visualization]==1.0.2
%pip install qiskit-ibm-runtime
%pip install qiskit-aer
%pip install graphviz
%pip install qiskit-serverless -U
%pip install qiskit-transpiler-service -U
''... |
https://github.com/ronitd2002/IBM-Quantum-challenge-2024 | ronitd2002 | # transpile_parallel.py
from qiskit import QuantumCircuit
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
from qiskit_transpiler_service.transpiler_service import TranspilerService
from qiskit_serverless import get_arguments, save_result, distribute_task, get
from qiskit_ibm_runtim... |
https://github.com/ronitd2002/IBM-Quantum-challenge-2024 | ronitd2002 | import numpy as np
from qiskit import transpile, QuantumCircuit
def version_check():
import qiskit
if qiskit.version.VERSION == '1.0.2':
return print("You have the right version! Enjoy the challenge!")
else:
return print("please install right version by copy/paste and execute - !pip... |
https://github.com/ronitd2002/IBM-Quantum-challenge-2024 | ronitd2002 | from qiskit_aer import AerSimulator
import logging
from typing import Optional
import time
import numpy as np
from scipy.optimize import minimize
from qiskit import QuantumCircuit
from qiskit_ibm_runtime import (
EstimatorV2 as Estimator,
SamplerV2 as Sampler,
QiskitRuntimeService,
Session,... |
https://github.com/ronitd2002/IBM-Quantum-challenge-2024 | ronitd2002 | import numpy as np
# Importing standard Qiskit libraries
from qiskit import QuantumCircuit, transpile, assemble, Aer, IBMQ, execute
from qiskit.quantum_info import Statevector
from qiskit.visualization import plot_bloch_multivector, plot_histogram
from qiskit_textbook.problems import dj_problem_oracle
def lab... |
https://github.com/ronitd2002/IBM-Quantum-challenge-2024 | ronitd2002 | # transpile_parallel.py
from qiskit import QuantumCircuit
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
from qiskit_transpiler_service.transpiler_service import TranspilerService
from qiskit_serverless import get_arguments, save_result, distribute_task, get
from qiskit_ibm_runtim... |
https://github.com/ronitd2002/IBM-Quantum-challenge-2024 | ronitd2002 | # transpile_parallel.py
from qiskit import QuantumCircuit
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
from qiskit_transpiler_service.transpiler_service import TranspilerService
from qiskit_serverless import get_arguments, save_result, distribute_task, get
from qiskit_ibm_runtim... |
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