repo stringclasses 900
values | file stringclasses 754
values | content stringlengths 4 215k |
|---|---|---|
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
from qiskit import QuantumCircuit
from qiskit.algorithms import IterativeAmplitudeEstimation, EstimationProblem
from qiskit.circuit.library import LinearAmplitudeFunction
from qiskit_aer.primitives import Sampler
from qiskit_finance.circui... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
from qiskit.algorithms import IterativeAmplitudeEstimation, EstimationProblem
from qiskit.circuit.library import LinearAmplitudeFunction
from qiskit_aer.primitives import Sampler
from qiskit_finance.circuit.library import LogNormalDistribut... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
from qiskit.algorithms import IterativeAmplitudeEstimation, EstimationProblem
from qiskit.circuit.library import LinearAmplitudeFunction
from qiskit_aer.primitives import Sampler
from qiskit_finance.circuit.library import LogNormalDistribut... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import matplotlib.pyplot as plt
from scipy.interpolate import griddata
%matplotlib inline
import numpy as np
from qiskit import QuantumRegister, QuantumCircuit, AncillaRegister, transpile
from qiskit.algorithms import IterativeAmplitudeEstimation, EstimationProblem
from qiskit.circuit.library import WeightedA... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import matplotlib.pyplot as plt
from scipy.interpolate import griddata
%matplotlib inline
import numpy as np
from qiskit import QuantumRegister, QuantumCircuit, AncillaRegister, transpile
from qiskit.circuit.library import IntegerComparator, WeightedAdder, LinearAmplitudeFunction
from qiskit.algorithms import... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
from qiskit import QuantumCircuit
from qiskit.algorithms import IterativeAmplitudeEstimation, EstimationProblem
from qiskit_aer.primitives import Sampler
from qiskit_finance.circuit.library import NormalDistribution
# can be used in case a... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import numpy as np
import matplotlib.pyplot as plt
from qiskit import QuantumRegister, QuantumCircuit
from qiskit.circuit.library import IntegerComparator
from qiskit.algorithms import IterativeAmplitudeEstimation, EstimationProblem
from qiskit_aer.primitives import Sampler
# set problem parameters
n_z = 2
... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import matplotlib.pyplot as plt
import numpy as np
from qiskit.circuit import ParameterVector
from qiskit.circuit.library import TwoLocal
from qiskit.quantum_info import Statevector
from qiskit.algorithms import IterativeAmplitudeEstimation, EstimationProblem
from qiskit_aer.primitives import Sampler
from qi... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | %matplotlib inline
from qiskit_finance import QiskitFinanceError
from qiskit_finance.data_providers import *
import datetime
import matplotlib.pyplot as plt
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
data = RandomDataProvider(
tickers=["TICKER1", "TICKER2... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from qiskit.utils import algorithm_globals
algorithm_globals.random_seed = 123456
from sklearn.datasets import make_blobs
features, labels = make_blobs(
n_samples=20,
centers=2,
center_box=(-1, 1),
cluster_std=0.1,
random_state=algorithm_globals.random_seed,
)
from qiskit import Ba... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from qiskit.utils import algorithm_globals
algorithm_globals.random_seed = 42
from qiskit.circuit import Parameter
from qiskit import QuantumCircuit
params1 = [Parameter("input1"), Parameter("weight1")]
qc1 = QuantumCircuit(1)
qc1.h(0)
qc1.ry(params1[0], 0)
qc1.rx(params1[1], 0)
qc1.draw("mpl")
from q... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from sklearn.datasets import load_iris
iris_data = load_iris()
print(iris_data.DESCR)
features = iris_data.data
labels = iris_data.target
from sklearn.preprocessing import MinMaxScaler
features = MinMaxScaler().fit_transform(features)
import pandas as pd
import seaborn as sns
df = pd.DataFrame(ir... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import matplotlib.pyplot as plt
import numpy as np
from IPython.display import clear_output
from qiskit import QuantumCircuit
from qiskit.algorithms.optimizers import COBYLA, L_BFGS_B
from qiskit.circuit import Parameter
from qiskit.circuit.library import RealAmplitudes, ZZFeatureMap
from qiskit.utils import alg... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from qiskit.utils import algorithm_globals
algorithm_globals.random_seed = 12345
from qiskit_machine_learning.datasets import ad_hoc_data
adhoc_dimension = 2
train_features, train_labels, test_features, test_labels, adhoc_total = ad_hoc_data(
training_size=20,
test_size=5,
n=adhoc_dimension,
... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import torch
from qiskit.utils import algorithm_globals
algorithm_globals.random_seed = 123456
_ = torch.manual_seed(123456) # suppress output
import numpy as np
num_dim = 2
num_discrete_values = 8
num_qubits = num_dim * int(np.log2(num_discrete_values))
from scipy.stats import multivariate_normal
c... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | # Necessary imports
import numpy as np
import matplotlib.pyplot as plt
from torch import Tensor
from torch.nn import Linear, CrossEntropyLoss, MSELoss
from torch.optim import LBFGS
from qiskit import QuantumCircuit
from qiskit.utils import algorithm_globals
from qiskit.circuit import Parameter
from qiski... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from sklearn.datasets import make_blobs
# example dataset
features, labels = make_blobs(n_samples=20, n_features=2, centers=2, random_state=3, shuffle=True)
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
features = MinMaxScaler(featur... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | # External imports
from pylab import cm
from sklearn import metrics
import numpy as np
import matplotlib.pyplot as plt
# Qiskit imports
from qiskit import QuantumCircuit
from qiskit.circuit import ParameterVector
from qiskit.visualization import circuit_drawer
from qiskit.algorithms.optimizers import SPSA
f... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import matplotlib.pyplot as plt
import numpy as np
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit.library import RealAmplitudes
from qiskit.primitives import Sampler
from qiskit.utils import algorithm_globals
from sklearn.model_selection import train_test_split
from sklearn.preprocessing imp... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | # Necessary imports
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import clear_output
from qiskit import QuantumCircuit
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit.library import ZFeatureMap, RealAmplitudes
from qiskit.utils import algorithm_globals
from sklearn... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import json
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import clear_output
from qiskit import QuantumCircuit
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit import ParameterVector
from qiskit.circuit.library import ZFeatureMap
from qiskit.quantum_info import Spar... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import json
import time
import warnings
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import clear_output
from qiskit import ClassicalRegister, QuantumRegister
from qiskit import QuantumCircuit
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit.library import RealAm... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from qiskit.utils import algorithm_globals
algorithm_globals.random_seed = 123456
from sklearn.datasets import make_blobs
features, labels = make_blobs(
n_samples=20,
centers=2,
center_box=(-1, 1),
cluster_std=0.1,
random_state=algorithm_globals.random_seed,
)
from qiskit import Ba... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from qiskit.utils import algorithm_globals
algorithm_globals.random_seed = 42
from qiskit.circuit import Parameter
from qiskit import QuantumCircuit
params1 = [Parameter("input1"), Parameter("weight1")]
qc1 = QuantumCircuit(1)
qc1.h(0)
qc1.ry(params1[0], 0)
qc1.rx(params1[1], 0)
qc1.draw("mpl")
from q... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from sklearn.datasets import load_iris
iris_data = load_iris()
print(iris_data.DESCR)
features = iris_data.data
labels = iris_data.target
from sklearn.preprocessing import MinMaxScaler
features = MinMaxScaler().fit_transform(features)
import pandas as pd
import seaborn as sns
df = pd.DataFrame(ir... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import matplotlib.pyplot as plt
import numpy as np
from IPython.display import clear_output
from qiskit import QuantumCircuit
from qiskit.algorithms.optimizers import COBYLA, L_BFGS_B
from qiskit.circuit import Parameter
from qiskit.circuit.library import RealAmplitudes, ZZFeatureMap
from qiskit.utils import alg... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from qiskit.utils import algorithm_globals
algorithm_globals.random_seed = 12345
from qiskit_machine_learning.datasets import ad_hoc_data
adhoc_dimension = 2
train_features, train_labels, test_features, test_labels, adhoc_total = ad_hoc_data(
training_size=20,
test_size=5,
n=adhoc_dimension,
... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import torch
from qiskit.utils import algorithm_globals
algorithm_globals.random_seed = 123456
_ = torch.manual_seed(123456) # suppress output
import numpy as np
num_dim = 2
num_discrete_values = 8
num_qubits = num_dim * int(np.log2(num_discrete_values))
from scipy.stats import multivariate_normal
c... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | # Necessary imports
import numpy as np
import matplotlib.pyplot as plt
from torch import Tensor
from torch.nn import Linear, CrossEntropyLoss, MSELoss
from torch.optim import LBFGS
from qiskit import QuantumCircuit
from qiskit.utils import algorithm_globals
from qiskit.circuit import Parameter
from qiski... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from sklearn.datasets import make_blobs
# example dataset
features, labels = make_blobs(n_samples=20, n_features=2, centers=2, random_state=3, shuffle=True)
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
features = MinMaxScaler(featur... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | # External imports
from pylab import cm
from sklearn import metrics
import numpy as np
import matplotlib.pyplot as plt
# Qiskit imports
from qiskit import QuantumCircuit
from qiskit.circuit import ParameterVector
from qiskit.visualization import circuit_drawer
from qiskit.algorithms.optimizers import SPSA
f... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import matplotlib.pyplot as plt
import numpy as np
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit.library import RealAmplitudes
from qiskit.primitives import Sampler
from qiskit.utils import algorithm_globals
from sklearn.model_selection import train_test_split
from sklearn.preprocessing imp... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | # Necessary imports
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import clear_output
from qiskit import QuantumCircuit
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit.library import ZFeatureMap, RealAmplitudes
from qiskit.utils import algorithm_globals
from sklearn... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import json
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import clear_output
from qiskit import QuantumCircuit
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit import ParameterVector
from qiskit.circuit.library import ZFeatureMap
from qiskit.quantum_info import Spar... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import json
import time
import warnings
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import clear_output
from qiskit import ClassicalRegister, QuantumRegister
from qiskit import QuantumCircuit
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit.library import RealAm... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from qiskit.utils import algorithm_globals
algorithm_globals.random_seed = 123456
from sklearn.datasets import make_blobs
features, labels = make_blobs(
n_samples=20,
centers=2,
center_box=(-1, 1),
cluster_std=0.1,
random_state=algorithm_globals.random_seed,
)
from qiskit import Ba... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from qiskit.utils import algorithm_globals
algorithm_globals.random_seed = 42
from qiskit.circuit import Parameter
from qiskit import QuantumCircuit
params1 = [Parameter("input1"), Parameter("weight1")]
qc1 = QuantumCircuit(1)
qc1.h(0)
qc1.ry(params1[0], 0)
qc1.rx(params1[1], 0)
qc1.draw("mpl")
from q... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from sklearn.datasets import load_iris
iris_data = load_iris()
print(iris_data.DESCR)
features = iris_data.data
labels = iris_data.target
from sklearn.preprocessing import MinMaxScaler
features = MinMaxScaler().fit_transform(features)
import pandas as pd
import seaborn as sns
df = pd.DataFrame(ir... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import matplotlib.pyplot as plt
import numpy as np
from IPython.display import clear_output
from qiskit import QuantumCircuit
from qiskit.algorithms.optimizers import COBYLA, L_BFGS_B
from qiskit.circuit import Parameter
from qiskit.circuit.library import RealAmplitudes, ZZFeatureMap
from qiskit.utils import alg... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from qiskit.utils import algorithm_globals
algorithm_globals.random_seed = 12345
from qiskit_machine_learning.datasets import ad_hoc_data
adhoc_dimension = 2
train_features, train_labels, test_features, test_labels, adhoc_total = ad_hoc_data(
training_size=20,
test_size=5,
n=adhoc_dimension,
... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import torch
from qiskit.utils import algorithm_globals
algorithm_globals.random_seed = 123456
_ = torch.manual_seed(123456) # suppress output
import numpy as np
num_dim = 2
num_discrete_values = 8
num_qubits = num_dim * int(np.log2(num_discrete_values))
from scipy.stats import multivariate_normal
c... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | # Necessary imports
import numpy as np
import matplotlib.pyplot as plt
from torch import Tensor
from torch.nn import Linear, CrossEntropyLoss, MSELoss
from torch.optim import LBFGS
from qiskit import QuantumCircuit
from qiskit.utils import algorithm_globals
from qiskit.circuit import Parameter
from qiski... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from sklearn.datasets import make_blobs
# example dataset
features, labels = make_blobs(n_samples=20, n_features=2, centers=2, random_state=3, shuffle=True)
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
features = MinMaxScaler(featur... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | # External imports
from pylab import cm
from sklearn import metrics
import numpy as np
import matplotlib.pyplot as plt
# Qiskit imports
from qiskit import QuantumCircuit
from qiskit.circuit import ParameterVector
from qiskit.visualization import circuit_drawer
from qiskit.algorithms.optimizers import SPSA
f... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import matplotlib.pyplot as plt
import numpy as np
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit.library import RealAmplitudes
from qiskit.primitives import Sampler
from qiskit.utils import algorithm_globals
from sklearn.model_selection import train_test_split
from sklearn.preprocessing imp... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | # Necessary imports
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import clear_output
from qiskit import QuantumCircuit
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit.library import ZFeatureMap, RealAmplitudes
from qiskit.utils import algorithm_globals
from sklearn... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import json
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import clear_output
from qiskit import QuantumCircuit
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit import ParameterVector
from qiskit.circuit.library import ZFeatureMap
from qiskit.quantum_info import Spar... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import json
import time
import warnings
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import clear_output
from qiskit import ClassicalRegister, QuantumRegister
from qiskit import QuantumCircuit
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit.library import RealAm... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from qiskit.utils import algorithm_globals
algorithm_globals.random_seed = 123456
from sklearn.datasets import make_blobs
features, labels = make_blobs(
n_samples=20,
centers=2,
center_box=(-1, 1),
cluster_std=0.1,
random_state=algorithm_globals.random_seed,
)
from qiskit import Ba... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from qiskit.utils import algorithm_globals
algorithm_globals.random_seed = 42
from qiskit.circuit import Parameter
from qiskit import QuantumCircuit
params1 = [Parameter("input1"), Parameter("weight1")]
qc1 = QuantumCircuit(1)
qc1.h(0)
qc1.ry(params1[0], 0)
qc1.rx(params1[1], 0)
qc1.draw("mpl")
from q... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from sklearn.datasets import load_iris
iris_data = load_iris()
print(iris_data.DESCR)
features = iris_data.data
labels = iris_data.target
from sklearn.preprocessing import MinMaxScaler
features = MinMaxScaler().fit_transform(features)
import pandas as pd
import seaborn as sns
df = pd.DataFrame(ir... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import matplotlib.pyplot as plt
import numpy as np
from IPython.display import clear_output
from qiskit import QuantumCircuit
from qiskit.algorithms.optimizers import COBYLA, L_BFGS_B
from qiskit.circuit import Parameter
from qiskit.circuit.library import RealAmplitudes, ZZFeatureMap
from qiskit.utils import alg... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from qiskit.utils import algorithm_globals
algorithm_globals.random_seed = 12345
from qiskit_machine_learning.datasets import ad_hoc_data
adhoc_dimension = 2
train_features, train_labels, test_features, test_labels, adhoc_total = ad_hoc_data(
training_size=20,
test_size=5,
n=adhoc_dimension,
... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import torch
from qiskit.utils import algorithm_globals
algorithm_globals.random_seed = 123456
_ = torch.manual_seed(123456) # suppress output
import numpy as np
num_dim = 2
num_discrete_values = 8
num_qubits = num_dim * int(np.log2(num_discrete_values))
from scipy.stats import multivariate_normal
c... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | # Necessary imports
import numpy as np
import matplotlib.pyplot as plt
from torch import Tensor
from torch.nn import Linear, CrossEntropyLoss, MSELoss
from torch.optim import LBFGS
from qiskit import QuantumCircuit
from qiskit.utils import algorithm_globals
from qiskit.circuit import Parameter
from qiski... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from sklearn.datasets import make_blobs
# example dataset
features, labels = make_blobs(n_samples=20, n_features=2, centers=2, random_state=3, shuffle=True)
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
features = MinMaxScaler(featur... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | # External imports
from pylab import cm
from sklearn import metrics
import numpy as np
import matplotlib.pyplot as plt
# Qiskit imports
from qiskit import QuantumCircuit
from qiskit.circuit import ParameterVector
from qiskit.visualization import circuit_drawer
from qiskit.algorithms.optimizers import SPSA
f... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import matplotlib.pyplot as plt
import numpy as np
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit.library import RealAmplitudes
from qiskit.primitives import Sampler
from qiskit.utils import algorithm_globals
from sklearn.model_selection import train_test_split
from sklearn.preprocessing imp... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | # Necessary imports
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import clear_output
from qiskit import QuantumCircuit
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit.library import ZFeatureMap, RealAmplitudes
from qiskit.utils import algorithm_globals
from sklearn... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import json
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import clear_output
from qiskit import QuantumCircuit
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit import ParameterVector
from qiskit.circuit.library import ZFeatureMap
from qiskit.quantum_info import Spar... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import json
import time
import warnings
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import clear_output
from qiskit import ClassicalRegister, QuantumRegister
from qiskit import QuantumCircuit
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit.library import RealAm... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from qiskit.utils import algorithm_globals
algorithm_globals.random_seed = 123456
from sklearn.datasets import make_blobs
features, labels = make_blobs(
n_samples=20,
centers=2,
center_box=(-1, 1),
cluster_std=0.1,
random_state=algorithm_globals.random_seed,
)
from qiskit import Ba... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from qiskit.utils import algorithm_globals
algorithm_globals.random_seed = 42
from qiskit.circuit import Parameter
from qiskit import QuantumCircuit
params1 = [Parameter("input1"), Parameter("weight1")]
qc1 = QuantumCircuit(1)
qc1.h(0)
qc1.ry(params1[0], 0)
qc1.rx(params1[1], 0)
qc1.draw("mpl")
from q... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from sklearn.datasets import load_iris
iris_data = load_iris()
print(iris_data.DESCR)
features = iris_data.data
labels = iris_data.target
from sklearn.preprocessing import MinMaxScaler
features = MinMaxScaler().fit_transform(features)
import pandas as pd
import seaborn as sns
df = pd.DataFrame(ir... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import matplotlib.pyplot as plt
import numpy as np
from IPython.display import clear_output
from qiskit import QuantumCircuit
from qiskit.algorithms.optimizers import COBYLA, L_BFGS_B
from qiskit.circuit import Parameter
from qiskit.circuit.library import RealAmplitudes, ZZFeatureMap
from qiskit.utils import alg... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from qiskit.utils import algorithm_globals
algorithm_globals.random_seed = 12345
from qiskit_machine_learning.datasets import ad_hoc_data
adhoc_dimension = 2
train_features, train_labels, test_features, test_labels, adhoc_total = ad_hoc_data(
training_size=20,
test_size=5,
n=adhoc_dimension,
... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import torch
from qiskit.utils import algorithm_globals
algorithm_globals.random_seed = 123456
_ = torch.manual_seed(123456) # suppress output
import numpy as np
num_dim = 2
num_discrete_values = 8
num_qubits = num_dim * int(np.log2(num_discrete_values))
from scipy.stats import multivariate_normal
c... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | # Necessary imports
import numpy as np
import matplotlib.pyplot as plt
from torch import Tensor
from torch.nn import Linear, CrossEntropyLoss, MSELoss
from torch.optim import LBFGS
from qiskit import QuantumCircuit
from qiskit.utils import algorithm_globals
from qiskit.circuit import Parameter
from qiski... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from sklearn.datasets import make_blobs
# example dataset
features, labels = make_blobs(n_samples=20, n_features=2, centers=2, random_state=3, shuffle=True)
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
features = MinMaxScaler(featur... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | # External imports
from pylab import cm
from sklearn import metrics
import numpy as np
import matplotlib.pyplot as plt
# Qiskit imports
from qiskit import QuantumCircuit
from qiskit.circuit import ParameterVector
from qiskit.visualization import circuit_drawer
from qiskit.algorithms.optimizers import SPSA
f... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import matplotlib.pyplot as plt
import numpy as np
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit.library import RealAmplitudes
from qiskit.primitives import Sampler
from qiskit.utils import algorithm_globals
from sklearn.model_selection import train_test_split
from sklearn.preprocessing imp... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | # Necessary imports
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import clear_output
from qiskit import QuantumCircuit
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit.library import ZFeatureMap, RealAmplitudes
from qiskit.utils import algorithm_globals
from sklearn... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import json
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import clear_output
from qiskit import QuantumCircuit
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit import ParameterVector
from qiskit.circuit.library import ZFeatureMap
from qiskit.quantum_info import Spar... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import json
import time
import warnings
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import clear_output
from qiskit import ClassicalRegister, QuantumRegister
from qiskit import QuantumCircuit
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit.library import RealAm... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from qiskit.utils import algorithm_globals
algorithm_globals.random_seed = 123456
from sklearn.datasets import make_blobs
features, labels = make_blobs(
n_samples=20,
centers=2,
center_box=(-1, 1),
cluster_std=0.1,
random_state=algorithm_globals.random_seed,
)
from qiskit import Ba... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from qiskit.utils import algorithm_globals
algorithm_globals.random_seed = 42
from qiskit.circuit import Parameter
from qiskit import QuantumCircuit
params1 = [Parameter("input1"), Parameter("weight1")]
qc1 = QuantumCircuit(1)
qc1.h(0)
qc1.ry(params1[0], 0)
qc1.rx(params1[1], 0)
qc1.draw("mpl")
from q... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from sklearn.datasets import load_iris
iris_data = load_iris()
print(iris_data.DESCR)
features = iris_data.data
labels = iris_data.target
from sklearn.preprocessing import MinMaxScaler
features = MinMaxScaler().fit_transform(features)
import pandas as pd
import seaborn as sns
df = pd.DataFrame(ir... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import matplotlib.pyplot as plt
import numpy as np
from IPython.display import clear_output
from qiskit import QuantumCircuit
from qiskit.algorithms.optimizers import COBYLA, L_BFGS_B
from qiskit.circuit import Parameter
from qiskit.circuit.library import RealAmplitudes, ZZFeatureMap
from qiskit.utils import alg... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from qiskit.utils import algorithm_globals
algorithm_globals.random_seed = 12345
from qiskit_machine_learning.datasets import ad_hoc_data
adhoc_dimension = 2
train_features, train_labels, test_features, test_labels, adhoc_total = ad_hoc_data(
training_size=20,
test_size=5,
n=adhoc_dimension,
... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import torch
from qiskit.utils import algorithm_globals
algorithm_globals.random_seed = 123456
_ = torch.manual_seed(123456) # suppress output
import numpy as np
num_dim = 2
num_discrete_values = 8
num_qubits = num_dim * int(np.log2(num_discrete_values))
from scipy.stats import multivariate_normal
c... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | # Necessary imports
import numpy as np
import matplotlib.pyplot as plt
from torch import Tensor
from torch.nn import Linear, CrossEntropyLoss, MSELoss
from torch.optim import LBFGS
from qiskit import QuantumCircuit
from qiskit.utils import algorithm_globals
from qiskit.circuit import Parameter
from qiski... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from sklearn.datasets import make_blobs
# example dataset
features, labels = make_blobs(n_samples=20, n_features=2, centers=2, random_state=3, shuffle=True)
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
features = MinMaxScaler(featur... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | # External imports
from pylab import cm
from sklearn import metrics
import numpy as np
import matplotlib.pyplot as plt
# Qiskit imports
from qiskit import QuantumCircuit
from qiskit.circuit import ParameterVector
from qiskit.visualization import circuit_drawer
from qiskit.algorithms.optimizers import SPSA
f... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import matplotlib.pyplot as plt
import numpy as np
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit.library import RealAmplitudes
from qiskit.primitives import Sampler
from qiskit.utils import algorithm_globals
from sklearn.model_selection import train_test_split
from sklearn.preprocessing imp... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | # Necessary imports
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import clear_output
from qiskit import QuantumCircuit
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit.library import ZFeatureMap, RealAmplitudes
from qiskit.utils import algorithm_globals
from sklearn... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import json
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import clear_output
from qiskit import QuantumCircuit
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit import ParameterVector
from qiskit.circuit.library import ZFeatureMap
from qiskit.quantum_info import Spar... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import json
import time
import warnings
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import clear_output
from qiskit import ClassicalRegister, QuantumRegister
from qiskit import QuantumCircuit
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit.library import RealAm... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from qiskit.utils import algorithm_globals
algorithm_globals.random_seed = 123456
from sklearn.datasets import make_blobs
features, labels = make_blobs(
n_samples=20,
centers=2,
center_box=(-1, 1),
cluster_std=0.1,
random_state=algorithm_globals.random_seed,
)
from qiskit import Ba... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from qiskit.utils import algorithm_globals
algorithm_globals.random_seed = 42
from qiskit.circuit import Parameter
from qiskit import QuantumCircuit
params1 = [Parameter("input1"), Parameter("weight1")]
qc1 = QuantumCircuit(1)
qc1.h(0)
qc1.ry(params1[0], 0)
qc1.rx(params1[1], 0)
qc1.draw("mpl")
from q... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from sklearn.datasets import load_iris
iris_data = load_iris()
print(iris_data.DESCR)
features = iris_data.data
labels = iris_data.target
from sklearn.preprocessing import MinMaxScaler
features = MinMaxScaler().fit_transform(features)
import pandas as pd
import seaborn as sns
df = pd.DataFrame(ir... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import matplotlib.pyplot as plt
import numpy as np
from IPython.display import clear_output
from qiskit import QuantumCircuit
from qiskit.algorithms.optimizers import COBYLA, L_BFGS_B
from qiskit.circuit import Parameter
from qiskit.circuit.library import RealAmplitudes, ZZFeatureMap
from qiskit.utils import alg... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from qiskit.utils import algorithm_globals
algorithm_globals.random_seed = 12345
from qiskit_machine_learning.datasets import ad_hoc_data
adhoc_dimension = 2
train_features, train_labels, test_features, test_labels, adhoc_total = ad_hoc_data(
training_size=20,
test_size=5,
n=adhoc_dimension,
... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import torch
from qiskit.utils import algorithm_globals
algorithm_globals.random_seed = 123456
_ = torch.manual_seed(123456) # suppress output
import numpy as np
num_dim = 2
num_discrete_values = 8
num_qubits = num_dim * int(np.log2(num_discrete_values))
from scipy.stats import multivariate_normal
c... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | # Necessary imports
import numpy as np
import matplotlib.pyplot as plt
from torch import Tensor
from torch.nn import Linear, CrossEntropyLoss, MSELoss
from torch.optim import LBFGS
from qiskit import QuantumCircuit
from qiskit.utils import algorithm_globals
from qiskit.circuit import Parameter
from qiski... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | from sklearn.datasets import make_blobs
# example dataset
features, labels = make_blobs(n_samples=20, n_features=2, centers=2, random_state=3, shuffle=True)
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
features = MinMaxScaler(featur... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | # External imports
from pylab import cm
from sklearn import metrics
import numpy as np
import matplotlib.pyplot as plt
# Qiskit imports
from qiskit import QuantumCircuit
from qiskit.circuit import ParameterVector
from qiskit.visualization import circuit_drawer
from qiskit.algorithms.optimizers import SPSA
f... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import matplotlib.pyplot as plt
import numpy as np
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit.library import RealAmplitudes
from qiskit.primitives import Sampler
from qiskit.utils import algorithm_globals
from sklearn.model_selection import train_test_split
from sklearn.preprocessing imp... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | # Necessary imports
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import clear_output
from qiskit import QuantumCircuit
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit.library import ZFeatureMap, RealAmplitudes
from qiskit.utils import algorithm_globals
from sklearn... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import json
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import clear_output
from qiskit import QuantumCircuit
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit import ParameterVector
from qiskit.circuit.library import ZFeatureMap
from qiskit.quantum_info import Spar... |
https://github.com/qiskit-community/qiskit-translations-staging | qiskit-community | import json
import time
import warnings
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import clear_output
from qiskit import ClassicalRegister, QuantumRegister
from qiskit import QuantumCircuit
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit.library import RealAm... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.