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https://github.com/qiskit-community/qiskit-translations-staging
|
qiskit-community
|
import numpy as np
from qiskit import QuantumCircuit, transpile
from qiskit.quantum_info import Kraus, SuperOp
from qiskit_aer import AerSimulator
from qiskit.tools.visualization import plot_histogram
# Import from Qiskit Aer noise module
from qiskit_aer.noise import (NoiseModel, QuantumError, ReadoutError,
pauli_error, depolarizing_error, thermal_relaxation_error)
# Construct a 1-qubit bit-flip and phase-flip errors
p_error = 0.05
bit_flip = pauli_error([('X', p_error), ('I', 1 - p_error)])
phase_flip = pauli_error([('Z', p_error), ('I', 1 - p_error)])
print(bit_flip)
print(phase_flip)
# Compose two bit-flip and phase-flip errors
bitphase_flip = bit_flip.compose(phase_flip)
print(bitphase_flip)
# Tensor product two bit-flip and phase-flip errors with
# bit-flip on qubit-0, phase-flip on qubit-1
error2 = phase_flip.tensor(bit_flip)
print(error2)
# Convert to Kraus operator
bit_flip_kraus = Kraus(bit_flip)
print(bit_flip_kraus)
# Convert to Superoperator
phase_flip_sop = SuperOp(phase_flip)
print(phase_flip_sop)
# Convert back to a quantum error
print(QuantumError(bit_flip_kraus))
# Check conversion is equivalent to original error
QuantumError(bit_flip_kraus) == bit_flip
# Measurement miss-assignement probabilities
p0given1 = 0.1
p1given0 = 0.05
ReadoutError([[1 - p1given0, p1given0], [p0given1, 1 - p0given1]])
# Create an empty noise model
noise_model = NoiseModel()
# Add depolarizing error to all single qubit u1, u2, u3 gates
error = depolarizing_error(0.05, 1)
noise_model.add_all_qubit_quantum_error(error, ['u1', 'u2', 'u3'])
# Print noise model info
print(noise_model)
# Create an empty noise model
noise_model = NoiseModel()
# Add depolarizing error to all single qubit u1, u2, u3 gates on qubit 0 only
error = depolarizing_error(0.05, 1)
noise_model.add_quantum_error(error, ['u1', 'u2', 'u3'], [0])
# Print noise model info
print(noise_model)
# System Specification
n_qubits = 4
circ = QuantumCircuit(n_qubits)
# Test Circuit
circ.h(0)
for qubit in range(n_qubits - 1):
circ.cx(qubit, qubit + 1)
circ.measure_all()
print(circ)
# Ideal simulator and execution
sim_ideal = AerSimulator()
result_ideal = sim_ideal.run(circ).result()
plot_histogram(result_ideal.get_counts(0))
# Example error probabilities
p_reset = 0.03
p_meas = 0.1
p_gate1 = 0.05
# QuantumError objects
error_reset = pauli_error([('X', p_reset), ('I', 1 - p_reset)])
error_meas = pauli_error([('X',p_meas), ('I', 1 - p_meas)])
error_gate1 = pauli_error([('X',p_gate1), ('I', 1 - p_gate1)])
error_gate2 = error_gate1.tensor(error_gate1)
# Add errors to noise model
noise_bit_flip = NoiseModel()
noise_bit_flip.add_all_qubit_quantum_error(error_reset, "reset")
noise_bit_flip.add_all_qubit_quantum_error(error_meas, "measure")
noise_bit_flip.add_all_qubit_quantum_error(error_gate1, ["u1", "u2", "u3"])
noise_bit_flip.add_all_qubit_quantum_error(error_gate2, ["cx"])
print(noise_bit_flip)
# Create noisy simulator backend
sim_noise = AerSimulator(noise_model=noise_bit_flip)
# Transpile circuit for noisy basis gates
circ_tnoise = transpile(circ, sim_noise)
# Run and get counts
result_bit_flip = sim_noise.run(circ_tnoise).result()
counts_bit_flip = result_bit_flip.get_counts(0)
# Plot noisy output
plot_histogram(counts_bit_flip)
# T1 and T2 values for qubits 0-3
T1s = np.random.normal(50e3, 10e3, 4) # Sampled from normal distribution mean 50 microsec
T2s = np.random.normal(70e3, 10e3, 4) # Sampled from normal distribution mean 50 microsec
# Truncate random T2s <= T1s
T2s = np.array([min(T2s[j], 2 * T1s[j]) for j in range(4)])
# Instruction times (in nanoseconds)
time_u1 = 0 # virtual gate
time_u2 = 50 # (single X90 pulse)
time_u3 = 100 # (two X90 pulses)
time_cx = 300
time_reset = 1000 # 1 microsecond
time_measure = 1000 # 1 microsecond
# QuantumError objects
errors_reset = [thermal_relaxation_error(t1, t2, time_reset)
for t1, t2 in zip(T1s, T2s)]
errors_measure = [thermal_relaxation_error(t1, t2, time_measure)
for t1, t2 in zip(T1s, T2s)]
errors_u1 = [thermal_relaxation_error(t1, t2, time_u1)
for t1, t2 in zip(T1s, T2s)]
errors_u2 = [thermal_relaxation_error(t1, t2, time_u2)
for t1, t2 in zip(T1s, T2s)]
errors_u3 = [thermal_relaxation_error(t1, t2, time_u3)
for t1, t2 in zip(T1s, T2s)]
errors_cx = [[thermal_relaxation_error(t1a, t2a, time_cx).expand(
thermal_relaxation_error(t1b, t2b, time_cx))
for t1a, t2a in zip(T1s, T2s)]
for t1b, t2b in zip(T1s, T2s)]
# Add errors to noise model
noise_thermal = NoiseModel()
for j in range(4):
noise_thermal.add_quantum_error(errors_reset[j], "reset", [j])
noise_thermal.add_quantum_error(errors_measure[j], "measure", [j])
noise_thermal.add_quantum_error(errors_u1[j], "u1", [j])
noise_thermal.add_quantum_error(errors_u2[j], "u2", [j])
noise_thermal.add_quantum_error(errors_u3[j], "u3", [j])
for k in range(4):
noise_thermal.add_quantum_error(errors_cx[j][k], "cx", [j, k])
print(noise_thermal)
# Run the noisy simulation
sim_thermal = AerSimulator(noise_model=noise_thermal)
# Transpile circuit for noisy basis gates
circ_tthermal = transpile(circ, sim_thermal)
# Run and get counts
result_thermal = sim_thermal.run(circ_tthermal).result()
counts_thermal = result_thermal.get_counts(0)
# Plot noisy output
plot_histogram(counts_thermal)
import qiskit.tools.jupyter
%qiskit_version_table
%qiskit_copyright
|
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 LogNormalDistribution
# number of qubits to represent the uncertainty
num_uncertainty_qubits = 3
# parameters for considered random distribution
S = 2.0 # initial spot price
vol = 0.4 # volatility of 40%
r = 0.05 # annual interest rate of 4%
T = 40 / 365 # 40 days to maturity
# resulting parameters for log-normal distribution
mu = (r - 0.5 * vol**2) * T + np.log(S)
sigma = vol * np.sqrt(T)
mean = np.exp(mu + sigma**2 / 2)
variance = (np.exp(sigma**2) - 1) * np.exp(2 * mu + sigma**2)
stddev = np.sqrt(variance)
# lowest and highest value considered for the spot price; in between, an equidistant discretization is considered.
low = np.maximum(0, mean - 3 * stddev)
high = mean + 3 * stddev
# construct A operator for QAE for the payoff function by
# composing the uncertainty model and the objective
uncertainty_model = LogNormalDistribution(
num_uncertainty_qubits, mu=mu, sigma=sigma**2, bounds=(low, high)
)
# plot probability distribution
x = uncertainty_model.values
y = uncertainty_model.probabilities
plt.bar(x, y, width=0.2)
plt.xticks(x, size=15, rotation=90)
plt.yticks(size=15)
plt.grid()
plt.xlabel("Spot Price at Maturity $S_T$ (\$)", size=15)
plt.ylabel("Probability ($\%$)", size=15)
plt.show()
# set the strike price (should be within the low and the high value of the uncertainty)
strike_price = 2.126
# set the approximation scaling for the payoff function
rescaling_factor = 0.25
# setup piecewise linear objective fcuntion
breakpoints = [low, strike_price]
slopes = [-1, 0]
offsets = [strike_price - low, 0]
f_min = 0
f_max = strike_price - low
european_put_objective = LinearAmplitudeFunction(
num_uncertainty_qubits,
slopes,
offsets,
domain=(low, high),
image=(f_min, f_max),
breakpoints=breakpoints,
rescaling_factor=rescaling_factor,
)
# construct A operator for QAE for the payoff function by
# composing the uncertainty model and the objective
european_put = european_put_objective.compose(uncertainty_model, front=True)
# plot exact payoff function (evaluated on the grid of the uncertainty model)
x = uncertainty_model.values
y = np.maximum(0, strike_price - x)
plt.plot(x, y, "ro-")
plt.grid()
plt.title("Payoff Function", size=15)
plt.xlabel("Spot Price", size=15)
plt.ylabel("Payoff", size=15)
plt.xticks(x, size=15, rotation=90)
plt.yticks(size=15)
plt.show()
# evaluate exact expected value (normalized to the [0, 1] interval)
exact_value = np.dot(uncertainty_model.probabilities, y)
exact_delta = -sum(uncertainty_model.probabilities[x <= strike_price])
print("exact expected value:\t%.4f" % exact_value)
print("exact delta value: \t%.4f" % exact_delta)
# set target precision and confidence level
epsilon = 0.01
alpha = 0.05
problem = EstimationProblem(
state_preparation=european_put,
objective_qubits=[num_uncertainty_qubits],
post_processing=european_put_objective.post_processing,
)
# construct amplitude estimation
ae = IterativeAmplitudeEstimation(
epsilon_target=epsilon, alpha=alpha, sampler=Sampler(run_options={"shots": 100})
)
result = ae.estimate(problem)
conf_int = np.array(result.confidence_interval_processed)
print("Exact value: \t%.4f" % exact_value)
print("Estimated value: \t%.4f" % (result.estimation_processed))
print("Confidence interval:\t[%.4f, %.4f]" % tuple(conf_int))
# setup piecewise linear objective fcuntion
breakpoints = [low, strike_price]
slopes = [0, 0]
offsets = [1, 0]
f_min = 0
f_max = 1
european_put_delta_objective = LinearAmplitudeFunction(
num_uncertainty_qubits,
slopes,
offsets,
domain=(low, high),
image=(f_min, f_max),
breakpoints=breakpoints,
)
# construct circuit for payoff function
european_put_delta = european_put_delta_objective.compose(uncertainty_model, front=True)
# set target precision and confidence level
epsilon = 0.01
alpha = 0.05
problem = EstimationProblem(
state_preparation=european_put_delta, objective_qubits=[num_uncertainty_qubits]
)
# construct amplitude estimation
ae_delta = IterativeAmplitudeEstimation(
epsilon_target=epsilon, alpha=alpha, sampler=Sampler(run_options={"shots": 100})
)
result_delta = ae_delta.estimate(problem)
conf_int = -np.array(result_delta.confidence_interval)[::-1]
print("Exact delta: \t%.4f" % exact_delta)
print("Esimated value: \t%.4f" % -result_delta.estimation)
print("Confidence interval: \t[%.4f, %.4f]" % tuple(conf_int))
import qiskit.tools.jupyter
%qiskit_version_table
%qiskit_copyright
|
https://github.com/urwin419/QiskitChecker
|
urwin419
|
from qiskit import QuantumCircuit
def create_bell_pair():
qc = QuantumCircuit(2)
qc.h(1)
qc.cx(1, 0)
return qc
def encode_message(qc, qubit, msg):
if len(msg) != 2 or not set([0,1]).issubset({0,1}):
raise ValueError(f"message '{msg}' is invalid")
if msg[1] == "1":
qc.x(qubit)
if msg[0] == "1":
qc.z(qubit)
return qc
def decode_message(qc):
qc.cx(1, 0)
### added y gate ###
qc.y(0)
qc.h(1)
return qc
|
https://github.com/DaisukeIto-ynu/KosakaQ_client
|
DaisukeIto-ynu
|
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 17 15:00:00 2022
@author: Yokohama National University, Kosaka Lab
"""
import warnings
import requests
from qiskit import qobj as qobj_mod
from qiskit.circuit.parameter import Parameter
from qiskit.circuit.library import IGate, XGate, YGate, ZGate, HGate, SGate, SdgGate, SXGate, SXdgGate
from qiskit.circuit.measure import Measure
from qiskit.providers import BackendV2 as Backend
from qiskit.providers import Options
from qiskit.transpiler import Target
from qiskit.providers.models import BackendConfiguration
from qiskit.exceptions import QiskitError
import kosakaq_job
import circuit_to_kosakaq
class KosakaQBackend(Backend):
def __init__(
self,
provider,
name,
url,
version,
n_qubits,
max_shots=4096,
max_experiments=1,
):
super().__init__(provider=provider, name=name)
self.url = url
self.version = version
self.n_qubits = n_qubits
self.max_shots = max_shots
self.max_experiments = max_experiments
self._configuration = BackendConfiguration.from_dict({
'backend_name': name,
'backend_version': version,
'url': self.url,
'simulator': False,
'local': False,
'coupling_map': None,
'description': 'KosakaQ Diamond device',
'basis_gates': ['i', 'x', 'y', 'z', 'h', 's', 'sdg', 'sx', 'sxdg'], #後で修正が必要
'memory': False,
'n_qubits': n_qubits,
'conditional': False,
'max_shots': max_shots,
'max_experiments': max_experiments,
'open_pulse': False,
'gates': [
{
'name': 'TODO',
'parameters': [],
'qasm_def': 'TODO'
}
]
})
self._target = Target(num_qubits=n_qubits)
# theta = Parameter('θ')
# phi = Parameter('ϕ')
# lam = Parameter('λ')
self._target.add_instruction(IGate())
self._target.add_instruction(XGate())
self._target.add_instruction(YGate())
self._target.add_instruction(ZGate())
self._target.add_instruction(HGate())
self._target.add_instruction(SGate())
self._target.add_instruction(SdgGate())
self._target.add_instruction(SXGate())
self._target.add_instruction(SXdgGate())
self._target.add_instruction(Measure())
self.options.set_validator("shots", (1,max_shots))
def configuration(self):
warnings.warn("The configuration() method is deprecated and will be removed in a "
"future release. Instead you should access these attributes directly "
"off the object or via the .target attribute. You can refer to qiskit "
"backend interface transition guide for the exact changes: "
"https://qiskit.org/documentation/apidoc/providers.html#backendv1-backendv2",
DeprecationWarning)
return self._configuration
def properties(self):
warnings.warn("The properties() method is deprecated and will be removed in a "
"future release. Instead you should access these attributes directly "
"off the object or via the .target attribute. You can refer to qiskit "
"backend interface transition guide for the exact changes: "
"https://qiskit.org/documentation/apidoc/providers.html#backendv1-backendv2",
DeprecationWarning)
@property
def max_circuits(self):
return 1
@property
def target(self):
return self._target
@classmethod
def _default_options(cls):
return Options(shots=4096)
def run(self, circuit, **kwargs):
if isinstance(circuit, qobj_mod.PulseQobj):
raise QiskitError("Pulse jobs are not accepted")
for kwarg in kwargs:
if kwarg != 'shots':
warnings.warn(
"Option %s is not used by this backend" % kwarg,
UserWarning, stacklevel=2)
out_shots = kwargs.get('shots', self.options.shots)
if out_shots > self.configuration().max_shots:
raise ValueError('Number of shots is larger than maximum '
'number of shots')
kosakaq_json = circuit_to_kosakaq.circuit_to_KosakaQ(circuit, self._provider.access_token, shots=out_shots, backend=self.name)[0]
header = {
"Authorization": "token " + self._provider.access_token,
"SDK": "qiskit"
}
res = requests.post(self.url+r"/job/", data=kosakaq_json, headers=header)
res.raise_for_status()
response = res.json()
if 'id' not in response:
raise Exception
job = kosakaq_job.KosakaQJob(self, response['id'], access_token=self.provider.access_token, qobj=circuit)
return job
|
https://github.com/maheshwaripranav/Qiskit-Fall-Fest-Kolkata-Hackathon
|
maheshwaripranav
|
import numpy as np
# Importing standard Qiskit libraries
from qiskit import QuantumCircuit, transpile, Aer, IBMQ, QuantumRegister, ClassicalRegister
from qiskit.tools.jupyter import *
from qiskit.visualization import *
from ibm_quantum_widgets import *
from qiskit.providers.aer import QasmSimulator
import qiskit
import copy
import matplotlib.pyplot as plt
# Loading your IBM Quantum account(s)
provider = IBMQ.load_account()
from sklearn import model_selection, datasets, svm
linnerrud = datasets.load_linnerrud()
X = linnerrud.data[0:100]
Y = linnerrud.target[0:100]
X_train, X_test, Y_train, Y_test = model_selection.train_test_split(X, Y, test_size = 0.33, random_state = 42)
print(Y_train)
print(X_train[0])
N = 4
def feature_map(X):
q = QuantumRegister(N)
c = ClassicalRegister(1)
qc = QuantumCircuit(q,c)
for i, x in enumerate(X_train[0]):
qc.rx(x,i)
return qc,c
def variational_circuit(qc, theta):
for i in range(N-1):
qc.cnot(i, i+1)
qc.cnot(N-1, 0)
for i in range(N):
qc.ry(theta[i], i)
return qc
def quantum_nn(X, theta, simulator = True):
qc,c = feature_map(X)
qc = variational_circuit(qc, theta)
qc.measure(0,c)
shots = 1E4
backend = Aer.get_backend('qasm_simulator')
if simulator == False:
shots = 5000
provider = IBMQ.load_account()
backend = provider.get_backend('ibmq_athens')
job = qiskit.execute(qc, backend, shots = shots)
result = job.result()
counts = result.get_counts(qc)
print(counts)
return(counts['1']/shots)
def loss(prediction, target):
return (prediction-target)**2
def gradient(X, Y, theta):
delta = 0.01
grad = []
for i in range(len(theta)):
dtheta = copy.copy(theta)
dtheta += delta
pred_1 = quantum_nn(X, dtheta)
pred_2 = quantum_nn(X, theta)
grad.append((loss(pred_1, Y) - loss(pred_2, Y))/delta)
return np.array(grad)
def accuracy(X, Y, theta):
counter = 0
for X_i, Y_i in zip(X, Y):
prediction = quantum_nn(X_i, theta)
if prediction < 0.5 and Y_i == 0:
counter += 1
if prediction >= 0.5 and Y_i == 1:
counter += 1
return counter/len(Y)
eta = 0.05
loss_list = []
theta = np.ones(N)
print('Epoch\t Loss\t Training Accuracy')
for i in range(20):
loss_tmp = []
for X_i, Y_i in zip(X_train, Y_train):
prediction = quantum_nn(X_i, theta)
loss_tmp.append(loss(prediction, Y_i))
theta = theta - eta * gradient(X_i, Y_i, theta)
loss_list.append(np.mean(loss_tmp))
acc = accuracy(X_train, Y_train, theta)
print(f'{i} \t {loss_list[-1]:0.3f} \t {acc:0.3f}')
plt.plot(loss_list)
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.show
accuracy(X_test, Y_test, theta)
clf = svm.SVC()
clf.fit(X_train, Y_train)
print(clf.predict(X_test))
print(Y_test)
quantum_nn(X_test[6], theta, simulator = False)
quantum_nn(X_test[6], theta)
Y_test[6]
|
https://github.com/shesha-raghunathan/DATE2019-qiskit-tutorial
|
shesha-raghunathan
|
#
# your solution is here
#
# let's import all necessary objects and methods for quantum circuits
from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit, execute, Aer
from qiskit.tools.visualization import matplotlib_circuit_drawer as drawer
# let's import randrange for random choices
from random import randrange
#
# your code is here
#
|
https://github.com/qiskit-community/qiskit-translations-staging
|
qiskit-community
|
from qiskit_nature.second_q.drivers import PySCFDriver
driver = PySCFDriver(
atom="H 0 0 0; H 0 0 0.72" # Two Hydrogen atoms, 0.72 Angstrom apart
)
molecule = driver.run()
from qiskit_nature.second_q.mappers import QubitConverter, ParityMapper
qubit_converter = QubitConverter(ParityMapper())
hamiltonian = qubit_converter.convert(molecule.second_q_ops()[0])
from qiskit.algorithms.minimum_eigensolvers import NumPyMinimumEigensolver
sol = NumPyMinimumEigensolver().compute_minimum_eigenvalue(hamiltonian)
real_solution = molecule.interpret(sol)
real_solution.groundenergy
from qiskit_ibm_runtime import QiskitRuntimeService, Estimator, Session, Options
service = QiskitRuntimeService()
backend = "ibmq_qasm_simulator"
from qiskit.algorithms.minimum_eigensolvers import VQE
# Use RealAmplitudes circuit to create trial states
from qiskit.circuit.library import RealAmplitudes
ansatz = RealAmplitudes(num_qubits=2, reps=2)
# Search for better states using SPSA algorithm
from qiskit.algorithms.optimizers import SPSA
optimizer = SPSA(150)
# Set a starting point for reproduceability
import numpy as np
np.random.seed(6)
initial_point = np.random.uniform(-np.pi, np.pi, 12)
# Create an object to store intermediate results
from dataclasses import dataclass
@dataclass
class VQELog:
values: list
parameters: list
def update(self, count, parameters, mean, _metadata):
self.values.append(mean)
self.parameters.append(parameters)
print(f"Running circuit {count} of ~350", end="\r", flush=True)
log = VQELog([],[])
# Main calculation
with Session(service=service, backend=backend) as session:
options = Options()
options.optimization_level = 3
vqe = VQE(Estimator(session=session, options=options),
ansatz, optimizer, callback=log.update, initial_point=initial_point)
result = vqe.compute_minimum_eigenvalue(hamiltonian)
print("Experiment complete.".ljust(30))
print(f"Raw result: {result.optimal_value}")
if 'simulator' not in backend:
# Run once with ZNE error mitigation
options.resilience_level = 2
vqe = VQE(Estimator(session=session, options=options),
ansatz, SPSA(1), initial_point=result.optimal_point)
result = vqe.compute_minimum_eigenvalue(hamiltonian)
print(f"Mitigated result: {result.optimal_value}")
import matplotlib.pyplot as plt
plt.rcParams["font.size"] = 14
# Plot energy and reference value
plt.figure(figsize=(12, 6))
plt.plot(log.values, label="Estimator VQE")
plt.axhline(y=real_solution.groundenergy, color="tab:red", ls="--", label="Target")
plt.legend(loc="best")
plt.xlabel("Iteration")
plt.ylabel("Energy [H]")
plt.title("VQE energy")
plt.show()
import qiskit_ibm_runtime
qiskit_ibm_runtime.version.get_version_info()
import qiskit.tools.jupyter
%qiskit_version_table
%qiskit_copyright
|
https://github.com/pyRiemann/pyRiemann-qiskit
|
pyRiemann
|
import logging
import numpy as np
from qiskit import ClassicalRegister, QuantumRegister, QuantumCircuit
from qiskit.circuit.library import RealAmplitudes
from qiskit.quantum_info import Statevector
from qiskit_algorithms.optimizers import SPSA
from qiskit_algorithms.utils import algorithm_globals
from qiskit_machine_learning.circuit.library import RawFeatureVector
from qiskit_machine_learning.neural_networks import SamplerQNN
from sklearn.base import TransformerMixin
def _ansatz(num_qubits):
return RealAmplitudes(num_qubits, reps=5)
def _auto_encoder_circuit(num_latent, num_trash):
qr = QuantumRegister(num_latent + 2 * num_trash + 1, "q")
cr = ClassicalRegister(1, "c")
circuit = QuantumCircuit(qr, cr)
circuit.compose(
_ansatz(num_latent + num_trash), range(0, num_latent + num_trash), inplace=True
)
circuit.barrier()
auxiliary_qubit = num_latent + 2 * num_trash
# swap test
circuit.h(auxiliary_qubit)
for i in range(num_trash):
circuit.cswap(auxiliary_qubit, num_latent + i, num_latent + num_trash + i)
circuit.h(auxiliary_qubit)
circuit.measure(auxiliary_qubit, cr[0])
return circuit
class BasicQnnAutoencoder(TransformerMixin):
"""Quantum denoising
This class implements a quantum auto encoder.
The implementation was adapted from [1]_, to be compatible with scikit-learn.
Parameters
----------
num_latent : int, default=3
The number of qubits in the latent space.
num_trash : int, default=2
The number of qubits in the trash space.
opt : Optimizer, default=SPSA(maxiter=100, blocking=True)
The classical optimizer to use.
callback : Callable[int, double], default=None
An additional callback for the optimizer.
The first parameter is the number of cost evaluation call.
The second parameter is the cost.
Notes
-----
.. versionadded:: 0.3.0
Attributes
----------
costs_ : list
The values of the cost function.
fidelities_ : list, shape (n_samples,)
fidelities (one fidelity for each sample).
References
----------
.. [1] \
https://qiskit-community.github.io/qiskit-machine-learning/tutorials/12_quantum_autoencoder.html
.. [2] A. Mostafa et al., 2024 \
‘Quantum Denoising in the Realm of Brain-Computer Interfaces:
A Preliminary Study’,
https://hal.science/hal-04501908
"""
def __init__(
self,
num_latent=3,
num_trash=2,
opt=SPSA(maxiter=100, blocking=True),
callback=None,
):
self.num_latent = num_latent
self.num_trash = num_trash
self.opt = opt
self.callback = callback
def _log(self, msg):
logging.info(f"[BasicQnnAutoencoder] {msg}")
def _get_transformer(self):
# encoder
transformer = QuantumCircuit(self.n_qubits)
transformer = transformer.compose(self._feature_map)
ansatz_qc = _ansatz(self.n_qubits)
transformer = transformer.compose(ansatz_qc)
transformer.barrier()
# trash space
for i in range(self.num_trash):
transformer.reset(self.num_latent + i)
transformer.barrier()
# decoder
transformer = transformer.compose(ansatz_qc.inverse())
self._transformer = transformer
return transformer
def _compute_fidelities(self, X):
fidelities = []
for x in X:
param_values = np.concatenate((x, self._opt_result.x))
output_qc = self._transformer.assign_parameters(param_values)
output_state = Statevector(output_qc).data
original_qc = self._feature_map.assign_parameters(x)
original_state = Statevector(original_qc).data
fidelity = np.sqrt(np.dot(original_state.conj(), output_state) ** 2)
fidelities.append(fidelity.real)
return fidelities
@property
def n_qubits(self):
return self.num_latent + self.num_trash
def fit(self, X, _y=None, **kwargs):
"""Fit the autoencoder.
Parameters
----------
X : ndarray, shape (n_samples, n_features)
Set of time epochs.
n_features must equal 2 ** n_qubits,
where n_qubits = num_trash + num_latent.
Returns
-------
self : BasicQnnAutoencoder
The BasicQnnAutoencoder instance.
"""
_, n_features = X.shape
self.costs_ = []
self.fidelities_ = []
self._iter = 0
self._log(
f"raw feature size: {2 ** self.n_qubits} and feature size: {n_features}"
)
assert 2**self.n_qubits == n_features
self._feature_map = RawFeatureVector(2**self.n_qubits)
self._auto_encoder = _auto_encoder_circuit(self.num_latent, self.num_trash)
qc = QuantumCircuit(self.num_latent + 2 * self.num_trash + 1, 1)
qc = qc.compose(self._feature_map, range(self.n_qubits))
qc = qc.compose(self._auto_encoder)
qnn = SamplerQNN(
circuit=qc,
input_params=self._feature_map.parameters,
weight_params=self._auto_encoder.parameters,
interpret=lambda x: x,
output_shape=2,
)
def cost_func(params_values):
self._iter += 1
if self._iter % 10 == 0:
self._log(f"Iteration {self._iter}")
probabilities = qnn.forward(X, params_values)
cost = np.sum(probabilities[:, 1]) / X.shape[0]
self.costs_.append(cost)
if self.callback:
self.callback(self._iter, cost)
return cost
initial_point = algorithm_globals.random.random(
self._auto_encoder.num_parameters
)
self._opt_result = self.opt.minimize(fun=cost_func, x0=initial_point)
# encoder/decoder circuit
self._transformer = self._get_transformer()
# compute fidelity
self.fidelities_ = self._compute_fidelities(X)
self._log(f"Mean fidelity: {np.mean(self.fidelities_)}")
return self
def transform(self, X, **kwargs):
"""Apply the transformer circuit.
Parameters
----------
X : ndarray, shape (n_samples, n_features)
Set of time epochs.
n_features must equal 2 ** n_qubits,
where n_qubits = num_trash + num_latent.
Returns
-------
outputs : ndarray, shape (n_samples, 2 ** n_qubits)
The autocoded sample. n_qubits = num_trash + num_latent.
"""
_, n_features = X.shape
outputs = []
for x in X:
param_values = np.concatenate((x, self._opt_result.x))
output_qc = self._transformer.assign_parameters(param_values)
output_sv = Statevector(output_qc).data
output_sv = np.reshape(np.abs(output_sv) ** 2, n_features)
outputs.append(output_sv)
return np.array(outputs)
|
https://github.com/Z-928/Bugs4Q
|
Z-928
|
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
from qiskit.wrapper import available_backends, get_backend
from qiskit.wrapper import execute as q_execute
q = QuantumRegister(2, name='q')
c = ClassicalRegister(2, name='c')
qc = QuantumCircuit(q,c)
qc.h(q[0])
qc.h(q[1])
qc.cx(q[0], q[1])
qc.measure(q, c)
z = 0.995004165 + 1j * 0.099833417
z = z / abs(z)
u_error = np.array([[1, 0], [0, z]])
noise_params = {'U':
{'gate_time': 1,
'p_depol': 0.001,
'p_pauli': [0, 0, 0.01],
'U_error': u_error
}
}
config = {"noise_params": noise_params}
ret = q_execute(qc, 'local_qasm_simulator_cpp', shots=1024, config=config)
ret = ret.result()
print(ret.get_counts())
|
https://github.com/usamisaori/quantum-expressibility-entangling-capability
|
usamisaori
|
from qiskit import *
from oracle_generation import generate_oracle
get_bin = lambda x, n: format(x, 'b').zfill(n)
def gen_circuits(min,max,size):
circuits = []
secrets = []
ORACLE_SIZE = size
for i in range(min,max+1):
cur_str = get_bin(i,ORACLE_SIZE-1)
(circuit, secret) = generate_oracle(ORACLE_SIZE,False,3,cur_str)
circuits.append(circuit)
secrets.append(secret)
return (circuits, secrets)
|
https://github.com/tomtuamnuq/compare-qiskit-ocean
|
tomtuamnuq
|
import time
from typing import Tuple
import dimod
from dwave.system import DWaveSampler, EmbeddingComposite, DWaveCliqueSampler
from qiskit_optimization import QuadraticProgram
from qiskit_optimization.algorithms import CplexOptimizer
from qiskit_optimization.converters import LinearEqualityToPenalty
from utilities.helpers import create_quadratic_programs_from_paths
DIR = 'TEST_DATA' + "/" + '27_04_2021'
# select linear programs to solve
qps = create_quadratic_programs_from_paths(DIR + "/DENSE/")
qp_dense = qps['test_70']
qps = create_quadratic_programs_from_paths(DIR + "/SPARSE/")
qp_sparse = qps['test_100']
# init solvers
cplex = CplexOptimizer()
dwave_dense = DWaveCliqueSampler()
dwave_sparse = EmbeddingComposite(DWaveSampler())
# solve classically
for qp in (qp_dense, qp_sparse):
print(cplex.solve(qp))
def qp_to_bqm(qp: QuadraticProgram) -> dimod.BQM:
conv = LinearEqualityToPenalty()
qubo = conv.convert(qp)
bqm = dimod.as_bqm(qubo.objective.linear.to_array(), qubo.objective.quadratic.to_array(), dimod.BINARY)
return bqm
solver_parameters = {'num_reads': 1024, 'annealing_time': 3}
def solve_bqm(sampler: dimod.Sampler, bqm: dimod.BQM):
sampleset = sampler.sample(bqm, **solver_parameters)
return sampleset
qubo_dense = qp_to_bqm(qp_dense)
sampleset_dense = solve_bqm(dwave_dense, qubo_dense)
# 44d0d592-9107-4661-a6df-3e65836c1bda
sampleset_dense.to_pandas_dataframe()
def eval_bqm_sampleset(qp: QuadraticProgram, sampleset) -> Tuple[bool, int, float]:
x = sampleset.record.sample[0]
feasible = qp.is_feasible(x)
num_occur = sampleset.record.num_occurrences[0]
energy = sampleset.record.energy[0]
return feasible, num_occur, energy
res = eval_bqm_sampleset(qp_dense, sampleset_dense)
res
|
https://github.com/qiskit-community/qiskit-translations-staging
|
qiskit-community
|
from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit
q = QuantumRegister(1)
c = ClassicalRegister(1)
qc = QuantumCircuit(q, c)
qc.h(q)
qc.measure(q, c)
qc.draw(output='mpl', style={'backgroundcolor': '#EEEEEE'})
|
https://github.com/shesha-raghunathan/DATE2019-qiskit-tutorial
|
shesha-raghunathan
|
from datasets import *
from qiskit_aqua.utils import split_dataset_to_data_and_labels, map_label_to_class_name
from qiskit_aqua.input import SVMInput
from qiskit_aqua import run_algorithm
feature_dim = 2 # dimension of each data point
training_dataset_size = 20
testing_dataset_size = 10
sample_Total, training_input, test_input, class_labels = ad_hoc_data(training_size=training_dataset_size,
test_size=testing_dataset_size,
n=feature_dim, gap=0.3, PLOT_DATA=True)
datapoints, class_to_label = split_dataset_to_data_and_labels(test_input)
print(class_to_label)
params = {
'problem': {'name': 'svm_classification'},
'algorithm': {
'name': 'SVM'
}
}
algo_input = SVMInput(training_input, test_input, datapoints[0])
result = run_algorithm(params, algo_input)
print("kernel matrix during the training:")
kernel_matrix = result['kernel_matrix_training']
img = plt.imshow(np.asmatrix(kernel_matrix),interpolation='nearest',origin='upper',cmap='bone_r')
plt.show()
print("testing success ratio: ", result['testing_accuracy'])
print("predicted classes:", result['predicted_classes'])
sample_Total, training_input, test_input, class_labels = Breast_cancer(training_size=20, test_size=10, n=2, PLOT_DATA=True)
# n =2 is the dimension of each data point
datapoints, class_to_label = split_dataset_to_data_and_labels(test_input)
label_to_class = {label:class_name for class_name, label in class_to_label.items()}
print(class_to_label, label_to_class)
algo_input = SVMInput(training_input, test_input, datapoints[0])
result = run_algorithm(params, algo_input)
# print(result)
print("kernel matrix during the training:")
kernel_matrix = result['kernel_matrix_training']
img = plt.imshow(np.asmatrix(kernel_matrix),interpolation='nearest',origin='upper',cmap='bone_r')
plt.show()
print("testing success ratio: ", result['testing_accuracy'])
print("ground truth: {}".format(map_label_to_class_name(datapoints[1], label_to_class)))
print("predicted: {}".format(result['predicted_classes']))
|
https://github.com/quantumyatra/quantum_computing
|
quantumyatra
|
from qiskit import QuantumCircuit, Aer, execute
from math import pi
import numpy as np
from qiskit.visualization import plot_bloch_multivector, plot_histogram
qc = QuantumCircuit(2)
qc.h(0)
qc.h(1)
qc.cx(0,1)
qc.draw()
# Let's see the result
statevector_backend = Aer.get_backend('statevector_simulator')
final_state = execute(qc,statevector_backend).result().get_statevector()
# In Jupyter Notebooks we can display this nicely using Latex.
# If not using Jupyter Notebooks you may need to remove the
# array_to_latex function and use print(final_state) instead.
from qiskit_textbook.tools import array_to_latex
array_to_latex(final_state, pretext="\\text{Statevector} = ", precision=1)
plot_bloch_multivector(final_state)
qc = QuantumCircuit(2)
qc.h(0)
qc.h(1)
qc.z(1)
qc.draw()
final_state = execute(qc,statevector_backend).result().get_statevector()
array_to_latex(final_state, pretext="\\text{Statevector} = ", precision=1)
plot_bloch_multivector(final_state)
qc.cx(0,1)
qc.draw()
final_state = execute(qc,statevector_backend).result().get_statevector()
array_to_latex(final_state, pretext="\\text{Statevector} = ", precision=1)
plot_bloch_multivector(final_state)
qc = QuantumCircuit(2)
qc.h(0)
qc.h(1)
qc.cx(0,1)
qc.h(0)
qc.h(1)
display(qc.draw())
# `display` is an IPython tool, remove if it cases an error
unitary_backend = Aer.get_backend('unitary_simulator')
unitary = execute(qc,unitary_backend).result().get_unitary()
array_to_latex(unitary, pretext="\\text{Circuit = }\n")
qc = QuantumCircuit(2)
qc.cx(1,0)
display(qc.draw())
unitary_backend = Aer.get_backend('unitary_simulator')
unitary = execute(qc,unitary_backend).result().get_unitary()
array_to_latex(unitary, pretext="\\text{Circuit = }\n")
qc = QuantumCircuit(2)
qc.cu1(pi/4, 0, 1)
qc.draw()
unitary_backend = Aer.get_backend('unitary_simulator')
unitary = execute(qc,unitary_backend).result().get_unitary()
array_to_latex(unitary, pretext="\\text{Controlled-T} = \n")
qc = QuantumCircuit(2)
qc.h(0)
qc.x(1)
display(qc.draw())
final_state = execute(qc,statevector_backend).result().get_statevector()
plot_bloch_multivector(final_state)
qc.cu1(pi/4, 0, 1)
display(qc.draw())
final_state = execute(qc,statevector_backend).result().get_statevector()
plot_bloch_multivector(final_state)
import qiskit
qiskit.__qiskit_version__
|
https://github.com/shesha-raghunathan/DATE2019-qiskit-tutorial
|
shesha-raghunathan
|
# the first and second elements are 1 and 1
F = [1,1]
for i in range(2,30):
F.append(F[i-1] + F[i-2])
# print the final list
print(F)
# define an empty list
N = []
for i in range(11):
N.append([ i , i*i , i*i*i , i*i + i*i*i ]) # a list having four elements is added to the list N
# Alternatively:
#N.append([i , i**2 , i**3 , i**2 + i**3]) # ** is the exponent operator
#N = N + [ [i , i*i , i*i*i , i*i + i*i*i] ] # Why using double brakets?
#N = N + [ [i , i**2 , i**3 , i**2 + i**3] ] # Why using double brakets?
# In the last two alternative solutions, you may try with a single braket,
# and then see why double brakets are needed for the exact solution.
# print the final list
print(N)
# let's print the list element by element
for i in range(len(N)):
print(N[i])
# let's print the list element by element by using an alternative method
for el in N: # el will iteratively takes the values of elements in N
print(el)
|
https://github.com/qiskit-community/qiskit-translations-staging
|
qiskit-community
|
from qiskit import QuantumCircuit, transpile
ghz = QuantumCircuit(15)
ghz.h(0)
ghz.cx(0, range(1, 15))
ghz.draw(output='mpl')
|
https://github.com/swe-bench/Qiskit__qiskit
|
swe-bench
|
# This code is part of Qiskit.
#
# (C) Copyright IBM 2020, 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 derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""ComposedOp Class"""
from functools import partial, reduce
from typing import List, Optional, Union, cast, Dict
from numbers import Number
import numpy as np
from qiskit import QuantumCircuit
from qiskit.circuit import ParameterExpression
from qiskit.opflow.exceptions import OpflowError
from qiskit.opflow.list_ops.list_op import ListOp
from qiskit.opflow.operator_base import OperatorBase
from qiskit.quantum_info import Statevector
from qiskit.utils.deprecation import deprecate_func
class ComposedOp(ListOp):
"""Deprecated: A class for lazily representing compositions of Operators. Often Operators cannot be
efficiently composed with one another, but may be manipulated further so that they can be
composed later. This class holds logic to indicate that the Operators in ``oplist`` are meant to
be composed, and therefore if they reach a point in which they can be, such as after
conversion to QuantumCircuits or matrices, they can be reduced by composition."""
@deprecate_func(
since="0.24.0",
additional_msg="For code migration guidelines, visit https://qisk.it/opflow_migration.",
)
def __init__(
self,
oplist: List[OperatorBase],
coeff: Union[complex, ParameterExpression] = 1.0,
abelian: bool = False,
) -> None:
"""
Args:
oplist: The Operators being composed.
coeff: A coefficient multiplying the operator
abelian: Indicates whether the Operators in ``oplist`` are known to mutually commute.
"""
super().__init__(oplist, combo_fn=partial(reduce, np.dot), coeff=coeff, abelian=abelian)
@property
def num_qubits(self) -> int:
return self.oplist[0].num_qubits
@property
def distributive(self) -> bool:
return False
@property
def settings(self) -> Dict:
"""Return settings."""
return {"oplist": self._oplist, "coeff": self._coeff, "abelian": self._abelian}
# TODO take advantage of the mixed product property, tensorpower each element in the composition
# def tensorpower(self, other):
# """ Tensor product with Self Multiple Times """
# raise NotImplementedError
def to_matrix(self, massive: bool = False) -> np.ndarray:
OperatorBase._check_massive("to_matrix", True, self.num_qubits, massive)
mat = self.coeff * reduce(
np.dot, [np.asarray(op.to_matrix(massive=massive)) for op in self.oplist]
)
# Note: As ComposedOp has a combo function of inner product we can end up here not with
# a matrix (array) but a scalar. In which case we make a single element array of it.
if isinstance(mat, Number):
mat = [mat]
return np.asarray(mat, dtype=complex)
def to_circuit(self) -> QuantumCircuit:
"""Returns the quantum circuit, representing the composed operator.
Returns:
The circuit representation of the composed operator.
Raises:
OpflowError: for operators where a single underlying circuit can not be obtained.
"""
# pylint: disable=cyclic-import
from ..state_fns.circuit_state_fn import CircuitStateFn
from ..primitive_ops.primitive_op import PrimitiveOp
circuit_op = self.to_circuit_op()
if isinstance(circuit_op, (PrimitiveOp, CircuitStateFn)):
return circuit_op.to_circuit()
raise OpflowError(
"Conversion to_circuit supported only for operators, where a single "
"underlying circuit can be produced."
)
def adjoint(self) -> "ComposedOp":
return ComposedOp([op.adjoint() for op in reversed(self.oplist)], coeff=self.coeff)
def compose(
self, other: OperatorBase, permutation: Optional[List[int]] = None, front: bool = False
) -> OperatorBase:
new_self, other = self._expand_shorter_operator_and_permute(other, permutation)
new_self = cast(ComposedOp, new_self)
if front:
return other.compose(new_self)
# Try composing with last element in list
if isinstance(other, ComposedOp):
return ComposedOp(new_self.oplist + other.oplist, coeff=new_self.coeff * other.coeff)
# Try composing with last element of oplist. We only try
# this if that last element isn't itself an
# ComposedOp, so we can tell whether composing the
# two elements directly worked. If it doesn't,
# continue to the final return statement below, appending other to the oplist.
if not isinstance(new_self.oplist[-1], ComposedOp):
comp_with_last = new_self.oplist[-1].compose(other)
# Attempt successful
if not isinstance(comp_with_last, ComposedOp):
new_oplist = new_self.oplist[0:-1] + [comp_with_last]
return ComposedOp(new_oplist, coeff=new_self.coeff)
return ComposedOp(new_self.oplist + [other], coeff=new_self.coeff)
def eval(
self, front: Optional[Union[str, dict, np.ndarray, OperatorBase, Statevector]] = None
) -> Union[OperatorBase, complex]:
if self._is_empty():
return 0.0
# pylint: disable=cyclic-import
from ..state_fns.state_fn import StateFn
def tree_recursive_eval(r, l_arg):
if isinstance(r, list):
return [tree_recursive_eval(r_op, l_arg) for r_op in r]
else:
return l_arg.eval(r)
eval_list = self.oplist.copy()
# Only one op needs to be multiplied, so just multiply the first.
eval_list[0] = eval_list[0] * self.coeff # type: ignore
if front and isinstance(front, OperatorBase):
eval_list = eval_list + [front]
elif front:
eval_list = [StateFn(front, is_measurement=True)] + eval_list # type: ignore
return reduce(tree_recursive_eval, reversed(eval_list))
# Try collapsing list or trees of compositions into a single <Measurement | Op | State>.
def non_distributive_reduce(self) -> OperatorBase:
"""Reduce without attempting to expand all distributive compositions.
Returns:
The reduced Operator.
"""
reduced_ops = [op.reduce() for op in self.oplist]
reduced_ops = reduce(lambda x, y: x.compose(y), reduced_ops) * self.coeff
if isinstance(reduced_ops, ComposedOp) and len(reduced_ops.oplist) > 1:
return reduced_ops
else:
return reduced_ops[0]
def reduce(self) -> OperatorBase:
reduced_ops = [op.reduce() for op in self.oplist]
if len(reduced_ops) == 0:
return self.__class__([], coeff=self.coeff, abelian=self.abelian)
def distribute_compose(l_arg, r):
if isinstance(l_arg, ListOp) and l_arg.distributive:
# Either ListOp or SummedOp, returns correct type
return l_arg.__class__(
[distribute_compose(l_op * l_arg.coeff, r) for l_op in l_arg.oplist]
)
if isinstance(r, ListOp) and r.distributive:
return r.__class__([distribute_compose(l_arg, r_op * r.coeff) for r_op in r.oplist])
else:
return l_arg.compose(r)
reduced_ops = reduce(distribute_compose, reduced_ops) * self.coeff
if isinstance(reduced_ops, ListOp) and len(reduced_ops.oplist) == 1:
return reduced_ops.oplist[0]
else:
return cast(OperatorBase, reduced_ops)
|
https://github.com/soultanis/Quantum-Database-Search
|
soultanis
|
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()['last_update_date']))
print("Backend has {0} pending jobs".format(
backend.status()['pending_jobs']))
ibmq_job = backend.retrieve_job(job_id)
status = ibmq_job.status()
print(status.name)
print(ibmq_job.creation_date())
if (status.name == 'DONE'):
print("EUREKA")
result = ibmq_job.result()
count = result.get_counts()
print("Result is: {0}".format())
from qiskit.tools.visualization import plot_histogram
plot_histogram(counts)
else:
print("... Work in progress ...")
print("Queue position = {0}".format(ibmq_job.queue_position()))
print("Error message = {0}".format(ibmq_job.error_message()))
IBMQ.load_accounts()
print("Account(s) loaded")
if (len(sys.argv) > 2):
get_job_status(sys.argv[1], sys.argv[2])
|
https://github.com/qiskit-community/qiskit-translations-staging
|
qiskit-community
|
import numpy as np
import copy
# Problem modelling imports
from docplex.mp.model import Model
# Qiskit imports
from qiskit.algorithms.minimum_eigensolvers import QAOA, NumPyMinimumEigensolver
from qiskit.algorithms.optimizers import COBYLA
from qiskit.primitives import Sampler
from qiskit.utils.algorithm_globals import algorithm_globals
from qiskit_optimization.algorithms import MinimumEigenOptimizer, CplexOptimizer
from qiskit_optimization import QuadraticProgram
from qiskit_optimization.problems.variable import VarType
from qiskit_optimization.converters.quadratic_program_to_qubo import QuadraticProgramToQubo
from qiskit_optimization.translators import from_docplex_mp
def create_problem(mu: np.array, sigma: np.array, total: int = 3) -> QuadraticProgram:
"""Solve the quadratic program using docplex."""
mdl = Model()
x = [mdl.binary_var("x%s" % i) for i in range(len(sigma))]
objective = mdl.sum([mu[i] * x[i] for i in range(len(mu))])
objective -= 2 * mdl.sum(
[sigma[i, j] * x[i] * x[j] for i in range(len(mu)) for j in range(len(mu))]
)
mdl.maximize(objective)
cost = mdl.sum(x)
mdl.add_constraint(cost == total)
qp = from_docplex_mp(mdl)
return qp
def relax_problem(problem) -> QuadraticProgram:
"""Change all variables to continuous."""
relaxed_problem = copy.deepcopy(problem)
for variable in relaxed_problem.variables:
variable.vartype = VarType.CONTINUOUS
return relaxed_problem
mu = np.array([3.418, 2.0913, 6.2415, 4.4436, 10.892, 3.4051])
sigma = np.array(
[
[1.07978412, 0.00768914, 0.11227606, -0.06842969, -0.01016793, -0.00839765],
[0.00768914, 0.10922887, -0.03043424, -0.0020045, 0.00670929, 0.0147937],
[0.11227606, -0.03043424, 0.985353, 0.02307313, -0.05249785, 0.00904119],
[-0.06842969, -0.0020045, 0.02307313, 0.6043817, 0.03740115, -0.00945322],
[-0.01016793, 0.00670929, -0.05249785, 0.03740115, 0.79839634, 0.07616951],
[-0.00839765, 0.0147937, 0.00904119, -0.00945322, 0.07616951, 1.08464544],
]
)
qubo = create_problem(mu, sigma)
print(qubo.prettyprint())
result = CplexOptimizer().solve(qubo)
print(result.prettyprint())
qp = relax_problem(QuadraticProgramToQubo().convert(qubo))
print(qp.prettyprint())
sol = CplexOptimizer().solve(qp)
print(sol.prettyprint())
c_stars = sol.samples[0].x
print(c_stars)
algorithm_globals.random_seed = 12345
qaoa_mes = QAOA(sampler=Sampler(), optimizer=COBYLA(), initial_point=[0.0, 1.0])
exact_mes = NumPyMinimumEigensolver()
qaoa = MinimumEigenOptimizer(qaoa_mes)
qaoa_result = qaoa.solve(qubo)
print(qaoa_result.prettyprint())
from qiskit import QuantumCircuit
thetas = [2 * np.arcsin(np.sqrt(c_star)) for c_star in c_stars]
init_qc = QuantumCircuit(len(sigma))
for idx, theta in enumerate(thetas):
init_qc.ry(theta, idx)
init_qc.draw(output="mpl")
from qiskit.circuit import Parameter
beta = Parameter("β")
ws_mixer = QuantumCircuit(len(sigma))
for idx, theta in enumerate(thetas):
ws_mixer.ry(-theta, idx)
ws_mixer.rz(-2 * beta, idx)
ws_mixer.ry(theta, idx)
ws_mixer.draw(output="mpl")
ws_qaoa_mes = QAOA(
sampler=Sampler(),
optimizer=COBYLA(),
initial_state=init_qc,
mixer=ws_mixer,
initial_point=[0.0, 1.0],
)
ws_qaoa = MinimumEigenOptimizer(ws_qaoa_mes)
ws_qaoa_result = ws_qaoa.solve(qubo)
print(ws_qaoa_result.prettyprint())
def format_qaoa_samples(samples, max_len: int = 10):
qaoa_res = []
for s in samples:
if sum(s.x) == 3:
qaoa_res.append(("".join([str(int(_)) for _ in s.x]), s.fval, s.probability))
res = sorted(qaoa_res, key=lambda x: -x[1])[0:max_len]
return [(_[0] + f": value: {_[1]:.3f}, probability: {1e2*_[2]:.1f}%") for _ in res]
format_qaoa_samples(qaoa_result.samples)
format_qaoa_samples(ws_qaoa_result.samples)
from qiskit_optimization.algorithms import WarmStartQAOAOptimizer
qaoa_mes = QAOA(sampler=Sampler(), optimizer=COBYLA(), initial_point=[0.0, 1.0])
ws_qaoa = WarmStartQAOAOptimizer(
pre_solver=CplexOptimizer(), relax_for_pre_solver=True, qaoa=qaoa_mes, epsilon=0.0
)
ws_result = ws_qaoa.solve(qubo)
print(ws_result.prettyprint())
format_qaoa_samples(ws_result.samples)
import qiskit.tools.jupyter
%qiskit_version_table
%qiskit_copyright
|
https://github.com/shesha-raghunathan/DATE2019-qiskit-tutorial
|
shesha-raghunathan
|
# I am a comment in python
print("Hello From Quantum World :-)")
# please run me
from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit, execute, Aer
from random import randrange
# Create my circuit and register objects
qreg = QuantumRegister(2) # my quantum register
creg = ClassicalRegister(2) # my classical register
circuit = QuantumCircuit(qreg,creg) # my quantum circuit
# let's apply a Hadamard gate to the first qubit
circuit.h(qreg[0])
# let's set the second qubit to |1>
circuit.x(qreg[1])
# let's apply CNOT(first_qubit,second_qubit)
circuit.cx(qreg[0],qreg[1])
# let's measure the both qubits
circuit.measure(qreg,creg)
print("The execution was completed, and the circuit was created :)")
## execute the circuit 100 times
job = execute(circuit,Aer.get_backend('qasm_simulator'),shots=1024)
# get the result
counts = job.result().get_counts(circuit)
print(counts)
from qiskit.tools.visualization import matplotlib_circuit_drawer as drawer
# draw the overall circuit
drawer(circuit)
# re-execute me if you DO NOT see the circuit diagram
from qiskit import IBMQ
IBMQ.save_account('write YOUR IBM API TOKEN here')
# Then, execute this cell
IBMQ.stored_accounts()
IBMQ.load_accounts()
IBMQ.active_accounts()
IBMQ.backends()
IBMQ.backends(operational=True, simulator=False)
from qiskit.backends.ibmq import least_busy
least_busy(IBMQ.backends(simulator=False))
backend = least_busy(IBMQ.backends(simulator=True))
backend.name()
from qiskit import compile
qobj = compile(circuit, backend=backend, shots=1024)
job = backend.run(qobj)
result = job.result()
counts = result.get_counts()
print(counts)
backend_real = least_busy(IBMQ.backends(simulator=False))
backend_real.name()
backend_real.status()
qobj_real = compile(circuit, backend=backend_real, shots=1024)
job_real = backend_real.run(qobj_real)
job_real.queue_position()
result_real = job_real.result()
counts_real = result_real.get_counts()
print(counts_real)
|
https://github.com/liyaochong/quantum-algorithm-learning-and-implementation
|
liyaochong
|
# 导入相应的运算库
import numpy as np
from qiskit import BasicAer, execute
from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister, execute
from qiskit.tools.visualization import circuit_drawer
desired_vector1 = [np.sqrt(3)/2, 1/2, 0, 0]
desired_vector2 = [1/2, 1/2, 1/2, 1/2]
q0 = QuantumRegister(1,'q0')
c0 = ClassicalRegister(1,'c0')
q1 = QuantumRegister(2,'q1')
c1 = ClassicalRegister(2,'c1')
q2 = QuantumRegister(2,'q2')
c2 = ClassicalRegister(2,'c2')
Circuit = QuantumCircuit()
Circuit.add_register(q0,c0)
Circuit.add_register(q1,c1)
Circuit.add_register(q2,c2)
Circuit.initialize(desired_vector1, [q1[0],q1[1]])
Circuit.initialize(desired_vector2, [q2[0],q2[1]])
Circuit.h(q0[0])
Circuit.ccx(q0[0],q1[1],q2[1])
Circuit.ccx(q0[0],q2[1],q1[1])
Circuit.ccx(q0[0],q1[1],q2[1])
Circuit.ccx(q0[0],q1[0],q2[0])
Circuit.ccx(q0[0],q2[0],q1[0])
Circuit.ccx(q0[0],q1[0],q2[0])
Circuit.h(q0[0])
Circuit.draw(output='mpl')
# 对寄存器q0进行测量
Circuit.measure(q0,c0)
# 测量次数设置为1024次
backend = BasicAer.get_backend('qasm_simulator')
job = execute(Circuit, backend,shots=1024)
job.result().get_counts(Circuit)
Inner_product = np.sum(np.multiply(np.array(desired_vector1),np.array(desired_vector2)))
Overlap = Inner_product**2
P_0 = 1/2 + Overlap/2
print(P_0*1024)
|
https://github.com/alejomonbar/Quantum-Supply-Chain-Manager
|
alejomonbar
|
# %pip install qiskit
# %pip install docplex
# %pip install qiskit_optimization
# %pip install networkx
# %pip install geopandas
# %pip install folium
import networkx as nx
import geopandas as gpd
import folium
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import itertools
import time
import random
from IPython.display import display
from qiskit import IBMQ
from qiskit.algorithms import QAOA, VQE
from qiskit.circuit.library import TwoLocal
from qiskit.algorithms.optimizers import COBYLA, SPSA # Classical simulator
from qiskit.providers.aer import AerSimulator
from docplex.mp.model import Model
from qiskit_optimization.runtime import QAOAClient, VQEClient
from qiskit_optimization.converters import QuadraticProgramToQubo
from qiskit_optimization.translators import from_docplex_mp
from qiskit_optimization.algorithms import CplexOptimizer, MinimumEigenOptimizer
path = gpd.datasets.get_path('nybb')
df = gpd.read_file(path)
m = folium.Map(location=[40.70, -73.94], zoom_start=10, max_zoom=12, tiles='CartoDB positron')
# Project to NAD83 projected crs
df = df.to_crs(epsg=2263)
# Access the centroid attribute of each polygon
df['centroid'] = df.centroid
# Project to WGS84 geographic crs
# geometry (active) column
df = df.to_crs(epsg=4326)
# Centroid column
df['centroid'] = df['centroid'].to_crs(epsg=4326)
np.random.seed(7)
locations = []
ids = 0
for _, r in df.iterrows():
lat, lon = r['centroid'].y, r['centroid'].x
for i in range(2):
lat_rand, lon_rand = lat + 0.2 * np.random.rand(), lon +0.1 * np.random.rand()
locations.append((lon_rand, lat_rand))
folium.Marker(location=[lat_rand, lon_rand], popup=f'Id: {ids}').add_to(m)
ids += 1
center = np.array(locations).mean(axis=0)
locations = [(center[0], center[1])] + locations
folium.CircleMarker(location=[center[1], center[0]], radius=10, popup="<stong>Warehouse</stong>",
color="red",fill=True, fillOpacity=1, fillColor="tab:red").add_to(m)
m
# Normalizing the results to produce a graph in Graphx
companies = np.array(locations)
companies -= companies[0]
companies /= (np.max(np.abs(companies), axis=0))
r = list(np.sqrt(np.sum(companies ** 2, axis=1)))
threshold = 1 # Limit for to not consider the route from company i to j if the distance is larger than a threshold
n_companies = len(companies)
G = nx.Graph(name="VRP")
G.add_nodes_from(range(n_companies))
# G.add_weighted_edges_from([0, i, w] for i, w in zip(range(1, n_companies), r[1:]))
np.random.seed(2)
count = 0
for i in range(n_companies):
for j in range(n_companies):
if i != j:
rij = np.sqrt(np.sum((companies[i] - companies[j])**2))
if (rij < threshold) or (0 in [i, j]):
count +=1
G.add_weighted_edges_from([[i, j, rij]])
r.append(rij)
colors = [plt.cm.get_cmap("coolwarm")(x) for x in r[1:]]
nx.draw(G, pos=companies, with_labels=True, node_size=500,
edge_color=colors, width=1, font_color="white",font_size=14,
node_color = ["tab:red"] + (n_companies-1)*["darkblue"])
print(f"The number of edges of this problem is: {len(G.edges)}")
mdl = Model(name="VRP")
n_trucks = 3 # number of K trucks
x = {}
for i, j in G.edges():
x[(i, j)] = mdl.binary_var(name=f"x_{i}_{j}") # Adding route from company i to company j as a binary variable
x[(j, i)] = mdl.binary_var(name=f"x_{j}_{i}") # Adding route from company j to company i as a binary variable
print(f"The number of qubits needed to solve the problem is: {mdl.number_of_binary_variables}")
cost_func = mdl.sum(w["weight"] * x[(i, j)] for i, j, w in G.edges(data=True)) + mdl.sum(w["weight"] * x[(j, i)] for i, j, w in G.edges(data=True))
mdl.minimize(cost_func)
# Constraint 1a(yellow Fig. above): Only one truck goes out from company i
for i in range(1, n_companies):
mdl.add_constraint(mdl.sum(x[i, j] for j in range(n_companies) if (i, j) in x.keys()) == 1)
# Constraint 1b (yellow Fig. above): Only one truck comes into company j
for j in range(1, n_companies):
mdl.add_constraint(mdl.sum(x[i, j] for i in range(n_companies) if (i, j) in x.keys()) == 1)
# Constraint 2: (orange Fig. above) For the warehouse
mdl.add_constraint(mdl.sum(x[i, 0] for i in range(1, n_companies)) == n_trucks)
mdl.add_constraint(mdl.sum(x[0, j] for j in range(1, n_companies)) == n_trucks)
# Constraint 3: (blue Fig. above) To eliminate sub-routes
companies_list = list(range(1, n_companies))
subroute_set = []
for i in range(2, len(companies_list) + 1):
for comb in itertools.combinations(companies_list, i):
subroute_set.append(list(comb)) #subset points
for subroute in subroute_set:
constraint_3 = []
for i, j in itertools.permutations(subroute, 2): #iterating over all the subset points
if (i, j) in x.keys():
constraint_3.append(x[(i,j)])
elif i == j:
pass
else:
constraint_3 = []
break
if len(constraint_3) != 0:
mdl.add_constraint(mdl.sum(constraint_3) <= len(subroute) - 1)
quadratic_program = from_docplex_mp(mdl)
print(quadratic_program.export_as_lp_string())
sol = CplexOptimizer().solve(quadratic_program)
print(sol.prettyprint())
solution_cplex = sol.raw_results.as_name_dict()
G_sol = nx.Graph()
G_sol.add_nodes_from(range(n_companies))
for i in solution_cplex:
nodes = i[2:].split("_")
G_sol.add_edge(int(nodes[0]), int(nodes[1]))
nx.draw(G_sol, pos=companies, with_labels=True, node_size=500,
edge_color=colors, width=1, font_color="white",font_size=14,
node_color = ["tab:red"] + (n_companies-1)*["darkblue"])
qubo = QuadraticProgramToQubo(penalty=15).convert(quadratic_program)
num_vars = qubo.get_num_binary_vars()
print(f"To represent the inital problem with {mdl.number_of_binary_variables} variables, the QUBO representation needs {num_vars} variables")
new_qubo = qubo.substitute_variables(sol.variables_dict)
start = time.time()
sol_slack = CplexOptimizer().solve(new_qubo)
end = time.time() - start
print(f"The solver needs {np.round(end, 3)} seconds to finish.")
qubo_no_slack = qubo.substitute_variables(sol_slack.variables_dict)
sol_no_slack = CplexOptimizer().solve(qubo_no_slack)
solution_slack = sol_no_slack.raw_results.as_name_dict()
G_sol_slack = nx.Graph()
G_sol_slack.add_nodes_from(range(n_companies))
for i in solution_slack:
nodes = i[2:].split("_")
G_sol.add_edge(int(nodes[0]), int(nodes[1]))
nx.draw(G_sol, pos=companies, with_labels=True, node_size=500,
edge_color=colors, width=1, font_color="white",font_size=14,
node_color = ["tab:red"] + (n_companies-1)*["darkblue"])
index_1s = [k for k, v in sol_no_slack.variables_dict.items() if v == 1]
index_0s = [k for k, v in sol_no_slack.variables_dict.items() if v == 0]
def Optimization_QAOA(qubo, reps=1, optimizer=COBYLA(maxiter=1000), backend=None,
shots=1024, alpha=1.0, provider=None, local=False, error_mitigation=False):
intermediate_info = {'nfev': [],
'parameters': [],
'stddev': [],
'mean': []
}
def callback(nfev, parameters, mean, stddev):
# display(f"Evaluation: {nfev}, Energy: {mean}, Std: {stddev}")
intermediate_info['nfev'].append(nfev)
intermediate_info['parameters'].append(parameters)
intermediate_info['mean'].append(mean)
intermediate_info['stddev'].append(stddev)
if local:
qaoa_mes = QAOA(optimizer=optimizer, reps=reps, quantum_instance=AerSimulator(),
callback=callback)
else:
qaoa_mes = QAOAClient(provider=provider, backend=backend, reps=reps, alpha=alpha,
shots=shots, callback=callback, optimizer=optimizer,
optimization_level=3,measurement_error_mitigation=error_mitigation)
qaoa = MinimumEigenOptimizer(qaoa_mes)
result = qaoa.solve(qubo)
return result, intermediate_info
def Optimization_VQE(qubo, ansatz, optimizer=SPSA(maxiter=50), backend=None,
shots=1024, provider=None, local=False, error_mitigation=False):
intermediate_info = {'nfev': [],
'parameters': [],
'stddev': [],
'mean': []
}
def callback(nfev, parameters, mean, stddev):
# display(f"Evaluation: {nfev}, Energy: {mean}, Std: {stddev}")
intermediate_info['nfev'].append(nfev)
intermediate_info['parameters'].append(parameters)
intermediate_info['mean'].append(mean)
intermediate_info['stddev'].append(stddev)
if local:
vqe_mes = VQE(ansatz=ansatz, quantum_instance=AerSimulator(),
callback=callback, optimizer=optimizer)
else:
vqe_mes = VQEClient(ansatz=ansatz, provider=provider, backend=backend, shots=shots,
callback=callback, optimizer=optimizer,measurement_error_mitigation=error_mitigation)
vqe = MinimumEigenOptimizer(vqe_mes)
result = vqe.solve(qubo)
return result, intermediate_info
def graph(solution, solution_not_kept):
G = nx.Graph()
G.add_nodes_from(range(n_companies))
for k, v in solution.items():
if v == 1:
nodes = k[2:].split("_")
G.add_edge(int(nodes[0]), int(nodes[1]))
for k, v in solution_not_kept.items():
if v == 1:
nodes = k[2:].split("_")
G.add_edge(int(nodes[0]), int(nodes[1]))
return G
num_1s = 3
num_0s = 4
random.seed(1)
keep_1s = random.sample(index_1s, num_1s)
keep_0s = random.sample(index_0s, num_0s)
sol_qubo = sol.variables_dict
solution_not_kept7 = {i:sol_qubo[i] for i in sol_qubo.keys() if i not in keep_1s + keep_0s}
qubo_7vars = qubo_no_slack.substitute_variables(solution_not_kept7)
print(qubo_7vars.export_as_lp_string())
sol7_local_qaoa_cobyla = Optimization_QAOA(qubo_7vars, reps=2, optimizer=COBYLA(maxiter=100), local=True)
sol7_local_qaoa_spsa = Optimization_QAOA(qubo_7vars, reps=2, optimizer=SPSA(maxiter=100), local=True)
IBMQ.load_account()
provider = IBMQ.get_provider(hub='ibm-q-community', group='cdl-hackathon', project='main')
provider2 = IBMQ.get_provider(hub='ibm-q-research', group='guanajuato-1', project='main')
sol7_oslo_qaoa_cobyla = Optimization_QAOA(qubo_7vars, reps=2, local=False, backend=provider2.backend.ibm_oslo,
provider=provider2, optimizer=COBYLA(maxiter=50))
sol7_oslo_qaoa_spsa = Optimization_QAOA(qubo_7vars, reps=2, local=False, backend=provider2.backend.ibm_oslo,
provider=provider2, optimizer=SPSA(maxiter=50))
sol7_oslo_qaoa_spsa_mitig = Optimization_QAOA(qubo_7vars, reps=2, local=False, backend=provider2.backend.ibm_oslo,
provider=provider2, optimizer=SPSA(maxiter=50), error_mitigation=True)
ansatz = TwoLocal(qubo_7vars.get_num_vars(), rotation_blocks='ry', entanglement_blocks='cz')
sol7_local_vqe_spsa = Optimization_VQE(qubo_7vars, ansatz, local=True, optimizer=SPSA(maxiter=200))
sol7_local_vqe_cobyla = Optimization_VQE(qubo_7vars, ansatz, local=True, optimizer=COBYLA(maxiter=200))
sol7_lagos_vqe_cobyla = Optimization_VQE(qubo_7vars, ansatz, local=False, optimizer=COBYLA(maxiter=100),
backend=provider2.backend.ibm_lagos, provider=provider2)
sol7_lagos_vqe_spsa = Optimization_VQE(qubo_7vars, ansatz, local=False, optimizer=SPSA(maxiter=25),
backend=provider2.backend.ibm_lagos, provider=provider2)
sol7_lagos_vqe_spsa2 = Optimization_VQE(qubo_7vars, ansatz, local=False, optimizer=SPSA(maxiter=25),
backend=provider2.backend.ibm_lagos, provider=provider2, error_mitigation=True)
plt.figure()
plt.plot(sol7_lagos_vqe_spsa[1]["mean"], linestyle=":", color="seagreen", label="VQE-Lagos-SPSA")
plt.plot(sol7_lagos_vqe_spsa2[1]["mean"], linestyle="-.", color="indigo", label="VQE-Lagos-SPSA-Mitig")
plt.plot(sol7_lagos_vqe_cobyla[1]["mean"], color="olive", label="VQE-Lagos-COBYLA-Mitig")
plt.grid()
plt.savefig("./Images/inset.png")
plt.figure()
plt.plot(sol7_oslo_qaoa_spsa[1]["mean"], linestyle=":", color="seagreen", label="QAOA-Oslo-SPSA")
plt.plot(sol7_oslo_qaoa_cobyla[1]["mean"], color="olive", label="QAOA-Oslo-COBYLA")
plt.grid()
plt.savefig("./Images/inset2.png")
sol7 = {"local_qaoa_cobyla":sol7_local_qaoa_cobyla, "local_vqe_cobyla":sol7_local_vqe_cobyla,
"local_qaoa_spsa":sol7_local_qaoa_spsa, "local_vqe_spsa":sol7_local_vqe_spsa,
"oslo_qaoa_cobyla":sol7_oslo_qaoa_cobyla, "oslo_qaoa_spsa":sol7_oslo_qaoa_spsa,
"lagos_vqe_spsa":sol7_lagos_vqe_spsa, "lagos_vqe_cobyla_mitig":sol7_lagos_vqe_cobyla}
np.save("./Data/sol7.npy", sol7)
sol7 = [sol7_local_qaoa_cobyla, sol7_local_vqe_cobyla, sol7_local_qaoa_spsa, sol7_local_vqe_spsa]
q_alg = ["QAOA", "VQE"]
fig, ax = plt.subplots(3,2, figsize=(15,18))
ax[0,0].plot(sol7_local_qaoa_cobyla[1]["mean"], color="darkblue", label="QAOA-COBYLA")
ax[0,0].plot(sol7_local_qaoa_spsa[1]["mean"], linestyle="--", color="lightcoral", label="QAOA-SPSA")
ax[0,0].plot(sol7_oslo_qaoa_spsa[1]["mean"], linestyle=":", color="seagreen", label="QAOA-Oslo-SPSA")
ax[0,0].plot(sol7_oslo_qaoa_cobyla[1]["mean"], color="olive", label="QAOA-Oslo-COBYLA")
ax[0,1].plot(sol7_local_vqe_cobyla[1]["mean"], color="darkblue", label="VQE-COBYLA")
ax[0,1].plot(sol7_local_vqe_spsa[1]["mean"], linestyle="--", color="lightcoral", label="VQE-SPSA")
ax[0,1].plot(sol7_lagos_vqe_spsa[1]["mean"], linestyle=":", color="seagreen", label="VQE-Lagos-SPSA")
ax[0,1].plot(sol7_lagos_vqe_spsa2[1]["mean"], linestyle="-.", color="indigo", label="VQE-Lagos-SPSA-Mitig")
ax[0,1].plot(sol7_lagos_vqe_cobyla[1]["mean"], color="olive", label="VQE-Lagos-COBYLA-Mitig")
for i in range(2):
ax[0,i].set_xlabel("Iterations", fontsize=14)
ax[0,i].set_ylabel("Cost", fontsize=14)
ax[0,i].legend()
ax[0,i].grid()
plt.sca(ax[1,i])
nx.draw(graph(sol7[i][0].variables_dict,solution_not_kept7), pos=companies, with_labels=True, node_size=500,
edge_color=colors, width=1, font_color="white",font_size=14,
node_color = ["tab:red"] + (n_companies-1)*["darkblue"])
ax[1,i].set_title(f"{q_alg[i]}-COBYLA", fontsize=14)
plt.sca(ax[2,i])
nx.draw(graph(sol7[i+2][0].variables_dict,solution_not_kept7), pos=companies, with_labels=True, node_size=500,
edge_color=colors, width=1, font_color="white",font_size=14,
node_color = ["tab:red"] + (n_companies-1)*["darkblue"])
ax[2,i].set_title(f"{q_alg[i]}-SPSA", fontsize=14)
plt.savefig("./Images/Sol7Q.png")
num_1s = 5
num_0s = 10
random.seed(1)
keep_1s = random.sample(index_1s, num_1s)
keep_0s = random.sample(index_0s, num_0s)
sol_qubo = sol.variables_dict
solution_not_kept = {i:sol_qubo[i] for i in sol_qubo.keys() if i not in keep_1s + keep_0s}
qubo_15vars = qubo_no_slack.substitute_variables(solution_not_kept)
sol15_qasm_qaoa_cobyla = Optimization_QAOA(qubo_15vars, reps=3, local=False, backend=provider2.backend.ibmq_qasm_simulator,
provider=provider2, optimizer=COBYLA(maxiter=100))
solution15_qaoa = sol15_qasm_qaoa_cobyla[0].variables_dict
G_sol15_qaoa = nx.Graph()
G_sol15_qaoa.add_nodes_from(range(n_companies))
for k, v in solution15_qaoa.items():
if v == 1:
nodes = k[2:].split("_")
G_sol15_qaoa.add_edge(int(nodes[0]), int(nodes[1]))
for k, v in solution_not_kept.items():
if v == 1:
nodes = k[2:].split("_")
G_sol15_qaoa.add_edge(int(nodes[0]), int(nodes[1]))
nx.draw(G_sol15_qaoa, pos=companies, with_labels=True, node_size=500,
edge_color=colors, width=1, font_color="white",font_size=14,
node_color = ["tab:red"] + (n_companies-1)*["darkblue"])
sol15_qasm_qaoa_spsa = Optimization_QAOA(qubo_15vars, reps=3, local=False, backend=provider2.backend.ibmq_qasm_simulator,
provider=provider2, optimizer=SPSA(maxiter=25))
ansatz15 = TwoLocal(qubo_15vars.get_num_vars(), rotation_blocks='ry', entanglement_blocks='cz')
sol15_qasm_vqe_cobyla = Optimization_VQE(qubo_15vars,ansatz=ansatz15, local=False, backend=provider2.backend.ibmq_qasm_simulator,
provider=provider2, optimizer=COBYLA(maxiter=100))
solution15_vqe = sol15_qasm_vqe_cobyla[0].variables_dict
G_sol15_vqe = nx.Graph()
G_sol15_vqe.add_nodes_from(range(n_companies))
for k, v in solution15_vqe.items():
if v == 1:
nodes = k[2:].split("_")
G_sol15_vqe.add_edge(int(nodes[0]), int(nodes[1]))
for k, v in solution_not_kept.items():
if v == 1:
nodes = k[2:].split("_")
G_sol15_vqe.add_edge(int(nodes[0]), int(nodes[1]))
nx.draw(G_sol15_vqe, pos=companies, with_labels=True, node_size=500,
edge_color=colors, width=1, font_color="white",font_size=14,
node_color = ["tab:red"] + (n_companies-1)*["darkblue"])
sol15_qasm = {"qaoa_cobyla":sol15_qasm_qaoa_cobyla, "vqe_cobyla":sol15_qasm_vqe_cobyla}
np.save("./Data/sol15_qasm.npy", sol15_qasm)
solution15 = [G_sol15_qaoa, G_sol15_vqe]
fig, ax = plt.subplots(2,2, figsize=(15,10))
ax[0,0].plot(sol15_qasm_qaoa_cobyla[1]["mean"], color="darkblue", label="QAOA-COBYLA")
ax[0,1].plot(sol15_qasm_vqe_cobyla[1]["mean"], color="slateblue", label="VQE-COBYLA")
for i in range(2):
ax[0,i].grid()
ax[0,i].set_xlabel("Iterations", fontsize=14)
ax[0,i].set_ylabel("Cost", fontsize=14)
ax[0,i].legend()
plt.sca(ax[1,i])
nx.draw(solution15[i], pos=companies, with_labels=True, node_size=500,
edge_color=colors, width=1, font_color="white",font_size=14,
node_color = ["tab:red"] + (n_companies-1)*["darkblue"])
fig.savefig("./Images/Sol15Q.png")
num_1s = 8
num_0s = 12
random.seed(1)
keep_1s = random.sample(index_1s, num_1s)
keep_0s = random.sample(index_0s, num_0s)
sol_qubo = sol.variables_dict
solution_not_kept = {i:sol_qubo[i] for i in sol_qubo.keys() if i not in keep_1s + keep_0s}
qubo_20vars = qubo_no_slack.substitute_variables(solution_not_kept)
sol20_qasm_qaoa_cobyla_r1 = Optimization_QAOA(qubo_20vars, reps=1, local=False, backend=provider2.backend.ibmq_qasm_simulator,
provider=provider2, optimizer=COBYLA(maxiter=10))
|
https://github.com/indian-institute-of-science-qc/qiskit-aakash
|
indian-institute-of-science-qc
|
# This code is part of Qiskit.
#
# (C) Copyright IBM 2017, 2019.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""Test RemoveResetInZeroState pass"""
import unittest
from qiskit import QuantumRegister, QuantumCircuit
from qiskit.transpiler import PassManager
from qiskit.transpiler.passes import RemoveResetInZeroState, DAGFixedPoint
from qiskit.converters import circuit_to_dag
from qiskit.test import QiskitTestCase
class TestRemoveResetInZeroState(QiskitTestCase):
"""Test swap-followed-by-measure optimizations."""
def test_optimize_single_reset(self):
"""Remove a single reset
qr0:--|0>-- ==> qr0:----
"""
qr = QuantumRegister(1, "qr")
circuit = QuantumCircuit(qr)
circuit.reset(qr)
dag = circuit_to_dag(circuit)
expected = QuantumCircuit(qr)
pass_ = RemoveResetInZeroState()
after = pass_.run(dag)
self.assertEqual(circuit_to_dag(expected), after)
def test_dont_optimize_non_zero_state(self):
"""Do not remove reset if not in a zero state
qr0:--[H]--|0>-- ==> qr0:--[H]--|0>--
"""
qr = QuantumRegister(1, "qr")
circuit = QuantumCircuit(qr)
circuit.h(qr)
circuit.reset(qr)
dag = circuit_to_dag(circuit)
expected = QuantumCircuit(qr)
expected.h(qr)
expected.reset(qr)
pass_ = RemoveResetInZeroState()
after = pass_.run(dag)
self.assertEqual(circuit_to_dag(expected), after)
def test_optimize_single_reset_in_diff_qubits(self):
"""Remove a single reset in different qubits
qr0:--|0>-- qr0:----
==>
qr1:--|0>-- qr1:----
"""
qr = QuantumRegister(2, "qr")
circuit = QuantumCircuit(qr)
circuit.reset(qr)
dag = circuit_to_dag(circuit)
expected = QuantumCircuit(qr)
pass_ = RemoveResetInZeroState()
after = pass_.run(dag)
self.assertEqual(circuit_to_dag(expected), after)
class TestRemoveResetInZeroStateFixedPoint(QiskitTestCase):
"""Test RemoveResetInZeroState in a transpiler, using fixed point."""
def test_two_resets(self):
"""Remove two initial resets
qr0:--|0>-|0>-- ==> qr0:----
"""
qr = QuantumRegister(1, "qr")
circuit = QuantumCircuit(qr)
circuit.reset(qr[0])
circuit.reset(qr[0])
expected = QuantumCircuit(qr)
pass_manager = PassManager()
pass_manager.append(
[RemoveResetInZeroState(), DAGFixedPoint()],
do_while=lambda property_set: not property_set["dag_fixed_point"],
)
after = pass_manager.run(circuit)
self.assertEqual(expected, after)
if __name__ == "__main__":
unittest.main()
|
https://github.com/Qiskit/feedback
|
Qiskit
|
import numpy
from qiskit.circuit.library import GraphState
from qiskit.test.mock import FakeMumbai
from qiskit.transpiler import CouplingMap
from qiskit.transpiler.passes import VF2Layout, CSPLayout
from qiskit.visualization import plot_gate_map, plot_circuit_layout
backend = FakeMumbai()
config = backend.configuration()
cm = config.coupling_map
size = config.n_qubits
display(plot_gate_map(backend))
rows = [x[0] for x in cm]
cols = [x[1] for x in cm]
A = numpy.zeros((size, size))
A[rows, cols] = 1
from random import shuffle
random_map = list(range(size))
shuffle(random_map)
B = numpy.zeros((size, size))
for i in range(size):
for j in range(size):
B[i][j] = A[random_map[i]][random_map[j]]
A = B
circuit = GraphState(A).decompose()
circuit.draw(fold=-1)
vf2_pass = VF2Layout(CouplingMap(cm))
csp_pass = CSPLayout(CouplingMap(cm), time_limit=None, call_limit=None)
csp_transpiled = csp_pass(circuit)
vf2_transpiled = vf2_pass(circuit)
plot_circuit_layout(vf2_transpiled, backend)
plot_circuit_layout(csp_transpiled, backend)
from qiskit import QuantumCircuit
# T-shape interaction map
circuit = QuantumCircuit(5)
circuit.cx(0,1)
circuit.cx(0,2)
circuit.cx(0,3)
circuit.cx(3,4)
# Multiple possible answers. This allows for noise-awareness (ish).
vf2_transpiled = vf2_pass(circuit)
vf2_transpiled
|
https://github.com/tanmaybisen31/Quantum-Teleportation
|
tanmaybisen31
|
# Do the necessary imports
import numpy as np
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
from qiskit import IBMQ, Aer, transpile, assemble
from qiskit.visualization import plot_histogram, plot_bloch_multivector, array_to_latex
from qiskit.extensions import Initialize
from qiskit.ignis.verification import marginal_counts
from qiskit.quantum_info import random_statevector
# Loading your IBM Quantum account(s)
provider = IBMQ.load_account()
## SETUP
# Protocol uses 3 qubits and 2 classical bits in 2 different registers
qr = QuantumRegister(3, name="q") # Protocol uses 3 qubits
crz = ClassicalRegister(1, name="crz") # and 2 classical bits
crx = ClassicalRegister(1, name="crx") # in 2 different registers
teleportation_circuit = QuantumCircuit(qr, crz, crx)
def create_bell_pair(qc, a, b):
qc.h(a)
qc.cx(a,b)
qr = QuantumRegister(3, name="q")
crz, crx = ClassicalRegister(1, name="crz"), ClassicalRegister(1, name="crx")
teleportation_circuit = QuantumCircuit(qr, crz, crx)
create_bell_pair(teleportation_circuit, 1, 2)
teleportation_circuit.draw()
def alice_gates(qc, psi, a):
qc.cx(psi, a)
qc.h(psi)
qr = QuantumRegister(3, name="q")
crz, crx = ClassicalRegister(1, name="crz"), ClassicalRegister(1, name="crx")
teleportation_circuit = QuantumCircuit(qr, crz, crx)
## STEP 1
create_bell_pair(teleportation_circuit, 1, 2)
## STEP 2
teleportation_circuit.barrier() # Use barrier to separate steps
alice_gates(teleportation_circuit, 0, 1)
teleportation_circuit.draw()
def measure_and_send(qc, a, b):
"""Measures qubits a & b and 'sends' the results to Bob"""
qc.barrier()
qc.measure(a,0)
qc.measure(b,1)
qr = QuantumRegister(3, name="q")
crz, crx = ClassicalRegister(1, name="crz"), ClassicalRegister(1, name="crx")
teleportation_circuit = QuantumCircuit(qr, crz, crx)
create_bell_pair(teleportation_circuit, 1, 2)
teleportation_circuit.barrier() # Use barrier to separate steps
alice_gates(teleportation_circuit, 0, 1)
measure_and_send(teleportation_circuit, 0 ,1)
teleportation_circuit.draw()
def bob_gates(qc, qubit, crz, crx):
qc.x(qubit).c_if(crx, 1) # Apply gates if the registers
qc.z(qubit).c_if(crz, 1) # are in the state '1'
qr = QuantumRegister(3, name="q")
crz, crx = ClassicalRegister(1, name="crz"), ClassicalRegister(1, name="crx")
teleportation_circuit = QuantumCircuit(qr, crz, crx)
## STEP 1
create_bell_pair(teleportation_circuit, 1, 2)
## STEP 2
teleportation_circuit.barrier() # Use barrier to separate steps
alice_gates(teleportation_circuit, 0, 1)
## STEP 3
measure_and_send(teleportation_circuit, 0, 1)
## STEP 4
teleportation_circuit.barrier() # Use barrier to separate steps
bob_gates(teleportation_circuit, 2, crz, crx)
teleportation_circuit.draw()
# Create random 1-qubit state
psi = random_statevector(2)
# Display it nicely
display(array_to_latex(psi, prefix="|\\psi\\rangle ="))
# Show it on a Bloch sphere
plot_bloch_multivector(psi)
init_gate = Initialize(psi)
init_gate.label = "init"
## SETUP
qr = QuantumRegister(3, name="q") # Protocol uses 3 qubits
crz = ClassicalRegister(1, name="crz") # and 2 classical registers
crx = ClassicalRegister(1, name="crx")
qc = QuantumCircuit(qr, crz, crx)
## STEP 0
# First, let's initialize Alice's q0
qc.append(init_gate, [0])
qc.barrier()
## STEP 1
# Now begins the teleportation protocol
create_bell_pair(qc, 1, 2)
qc.barrier()
## STEP 2
# Send q1 to Alice and q2 to Bob
alice_gates(qc, 0, 1)
## STEP 3
# Alice then sends her classical bits to Bob
measure_and_send(qc, 0, 1)
## STEP 4
# Bob decodes qubits
bob_gates(qc, 2, crz, crx)
# Display the circuit
qc.draw()
sim = Aer.get_backend('aer_simulator')
qc.save_statevector()
out_vector = sim.run(qc).result().get_statevector()
plot_bloch_multivector(out_vector)
|
https://github.com/yonahirakawa/qiskit-iniciantes
|
yonahirakawa
|
from qiskit.visualization import plot_bloch_vector
plot_bloch_vector([1,0,0], title="Esfera de Bloch")
from qiskit.visualization import plot_bloch_vector, bloch
from matplotlib.pyplot import text
from math import pi, cos, sin
from qutip import *
# angles that represent our state
theta = pi/2
phi = 4*pi/3
# calculating the coordinates
x = sin(theta)*cos(phi)
y = sin(theta)*sin(phi)
z = cos(theta)
# preparing the sphere
sphere = bloch
sphere.Bloch
b = Bloch()
# preparing the lables
b.ylpos = [1.1, -1.2]
b.xlabel = ['$\\left|0\\right>+\\left|1\\right>$', '$\\left|0\\right>-\\left|1\\right>$']
b.ylabel = ['$\\left|0\\right>+i\\left|1\\right>$', '$\\left|0\\right>-i\\left|1\\right>$']
# first 6 drawn vectors will be blue, the 7th - red
b.vector_color = ['b','b','b','b','b','b','r']
# drawing vectors of orthogonal states (most popular bases), note the coordinates of vectors,
# they correspond to the according states.
b.add_vectors([[0,0,1],[0,0,-1],[0,1,0],[0,-1,0],[1,0,0],[-1,0,0]])
# drawing our state (as 7th vector)
b.add_vectors([x,y,z])
# showing the Bloch sphere with all that we have prepared
b.show()
|
https://github.com/harshagarine/QISKIT_INDIA_CHALLENGE
|
harshagarine
|
### WRITE YOUR CODE BETWEEN THESE LINES - START
# import libraries that are used in the function below.
from qiskit import QuantumCircuit
import numpy as np
from math import sqrt, pi
### WRITE YOUR CODE BETWEEN THESE LINES - END
def build_state():
# create a quantum circuit on one qubit
circuit = QuantumCircuit(1)
### WRITE YOUR CODE BETWEEN THESE LINES - START
init = [1/sqrt(2),0-1j/sqrt(2)]
circuit.initialize(init,0)
# apply necessary gates
circuit.rx(pi/2,0)
### WRITE YOUR CODE BETWEEN THESE LINES - END
return circuit
|
https://github.com/swe-train/qiskit__qiskit
|
swe-train
|
# 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 derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""
Testing InverseCancellation
"""
import unittest
import numpy as np
from qiskit import QuantumCircuit
from qiskit.transpiler.exceptions import TranspilerError
from qiskit.transpiler.passes import InverseCancellation
from qiskit.transpiler import PassManager
from qiskit.test import QiskitTestCase
from qiskit.circuit.library import RXGate, HGate, CXGate, PhaseGate, XGate, TGate, TdgGate
class TestInverseCancellation(QiskitTestCase):
"""Test the InverseCancellation transpiler pass."""
def test_basic_self_inverse(self):
"""Test that a single self-inverse gate as input can be cancelled."""
qc = QuantumCircuit(2, 2)
qc.h(0)
qc.h(0)
pass_ = InverseCancellation([HGate()])
pm = PassManager(pass_)
new_circ = pm.run(qc)
gates_after = new_circ.count_ops()
self.assertNotIn("h", gates_after)
def test_odd_number_self_inverse(self):
"""Test that an odd number of self-inverse gates leaves one gate remaining."""
qc = QuantumCircuit(2, 2)
qc.h(0)
qc.h(0)
qc.h(0)
pass_ = InverseCancellation([HGate()])
pm = PassManager(pass_)
new_circ = pm.run(qc)
gates_after = new_circ.count_ops()
self.assertIn("h", gates_after)
self.assertEqual(gates_after["h"], 1)
def test_basic_cx_self_inverse(self):
"""Test that a single self-inverse cx gate as input can be cancelled."""
qc = QuantumCircuit(2, 2)
qc.cx(0, 1)
qc.cx(0, 1)
pass_ = InverseCancellation([CXGate()])
pm = PassManager(pass_)
new_circ = pm.run(qc)
gates_after = new_circ.count_ops()
self.assertNotIn("cx", gates_after)
def test_basic_gate_inverse(self):
"""Test that a basic pair of gate inverse can be cancelled."""
qc = QuantumCircuit(2, 2)
qc.rx(np.pi / 4, 0)
qc.rx(-np.pi / 4, 0)
pass_ = InverseCancellation([(RXGate(np.pi / 4), RXGate(-np.pi / 4))])
pm = PassManager(pass_)
new_circ = pm.run(qc)
gates_after = new_circ.count_ops()
self.assertNotIn("rx", gates_after)
def test_non_inverse_do_not_cancel(self):
"""Test that non-inverse gate pairs do not cancel."""
qc = QuantumCircuit(2, 2)
qc.rx(np.pi / 4, 0)
qc.rx(np.pi / 4, 0)
pass_ = InverseCancellation([(RXGate(np.pi / 4), RXGate(-np.pi / 4))])
pm = PassManager(pass_)
new_circ = pm.run(qc)
gates_after = new_circ.count_ops()
self.assertIn("rx", gates_after)
self.assertEqual(gates_after["rx"], 2)
def test_non_consecutive_gates(self):
"""Test that only consecutive gates cancel."""
qc = QuantumCircuit(2, 2)
qc.h(0)
qc.h(0)
qc.h(0)
qc.cx(0, 1)
qc.cx(0, 1)
qc.h(0)
pass_ = InverseCancellation([HGate(), CXGate()])
pm = PassManager(pass_)
new_circ = pm.run(qc)
gates_after = new_circ.count_ops()
self.assertNotIn("cx", gates_after)
self.assertEqual(gates_after["h"], 2)
def test_gate_inverse_phase_gate(self):
"""Test that an inverse pair of a PhaseGate can be cancelled."""
qc = QuantumCircuit(2, 2)
qc.p(np.pi / 4, 0)
qc.p(-np.pi / 4, 0)
pass_ = InverseCancellation([(PhaseGate(np.pi / 4), PhaseGate(-np.pi / 4))])
pm = PassManager(pass_)
new_circ = pm.run(qc)
gates_after = new_circ.count_ops()
self.assertNotIn("p", gates_after)
def test_self_inverse_on_different_qubits(self):
"""Test that self_inverse gates cancel on the correct qubits."""
qc = QuantumCircuit(2, 2)
qc.h(0)
qc.h(1)
qc.h(0)
qc.h(1)
pass_ = InverseCancellation([HGate()])
pm = PassManager(pass_)
new_circ = pm.run(qc)
gates_after = new_circ.count_ops()
self.assertNotIn("h", gates_after)
def test_non_inverse_raise_error(self):
"""Test that non-inverse gate inputs raise an error."""
qc = QuantumCircuit(2, 2)
qc.rx(np.pi / 2, 0)
qc.rx(np.pi / 4, 0)
with self.assertRaises(TranspilerError):
InverseCancellation([RXGate(0.5)])
def test_non_gate_inverse_raise_error(self):
"""Test that non-inverse gate inputs raise an error."""
qc = QuantumCircuit(2, 2)
qc.rx(np.pi / 4, 0)
qc.rx(np.pi / 4, 0)
with self.assertRaises(TranspilerError):
InverseCancellation([(RXGate(np.pi / 4))])
def test_string_gate_error(self):
"""Test that when gate is passed as a string an error is raised."""
qc = QuantumCircuit(2, 2)
qc.h(0)
qc.h(0)
with self.assertRaises(TranspilerError):
InverseCancellation(["h"])
def test_consecutive_self_inverse_h_x_gate(self):
"""Test that only consecutive self-inverse gates cancel."""
qc = QuantumCircuit(2, 2)
qc.h(0)
qc.h(0)
qc.h(0)
qc.x(0)
qc.x(0)
qc.h(0)
pass_ = InverseCancellation([HGate(), XGate()])
pm = PassManager(pass_)
new_circ = pm.run(qc)
gates_after = new_circ.count_ops()
self.assertNotIn("x", gates_after)
self.assertEqual(gates_after["h"], 2)
def test_inverse_with_different_names(self):
"""Test that inverse gates that have different names."""
qc = QuantumCircuit(2, 2)
qc.t(0)
qc.tdg(0)
pass_ = InverseCancellation([(TGate(), TdgGate())])
pm = PassManager(pass_)
new_circ = pm.run(qc)
gates_after = new_circ.count_ops()
self.assertNotIn("t", gates_after)
self.assertNotIn("tdg", gates_after)
def test_three_alternating_inverse_gates(self):
"""Test that inverse cancellation works correctly for alternating sequences
of inverse gates of odd-length."""
qc = QuantumCircuit(2, 2)
qc.p(np.pi / 4, 0)
qc.p(-np.pi / 4, 0)
qc.p(np.pi / 4, 0)
pass_ = InverseCancellation([(PhaseGate(np.pi / 4), PhaseGate(-np.pi / 4))])
pm = PassManager(pass_)
new_circ = pm.run(qc)
gates_after = new_circ.count_ops()
self.assertIn("p", gates_after)
self.assertEqual(gates_after["p"], 1)
def test_four_alternating_inverse_gates(self):
"""Test that inverse cancellation works correctly for alternating sequences
of inverse gates of even-length."""
qc = QuantumCircuit(2, 2)
qc.p(np.pi / 4, 0)
qc.p(-np.pi / 4, 0)
qc.p(np.pi / 4, 0)
qc.p(-np.pi / 4, 0)
pass_ = InverseCancellation([(PhaseGate(np.pi / 4), PhaseGate(-np.pi / 4))])
pm = PassManager(pass_)
new_circ = pm.run(qc)
gates_after = new_circ.count_ops()
self.assertNotIn("p", gates_after)
def test_five_alternating_inverse_gates(self):
"""Test that inverse cancellation works correctly for alternating sequences
of inverse gates of odd-length."""
qc = QuantumCircuit(2, 2)
qc.p(np.pi / 4, 0)
qc.p(-np.pi / 4, 0)
qc.p(np.pi / 4, 0)
qc.p(-np.pi / 4, 0)
qc.p(np.pi / 4, 0)
pass_ = InverseCancellation([(PhaseGate(np.pi / 4), PhaseGate(-np.pi / 4))])
pm = PassManager(pass_)
new_circ = pm.run(qc)
gates_after = new_circ.count_ops()
self.assertIn("p", gates_after)
self.assertEqual(gates_after["p"], 1)
def test_sequence_of_inverse_gates_1(self):
"""Test that inverse cancellation works correctly for more general sequences
of inverse gates. In this test two pairs of inverse gates are supposed to
cancel out."""
qc = QuantumCircuit(2, 2)
qc.p(np.pi / 4, 0)
qc.p(-np.pi / 4, 0)
qc.p(-np.pi / 4, 0)
qc.p(np.pi / 4, 0)
qc.p(np.pi / 4, 0)
pass_ = InverseCancellation([(PhaseGate(np.pi / 4), PhaseGate(-np.pi / 4))])
pm = PassManager(pass_)
new_circ = pm.run(qc)
gates_after = new_circ.count_ops()
self.assertIn("p", gates_after)
self.assertEqual(gates_after["p"], 1)
def test_sequence_of_inverse_gates_2(self):
"""Test that inverse cancellation works correctly for more general sequences
of inverse gates. In this test, in theory three pairs of inverse gates can
cancel out, but in practice only two pairs are back-to-back."""
qc = QuantumCircuit(2, 2)
qc.p(np.pi / 4, 0)
qc.p(np.pi / 4, 0)
qc.p(-np.pi / 4, 0)
qc.p(-np.pi / 4, 0)
qc.p(-np.pi / 4, 0)
qc.p(np.pi / 4, 0)
qc.p(np.pi / 4, 0)
pass_ = InverseCancellation([(PhaseGate(np.pi / 4), PhaseGate(-np.pi / 4))])
pm = PassManager(pass_)
new_circ = pm.run(qc)
gates_after = new_circ.count_ops()
self.assertIn("p", gates_after)
self.assertEqual(gates_after["p"] % 2, 1)
def test_cx_do_not_wrongly_cancel(self):
"""Test that CX(0,1) and CX(1, 0) do not cancel out, when (CX, CX) is passed
as an inverse pair."""
qc = QuantumCircuit(2, 0)
qc.cx(0, 1)
qc.cx(1, 0)
pass_ = InverseCancellation([(CXGate(), CXGate())])
pm = PassManager(pass_)
new_circ = pm.run(qc)
gates_after = new_circ.count_ops()
self.assertIn("cx", gates_after)
self.assertEqual(gates_after["cx"], 2)
if __name__ == "__main__":
unittest.main()
|
https://github.com/qiskit-community/qiskit-translations-staging
|
qiskit-community
|
#General imports
import numpy as np
#Operator Imports
from qiskit.opflow import Z, X, I, StateFn, CircuitStateFn, SummedOp
from qiskit.opflow.gradients import Gradient, NaturalGradient, QFI, Hessian
#Circuit imports
from qiskit.circuit import QuantumCircuit, QuantumRegister, Parameter, ParameterVector, ParameterExpression
from qiskit.circuit.library import EfficientSU2
# Instantiate the quantum state
a = Parameter('a')
b = Parameter('b')
q = QuantumRegister(1)
qc = QuantumCircuit(q)
qc.h(q)
qc.rz(a, q[0])
qc.rx(b, q[0])
# Instantiate the Hamiltonian observable
H = (2 * X) + Z
# Combine the Hamiltonian observable and the state
op = ~StateFn(H) @ CircuitStateFn(primitive=qc, coeff=1.)
# Print the operator corresponding to the expectation value
print(op)
params = [a, b]
# Define the values to be assigned to the parameters
value_dict = {a: np.pi / 4, b: np.pi}
# Convert the operator and the gradient target params into the respective operator
grad = Gradient().convert(operator = op, params = params)
# Print the operator corresponding to the Gradient
print(grad)
# Assign the parameters and evaluate the gradient
grad_result = grad.assign_parameters(value_dict).eval()
print('Gradient', grad_result)
# Define the Hamiltonian with fixed coefficients
H = 0.5 * X - 1 * Z
# Define the parameters w.r.t. we want to compute the gradients
params = [a, b]
# Define the values to be assigned to the parameters
value_dict = { a: np.pi / 4, b: np.pi}
# Combine the Hamiltonian observable and the state into an expectation value operator
op = ~StateFn(H) @ CircuitStateFn(primitive=qc, coeff=1.)
print(op)
# Convert the expectation value into an operator corresponding to the gradient w.r.t. the state parameters using
# the parameter shift method.
state_grad = Gradient(grad_method='param_shift').convert(operator=op, params=params)
# Print the operator corresponding to the gradient
print(state_grad)
# Assign the parameters and evaluate the gradient
state_grad_result = state_grad.assign_parameters(value_dict).eval()
print('State gradient computed with parameter shift', state_grad_result)
# Convert the expectation value into an operator corresponding to the gradient w.r.t. the state parameter using
# the linear combination of unitaries method.
state_grad = Gradient(grad_method='lin_comb').convert(operator=op, params=params)
# Print the operator corresponding to the gradient
print(state_grad)
# Assign the parameters and evaluate the gradient
state_grad_result = state_grad.assign_parameters(value_dict).eval()
print('State gradient computed with the linear combination method', state_grad_result)
# Convert the expectation value into an operator corresponding to the gradient w.r.t. the state parameter using
# the finite difference method.
state_grad = Gradient(grad_method='fin_diff').convert(operator=op, params=params)
# Print the operator corresponding to the gradient
print(state_grad)
# Assign the parameters and evaluate the gradient
state_grad_result = state_grad.assign_parameters(value_dict).eval()
print('State gradient computed with finite difference', state_grad_result)
# Besides the method to compute the circuit gradients resp. QFI, a regularization method can be chosen:
# `ridge` or `lasso` with automatic parameter search or `perturb_diag_elements` or `perturb_diag`
# which perturb the diagonal elements of the QFI.
nat_grad = NaturalGradient(grad_method='lin_comb', qfi_method='lin_comb_full', regularization='ridge').convert(
operator=op, params=params)
# Assign the parameters and evaluate the gradient
nat_grad_result = nat_grad.assign_parameters(value_dict).eval()
print('Natural gradient computed with linear combination of unitaries', nat_grad_result)
# Instantiate the Hamiltonian observable
H = X
# Instantiate the quantum state with two parameters
a = Parameter('a')
b = Parameter('b')
q = QuantumRegister(1)
qc = QuantumCircuit(q)
qc.h(q)
qc.rz(a, q[0])
qc.rx(b, q[0])
# Combine the Hamiltonian observable and the state
op = ~StateFn(H) @ CircuitStateFn(primitive=qc, coeff=1.)
# Convert the operator and the hessian target coefficients into the respective operator
hessian = Hessian().convert(operator = op, params = [a, b])
# Define the values to be assigned to the parameters
value_dict = {a: np.pi / 4, b: np.pi/4}
# Assign the parameters and evaluate the Hessian w.r.t. the Hamiltonian coefficients
hessian_result = hessian.assign_parameters(value_dict).eval()
print('Hessian \n', np.real(np.array(hessian_result)))
# Define parameters
params = [a, b]
# Get the operator object representing the Hessian
state_hess = Hessian(hess_method='param_shift').convert(operator=op, params=params)
# Assign the parameters and evaluate the Hessian
hessian_result = state_hess.assign_parameters(value_dict).eval()
print('Hessian computed using the parameter shift method\n', (np.array(hessian_result)))
# Get the operator object representing the Hessian
state_hess = Hessian(hess_method='lin_comb').convert(operator=op, params=params)
# Assign the parameters and evaluate the Hessian
hessian_result = state_hess.assign_parameters(value_dict).eval()
print('Hessian computed using the linear combination of unitaries method\n', (np.array(hessian_result)))
# Get the operator object representing the Hessian using finite difference
state_hess = Hessian(hess_method='fin_diff').convert(operator=op, params=params)
# Assign the parameters and evaluate the Hessian
hessian_result = state_hess.assign_parameters(value_dict).eval()
print('Hessian computed with finite difference\n', (np.array(hessian_result)))
# Wrap the quantum circuit into a CircuitStateFn
state = CircuitStateFn(primitive=qc, coeff=1.)
# Convert the state and the parameters into the operator object that represents the QFI
qfi = QFI(qfi_method='lin_comb_full').convert(operator=state, params=params)
# Define the values for which the QFI is to be computed
values_dict = {a: np.pi / 4, b: 0.1}
# Assign the parameters and evaluate the QFI
qfi_result = qfi.assign_parameters(values_dict).eval()
print('full QFI \n', np.real(np.array(qfi_result)))
# Convert the state and the parameters into the operator object that represents the QFI
# and set the approximation to 'block_diagonal'
qfi = QFI('overlap_block_diag').convert(operator=state, params=params)
# Assign the parameters and evaluate the QFI
qfi_result = qfi.assign_parameters(values_dict).eval()
print('Block-diagonal QFI \n', np.real(np.array(qfi_result)))
# Convert the state and the parameters into the operator object that represents the QFI
# and set the approximation to 'diagonal'
qfi = QFI('overlap_diag').convert(operator=state, params=params)
# Assign the parameters and evaluate the QFI
qfi_result = qfi.assign_parameters(values_dict).eval()
print('Diagonal QFI \n', np.real(np.array(qfi_result)))
# Execution Imports
from qiskit import Aer
from qiskit.utils import QuantumInstance
# Algorithm Imports
from qiskit.algorithms import VQE
from qiskit.algorithms.optimizers import CG
from qiskit.opflow import I, X, Z
from qiskit.circuit import QuantumCircuit, ParameterVector
from scipy.optimize import minimize
# Instantiate the system Hamiltonian
h2_hamiltonian = -1.05 * (I ^ I) + 0.39 * (I ^ Z) - 0.39 * (Z ^ I) - 0.01 * (Z ^ Z) + 0.18 * (X ^ X)
# This is the target energy
h2_energy = -1.85727503
# Define the Ansatz
wavefunction = QuantumCircuit(2)
params = ParameterVector('theta', length=8)
it = iter(params)
wavefunction.ry(next(it), 0)
wavefunction.ry(next(it), 1)
wavefunction.rz(next(it), 0)
wavefunction.rz(next(it), 1)
wavefunction.cx(0, 1)
wavefunction.ry(next(it), 0)
wavefunction.ry(next(it), 1)
wavefunction.rz(next(it), 0)
wavefunction.rz(next(it), 1)
# Define the expectation value corresponding to the energy
op = ~StateFn(h2_hamiltonian) @ StateFn(wavefunction)
grad = Gradient(grad_method='lin_comb')
qi_sv = QuantumInstance(Aer.get_backend('aer_simulator_statevector'),
shots=1,
seed_simulator=2,
seed_transpiler=2)
#Conjugate Gradient algorithm
optimizer = CG(maxiter=50)
# Gradient callable
vqe = VQE(wavefunction, optimizer=optimizer, gradient=grad, quantum_instance=qi_sv)
result = vqe.compute_minimum_eigenvalue(h2_hamiltonian)
print('Result:', result.optimal_value, 'Reference:', h2_energy)
import qiskit.tools.jupyter
%qiskit_version_table
%qiskit_copyright
|
https://github.com/2lambda123/Qiskit-qiskit
|
2lambda123
|
# This code is part of Qiskit.
#
# (C) Copyright IBM 2017, 2021.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
# pylint: disable=invalid-name
"""Tests for error mitigation routines."""
import unittest
from collections import Counter
import numpy as np
from qiskit import QiskitError, QuantumCircuit
from qiskit.quantum_info import Statevector
from qiskit.quantum_info.operators.predicates import matrix_equal
from qiskit.result import (
CorrelatedReadoutMitigator,
Counts,
LocalReadoutMitigator,
)
from qiskit.result.mitigation.utils import (
counts_probability_vector,
expval_with_stddev,
stddev,
str2diag,
)
from qiskit.result.utils import marginal_counts
from qiskit.test import QiskitTestCase
from qiskit.providers.fake_provider import FakeYorktown
class TestReadoutMitigation(QiskitTestCase):
"""Tests for correlated and local readout mitigation."""
@classmethod
def setUpClass(cls):
super().setUpClass()
cls.rng = np.random.default_rng(42)
@staticmethod
def compare_results(res1, res2):
"""Compare the results between two runs"""
res1_total_shots = sum(res1.values())
res2_total_shots = sum(res2.values())
keys = set(res1.keys()).union(set(res2.keys()))
total = 0
for key in keys:
val1 = res1.get(key, 0) / res1_total_shots
val2 = res2.get(key, 0) / res2_total_shots
total += abs(val1 - val2) ** 2
return total
@staticmethod
def mitigators(assignment_matrices, qubits=None):
"""Generates the mitigators to test for given assignment matrices"""
full_assignment_matrix = assignment_matrices[0]
for m in assignment_matrices[1:]:
full_assignment_matrix = np.kron(full_assignment_matrix, m)
CRM = CorrelatedReadoutMitigator(full_assignment_matrix, qubits)
LRM = LocalReadoutMitigator(assignment_matrices, qubits)
mitigators = [CRM, LRM]
return mitigators
@staticmethod
def simulate_circuit(circuit, assignment_matrix, num_qubits, shots=1024):
"""Simulates the given circuit under the given readout noise"""
probs = Statevector.from_instruction(circuit).probabilities()
noisy_probs = assignment_matrix @ probs
labels = [bin(a)[2:].zfill(num_qubits) for a in range(2**num_qubits)]
results = TestReadoutMitigation.rng.choice(labels, size=shots, p=noisy_probs)
return Counts(dict(Counter(results)))
@staticmethod
def ghz_3_circuit():
"""A 3-qubit circuit generating |000>+|111>"""
c = QuantumCircuit(3)
c.h(0)
c.cx(0, 1)
c.cx(1, 2)
return (c, "ghz_3_ciruit", 3)
@staticmethod
def first_qubit_h_3_circuit():
"""A 3-qubit circuit generating |000>+|001>"""
c = QuantumCircuit(3)
c.h(0)
return (c, "first_qubit_h_3_circuit", 3)
@staticmethod
def assignment_matrices():
"""A 3-qubit readout noise assignment matrices"""
return LocalReadoutMitigator(backend=FakeYorktown())._assignment_mats[0:3]
@staticmethod
def counts_data(circuit, assignment_matrices, shots=1024):
"""Generates count data for the noisy and noiseless versions of the circuit simulation"""
full_assignment_matrix = assignment_matrices[0]
for m in assignment_matrices[1:]:
full_assignment_matrix = np.kron(full_assignment_matrix, m)
num_qubits = len(assignment_matrices)
ideal_assignment_matrix = np.eye(2**num_qubits)
counts_ideal = TestReadoutMitigation.simulate_circuit(
circuit, ideal_assignment_matrix, num_qubits, shots
)
counts_noise = TestReadoutMitigation.simulate_circuit(
circuit, full_assignment_matrix, num_qubits, shots
)
probs_noise = {key: value / shots for key, value in counts_noise.items()}
return counts_ideal, counts_noise, probs_noise
def test_mitigation_improvement(self):
"""Test whether readout mitigation led to more accurate results"""
shots = 1024
assignment_matrices = self.assignment_matrices()
num_qubits = len(assignment_matrices)
mitigators = self.mitigators(assignment_matrices)
circuit, circuit_name, num_qubits = self.ghz_3_circuit()
counts_ideal, counts_noise, probs_noise = self.counts_data(
circuit, assignment_matrices, shots
)
unmitigated_error = self.compare_results(counts_ideal, counts_noise)
unmitigated_stddev = stddev(probs_noise, shots)
for mitigator in mitigators:
mitigated_quasi_probs = mitigator.quasi_probabilities(counts_noise)
mitigated_probs = (
mitigated_quasi_probs.nearest_probability_distribution().binary_probabilities(
num_bits=num_qubits
)
)
mitigated_error = self.compare_results(counts_ideal, mitigated_probs)
self.assertLess(
mitigated_error,
unmitigated_error * 0.8,
"Mitigator {} did not improve circuit {} measurements".format(
mitigator, circuit_name
),
)
mitigated_stddev_upper_bound = mitigated_quasi_probs._stddev_upper_bound
max_unmitigated_stddev = max(unmitigated_stddev.values())
self.assertGreaterEqual(
mitigated_stddev_upper_bound,
max_unmitigated_stddev,
"Mitigator {} on circuit {} gave stddev upper bound {} "
"while unmitigated stddev maximum is {}".format(
mitigator,
circuit_name,
mitigated_stddev_upper_bound,
max_unmitigated_stddev,
),
)
def test_expectation_improvement(self):
"""Test whether readout mitigation led to more accurate results
and that its standard deviation is increased"""
shots = 1024
assignment_matrices = self.assignment_matrices()
mitigators = self.mitigators(assignment_matrices)
num_qubits = len(assignment_matrices)
diagonals = []
diagonals.append("IZ0")
diagonals.append("101")
diagonals.append("IZZ")
qubit_index = {i: i for i in range(num_qubits)}
circuit, circuit_name, num_qubits = self.ghz_3_circuit()
counts_ideal, counts_noise, _ = self.counts_data(circuit, assignment_matrices, shots)
probs_ideal, _ = counts_probability_vector(counts_ideal, qubit_index=qubit_index)
probs_noise, _ = counts_probability_vector(counts_noise, qubit_index=qubit_index)
for diagonal in diagonals:
if isinstance(diagonal, str):
diagonal = str2diag(diagonal)
unmitigated_expectation, unmitigated_stddev = expval_with_stddev(
diagonal, probs_noise, shots=counts_noise.shots()
)
ideal_expectation = np.dot(probs_ideal, diagonal)
unmitigated_error = np.abs(ideal_expectation - unmitigated_expectation)
for mitigator in mitigators:
mitigated_expectation, mitigated_stddev = mitigator.expectation_value(
counts_noise, diagonal
)
mitigated_error = np.abs(ideal_expectation - mitigated_expectation)
self.assertLess(
mitigated_error,
unmitigated_error,
"Mitigator {} did not improve circuit {} expectation computation for diagonal {} "
"ideal: {}, unmitigated: {} mitigated: {}".format(
mitigator,
circuit_name,
diagonal,
ideal_expectation,
unmitigated_expectation,
mitigated_expectation,
),
)
self.assertGreaterEqual(
mitigated_stddev,
unmitigated_stddev,
"Mitigator {} did not increase circuit {} the standard deviation".format(
mitigator, circuit_name
),
)
def test_clbits_parameter(self):
"""Test whether the clbits parameter is handled correctly"""
shots = 10000
assignment_matrices = self.assignment_matrices()
mitigators = self.mitigators(assignment_matrices)
circuit, _, _ = self.first_qubit_h_3_circuit()
counts_ideal, counts_noise, _ = self.counts_data(circuit, assignment_matrices, shots)
counts_ideal_12 = marginal_counts(counts_ideal, [1, 2])
counts_ideal_02 = marginal_counts(counts_ideal, [0, 2])
for mitigator in mitigators:
mitigated_probs_12 = (
mitigator.quasi_probabilities(counts_noise, qubits=[1, 2], clbits=[1, 2])
.nearest_probability_distribution()
.binary_probabilities(num_bits=2)
)
mitigated_error = self.compare_results(counts_ideal_12, mitigated_probs_12)
self.assertLess(
mitigated_error,
0.001,
"Mitigator {} did not correctly marganalize for qubits 1,2".format(mitigator),
)
mitigated_probs_02 = (
mitigator.quasi_probabilities(counts_noise, qubits=[0, 2], clbits=[0, 2])
.nearest_probability_distribution()
.binary_probabilities(num_bits=2)
)
mitigated_error = self.compare_results(counts_ideal_02, mitigated_probs_02)
self.assertLess(
mitigated_error,
0.001,
"Mitigator {} did not correctly marganalize for qubits 0,2".format(mitigator),
)
def test_qubits_parameter(self):
"""Test whether the qubits parameter is handled correctly"""
shots = 10000
assignment_matrices = self.assignment_matrices()
mitigators = self.mitigators(assignment_matrices)
circuit, _, _ = self.first_qubit_h_3_circuit()
counts_ideal, counts_noise, _ = self.counts_data(circuit, assignment_matrices, shots)
counts_ideal_012 = counts_ideal
counts_ideal_210 = Counts({"000": counts_ideal["000"], "100": counts_ideal["001"]})
counts_ideal_102 = Counts({"000": counts_ideal["000"], "010": counts_ideal["001"]})
for mitigator in mitigators:
mitigated_probs_012 = (
mitigator.quasi_probabilities(counts_noise, qubits=[0, 1, 2])
.nearest_probability_distribution()
.binary_probabilities(num_bits=3)
)
mitigated_error = self.compare_results(counts_ideal_012, mitigated_probs_012)
self.assertLess(
mitigated_error,
0.001,
"Mitigator {} did not correctly handle qubit order 0, 1, 2".format(mitigator),
)
mitigated_probs_210 = (
mitigator.quasi_probabilities(counts_noise, qubits=[2, 1, 0])
.nearest_probability_distribution()
.binary_probabilities(num_bits=3)
)
mitigated_error = self.compare_results(counts_ideal_210, mitigated_probs_210)
self.assertLess(
mitigated_error,
0.001,
"Mitigator {} did not correctly handle qubit order 2, 1, 0".format(mitigator),
)
mitigated_probs_102 = (
mitigator.quasi_probabilities(counts_noise, qubits=[1, 0, 2])
.nearest_probability_distribution()
.binary_probabilities(num_bits=3)
)
mitigated_error = self.compare_results(counts_ideal_102, mitigated_probs_102)
self.assertLess(
mitigated_error,
0.001,
"Mitigator {} did not correctly handle qubit order 1, 0, 2".format(mitigator),
)
def test_repeated_qubits_parameter(self):
"""Tests the order of mitigated qubits."""
shots = 10000
assignment_matrices = self.assignment_matrices()
mitigators = self.mitigators(assignment_matrices, qubits=[0, 1, 2])
circuit, _, _ = self.first_qubit_h_3_circuit()
counts_ideal, counts_noise, _ = self.counts_data(circuit, assignment_matrices, shots)
counts_ideal_012 = counts_ideal
counts_ideal_210 = Counts({"000": counts_ideal["000"], "100": counts_ideal["001"]})
for mitigator in mitigators:
mitigated_probs_210 = (
mitigator.quasi_probabilities(counts_noise, qubits=[2, 1, 0])
.nearest_probability_distribution()
.binary_probabilities(num_bits=3)
)
mitigated_error = self.compare_results(counts_ideal_210, mitigated_probs_210)
self.assertLess(
mitigated_error,
0.001,
"Mitigator {} did not correctly handle qubit order 2,1,0".format(mitigator),
)
# checking qubit order 2,1,0 should not "overwrite" the default 0,1,2
mitigated_probs_012 = (
mitigator.quasi_probabilities(counts_noise)
.nearest_probability_distribution()
.binary_probabilities(num_bits=3)
)
mitigated_error = self.compare_results(counts_ideal_012, mitigated_probs_012)
self.assertLess(
mitigated_error,
0.001,
"Mitigator {} did not correctly handle qubit order 0,1,2 (the expected default)".format(
mitigator
),
)
def test_qubits_subset_parameter(self):
"""Tests mitigation on a subset of the initial set of qubits."""
shots = 10000
assignment_matrices = self.assignment_matrices()
mitigators = self.mitigators(assignment_matrices, qubits=[2, 4, 6])
circuit, _, _ = self.first_qubit_h_3_circuit()
counts_ideal, counts_noise, _ = self.counts_data(circuit, assignment_matrices, shots)
counts_ideal_2 = marginal_counts(counts_ideal, [0])
counts_ideal_6 = marginal_counts(counts_ideal, [2])
for mitigator in mitigators:
mitigated_probs_2 = (
mitigator.quasi_probabilities(counts_noise, qubits=[2])
.nearest_probability_distribution()
.binary_probabilities(num_bits=1)
)
mitigated_error = self.compare_results(counts_ideal_2, mitigated_probs_2)
self.assertLess(
mitigated_error,
0.001,
"Mitigator {} did not correctly handle qubit subset".format(mitigator),
)
mitigated_probs_6 = (
mitigator.quasi_probabilities(counts_noise, qubits=[6])
.nearest_probability_distribution()
.binary_probabilities(num_bits=1)
)
mitigated_error = self.compare_results(counts_ideal_6, mitigated_probs_6)
self.assertLess(
mitigated_error,
0.001,
"Mitigator {} did not correctly handle qubit subset".format(mitigator),
)
diagonal = str2diag("ZZ")
ideal_expectation = 0
mitigated_expectation, _ = mitigator.expectation_value(
counts_noise, diagonal, qubits=[2, 6]
)
mitigated_error = np.abs(ideal_expectation - mitigated_expectation)
self.assertLess(
mitigated_error,
0.1,
"Mitigator {} did not improve circuit expectation".format(mitigator),
)
def test_from_backend(self):
"""Test whether a local mitigator can be created directly from backend properties"""
backend = FakeYorktown()
num_qubits = len(backend.properties().qubits)
probs = TestReadoutMitigation.rng.random((num_qubits, 2))
for qubit_idx, qubit_prop in enumerate(backend.properties().qubits):
for prop in qubit_prop:
if prop.name == "prob_meas1_prep0":
prop.value = probs[qubit_idx][0]
if prop.name == "prob_meas0_prep1":
prop.value = probs[qubit_idx][1]
LRM_from_backend = LocalReadoutMitigator(backend=backend)
mats = []
for qubit_idx in range(num_qubits):
mat = np.array(
[
[1 - probs[qubit_idx][0], probs[qubit_idx][1]],
[probs[qubit_idx][0], 1 - probs[qubit_idx][1]],
]
)
mats.append(mat)
LRM_from_matrices = LocalReadoutMitigator(assignment_matrices=mats)
self.assertTrue(
matrix_equal(
LRM_from_backend.assignment_matrix(), LRM_from_matrices.assignment_matrix()
)
)
def test_error_handling(self):
"""Test that the assignment matrices are valid."""
bad_matrix_A = np.array([[-0.3, 1], [1.3, 0]]) # negative indices
bad_matrix_B = np.array([[0.2, 1], [0.7, 0]]) # columns not summing to 1
good_matrix_A = np.array([[0.2, 1], [0.8, 0]])
for bad_matrix in [bad_matrix_A, bad_matrix_B]:
with self.assertRaises(QiskitError) as cm:
CorrelatedReadoutMitigator(bad_matrix)
self.assertEqual(
cm.exception.message,
"Assignment matrix columns must be valid probability distributions",
)
with self.assertRaises(QiskitError) as cm:
amats = [good_matrix_A, bad_matrix_A]
LocalReadoutMitigator(amats)
self.assertEqual(
cm.exception.message,
"Assignment matrix columns must be valid probability distributions",
)
with self.assertRaises(QiskitError) as cm:
amats = [bad_matrix_B, good_matrix_A]
LocalReadoutMitigator(amats)
self.assertEqual(
cm.exception.message,
"Assignment matrix columns must be valid probability distributions",
)
def test_expectation_value_endian(self):
"""Test that endian for expval is little."""
mitigators = self.mitigators(self.assignment_matrices())
counts = Counts({"10": 3, "11": 24, "00": 74, "01": 923})
for mitigator in mitigators:
expval, _ = mitigator.expectation_value(counts, diagonal="IZ", qubits=[0, 1])
self.assertAlmostEqual(expval, -1.0, places=0)
def test_quasi_probabilities_shots_passing(self):
"""Test output of LocalReadoutMitigator.quasi_probabilities
We require the number of shots to be set in the output.
"""
mitigator = LocalReadoutMitigator([np.array([[0.9, 0.1], [0.1, 0.9]])], qubits=[0])
counts = Counts({"10": 3, "11": 24, "00": 74, "01": 923})
quasi_dist = mitigator.quasi_probabilities(counts)
self.assertEqual(quasi_dist.shots, sum(counts.values()))
# custom number of shots
quasi_dist = mitigator.quasi_probabilities(counts, shots=1025)
self.assertEqual(quasi_dist.shots, 1025)
class TestLocalReadoutMitigation(QiskitTestCase):
"""Tests specific to the local readout mitigator"""
def test_assignment_matrix(self):
"""Tests that the local mitigator generates the full assignment matrix correctly"""
qubits = [7, 2, 3]
assignment_matrices = LocalReadoutMitigator(backend=FakeYorktown())._assignment_mats[0:3]
expected_assignment_matrix = np.kron(
np.kron(assignment_matrices[2], assignment_matrices[1]), assignment_matrices[0]
)
expected_mitigation_matrix = np.linalg.inv(expected_assignment_matrix)
LRM = LocalReadoutMitigator(assignment_matrices, qubits)
self.assertTrue(matrix_equal(expected_mitigation_matrix, LRM.mitigation_matrix()))
self.assertTrue(matrix_equal(expected_assignment_matrix, LRM.assignment_matrix()))
if __name__ == "__main__":
unittest.main()
|
https://github.com/TheGupta2012/QPE-Algorithms
|
TheGupta2012
|
from qiskit import QuantumCircuit, execute, transpile, Aer
from qiskit.extensions import UnitaryGate, Initialize
from qiskit.quantum_info import Statevector
from qiskit.tools.monitor import job_monitor
from qiskit.compiler import assemble
from qiskit.tools.visualization import plot_bloch_vector
from qiskit.tools.visualization import plot_histogram, plot_bloch_multivector
import numpy as np
from time import sleep
import sys
from scipy.stats import unitary_group
import matplotlib.pyplot as plt
x = np.linspace(0.05, 1, 200)
c = np.cos(x)
c2 = (np.cos(x))**2
ex = np.exp(-x)
ex2 = np.exp(-x**2)
x_inv = (1/x)
x_inv2 = (1/x**2)
plt.figure(dpi=120)
plt.title("Cost Function plots", fontsize=20)
plt.xlabel("Theta values", fontsize=16)
plt.ylabel("Metric values", fontsize=16)
plt.plot(x, c, label="$cos(x)$")
plt.plot(x, c2, label="$cos^{2}(x)$")
plt.plot(x, ex, label="$e^{-x}$")
plt.plot(x, ex2, label="$e^{-x^{2}}$")
plt.grid()
plt.legend()
plt.figure(dpi=120)
plt.title("Cost Function plots", fontsize=20)
plt.xlabel("Theta values", fontsize=16)
plt.ylabel("Metric values", fontsize=16)
plt.plot(x, x_inv, label="$\\frac{1}{x}$")
plt.plot(x, x_inv2, label="$\\frac{1}{x^{2}}$")
plt.grid()
plt.legend()
class global_max_SPEA():
def __init__(self, unitary, cost_function, max_metric_val, resolution=100, max_iters=20):
# handle resolution
if not isinstance(resolution, int):
raise TypeError(
"Please enter the number of intervals as an integer value")
if resolution < 10 or resolution > 1e6:
raise ValueError(
"Resolution needs to be atleast 0.1 and greater than 0.000001")
self.resolution = resolution
# handle unitary
if not isinstance(unitary, np.ndarray) and not isinstance(unitary, QuantumCircuit)\
and not isinstance(unitary, UnitaryGate):
raise TypeError(
"A numpy array or Quantum Circuit or UnitaryGate needs to be passed as the unitary matrix")
# convert circuit to numpy array for uniformity
if isinstance(unitary, UnitaryGate):
U = unitary.to_matrix()
else: # both QC and ndarray type
U = unitary
# note - the unitary here is not just a single qubit unitary
if isinstance(U, np.ndarray):
self.dims = U.shape[0]
else:
self.dims = 2**(U.num_qubits)
if isinstance(U, np.ndarray):
self.c_unitary_gate = UnitaryGate(data=U).control(
num_ctrl_qubits=1, label='CU', ctrl_state='1')
else:
self.c_unitary_gate = U.control(
num_ctrl_qubits=1, label='CU', ctrl_state='1')
# handle metric
if not isinstance(max_metric_val, int) and not isinstance(max_metric_val, float):
raise TypeError(
"The required metric value should be provided as an int or a float.")
elif max_metric_val <= 0:
raise ValueError(
"The required metric must be finite and greater than 0.")
elif cost_function in ['cos', 'cos2', 'exp', 'exp2'] and max_metric_val > 1:
raise ValueError(
"Maximum metric can't be greater than 1 in case of given cost function")
self.max_metric = max_metric_val
# handle max_iters
if not isinstance(max_iters, int):
raise TypeError("Max iterations must be of integer type")
if max_iters <= 0 and max_iters > 1e5:
raise ValueError(
"Max iterations should be atleast 1 and less than 1e5")
self.iterations = max_iters
cost_fns = ['cos', 'cos2', 'exp', 'exp2', 'inv', 'inv2']
# handle cost function
if cost_function not in cost_fns:
raise ValueError(
"Cost function must be one of [cos, cos2, exp, exp2, inv, inv2]")
self.cost_function = cost_function
self.basis = []
def __get_basis_vectors(self, randomize=True):
# get the d dimensional basis for the unitary provided
if randomize == True:
UR = unitary_group.rvs(self.dims)
else:
UR = np.identity(self.dims)
basis = []
for k in UR:
basis.append(np.array(k, dtype=complex))
return basis
def __get_unitary_circuit(self, backend):
'''Return the pretranspiled circuit '''
if backend is None:
backend = Aer.get_backend('qasm_simulator')
qc = QuantumCircuit(1 + int(np.log2(self.dims)))
# make the circuit
qc.h(0)
qc = qc.compose(self.c_unitary_gate, qubits=range(
1+int(np.log2(self.dims))))
qc.barrier()
qc = transpile(qc, backend=backend, optimization_level=3)
return qc
def __get_circuit(self, state, backend, shots, angle=None):
'''Given an initial state ,
return the circuit that is generated with
inverse rotation '''
# all theta values are iterated over for the same state
phi = Initialize(state)
qc1 = QuantumCircuit(1 + int(np.log2(self.dims)), 1)
# initialize the circuit
qc1 = qc1.compose(phi, qubits=list(
range(1, int(np.log2(self.dims))+1)))
qc1.barrier()
qc1 = transpile(qc1, backend=backend, optimization_level=1)
# get the circuit2
qc2 = self.unitary_circuit
qc3 = QuantumCircuit(1 + int(np.log2(self.dims)), 1)
if angle is not None:
# add inverse rotation on the first qubit
qc3.p(-2*np.pi*angle, 0)
# add hadamard
qc3.h(0)
qc3 = transpile(qc3, backend=backend, optimization_level=1)
# make final circuit
qc = qc1 + qc2 + qc3
# measure
qc.measure([0], [0])
# qc = assemble(qc,shots = shots)
return qc
def __get_standard_cost(self, angles, state, backend, shots):
'''Given an initial state and a set of angles,
return the best cost and the associated angle
state is a normalized state in ndarray form'''
result = {'cost': -1, 'theta': -1}
# all theta values are iterated over for the same state
circuits = []
for theta in angles:
qc = self.__get_circuit(state, backend, shots, theta)
circuits.append(qc)
# execute only once...
counts = backend.run(circuits, shots=shots).result().get_counts()
# get the cost for this theta
for k, theta in zip(counts, angles):
# for all experiments you ran
try:
C_val = (k['0'])/shots
except:
C_val = 0
if C_val > result['cost']:
# means this is a better theta value
result['theta'] = theta
result['cost'] = C_val
return result
def __get_alternate_cost(self, angles, state, backend, shots):
'''Given an initial state and a set of angles,
return the best cost and the associated angle
state is a normalized state in ndarray form'''
result = {'cost': -1, 'theta': -1}
# all theta values are iterated over for the same state
# run the circuit once
qc = self.__get_circuit(state, backend, shots)
# execute only once...
counts = backend.run(qc, shots=shots).result().get_counts()
# generate experimental probabilities
try:
p0 = counts['0']/shots
except:
p0 = 0
try:
p1 = counts['1']/shots
except:
p1 = 0
# now, find the best theta as specified by the
# alternate method classically
min_s = 1e5
for theta in angles:
# generate theoretical probabilities
c0 = (np.cos(np.pi*theta))**2
c1 = (np.sin(np.pi*theta))**2
# generate s value
s = (p0-c0)**2 + (p1-c1)**2
if s < min_s:
result['theta'] = theta
min_s = s
# now , we have the best theta stored in phi
# run circuit once again to get the value of C*
qc = self.__get_circuit(state, backend, shots, result['theta'])
counts = backend.run(qc, shots=shots).result().get_counts()
try:
result['cost'] = counts['0']/shots
except:
result['cost'] = 0
# no 0 counts present
# return the result
return result
def __get_metric_val(self, cost, theta):
'''Generate optimization metric value
based on the given cost function '''
func = self.cost_function
if func == 'cos':
return cost*(np.cos(theta))
elif func == 'cos2':
return cost*((np.cos(theta))**2)
elif func == 'exp':
return cost*(np.exp(-theta))
elif func == 'exp2':
return cost*(np.exp(-theta**2))
elif func == 'inv':
try:
return cost/theta
except:
return 1e7
elif func == 'inv2':
try:
return cost/(theta**2)
except:
return 1e7
def get_eigen_pair(self, backend, algo='alternate', theta_left=0, theta_right=1,
progress=False, basis=None, basis_ind=None, randomize=True, target_metric=None, shots=512
):
'''Finding the eigenstate pair for the unitary'''
# handle algorithm...
self.unitary_circuit = self.__get_unitary_circuit(backend)
# handle theta bounds
if(theta_left > theta_right):
raise ValueError(
"Left bound for theta should be smaller than the right bound")
elif (theta_left < 0) or (theta_right > 1):
raise ValueError("Bounds of theta are [0,1].")
# handle algorithm
if not isinstance(algo, str):
raise TypeError(
"Algorithm must be mentioned as a string from the values {alternate,standard}")
elif algo not in ['alternate', 'standard']:
raise ValueError(
"Algorithm must be specified as 'alternate' or 'standard' ")
if target_metric is not None:
if (target_metric <= 0):
raise ValueError("Target metric must be a real value > 0")
# handle progress...
if not isinstance(progress, bool):
raise TypeError("Progress must be a boolean variable")
if not isinstance(randomize, bool):
raise Exception("Randomize must be a boolean variable")
results = dict()
# first initialize the state phi
if basis is None:
self.basis = self.__get_basis_vectors(randomize)
else:
# is basis is specified, given as array of vectors...
self.basis = basis
# choose a random index
if basis_ind is None:
ind = np.random.choice(self.dims)
else:
# choose the index given in that basis
ind = basis_ind
phi = self.basis[ind]
# doing the method 1 of our algorithm
# new
if target_metric is None:
max_metric = self.max_metric
else:
if self.cost_function in ['cos','cos2','exp','exp2']:
assert target_metric <= 1 , "For given cost function, target cost can't be greater than 1"
max_metric = target_metric
samples = self.resolution
# initialization of range
left, right = theta_left, theta_right
# generate the angles
angles = np.linspace(left, right, samples)
# iterate once
if algo == 'alternate':
result = self.__get_alternate_cost(angles, phi, backend, shots)
else:
result = self.__get_standard_cost(angles, phi, backend, shots)
# get initial estimates
cost = result['cost']
theta_max = result['theta']
best_phi = phi
global_metric = self.__get_metric_val(cost, theta_max)
# the range upto which theta extends iin each iteration
angle_range = (right - left)/2
# a parameter
a = 1
# start algorithm
iters = 0
found = True
while global_metric < max_metric:
# get angles, note if theta didn't change, then we need to
# again generate the same range again
right = min(theta_right, theta_max + angle_range/2)
left = max(theta_left, theta_max - angle_range/2)
if progress:
print("Right :", right)
print("Left :", left)
# generate the angles only if the theta has been updated
if found == True:
angles = np.linspace(left, right, samples)
found = False # for this iteration
if progress:
print("ITERATION NUMBER", iters+1, "...")
# generate a cost dict for each of the iterations
thetas, costs, states, metrics = [], [], [], []
for i in range((2*self.dims)):
# everyone is supplied with the same range of theta in one iteration
# define z
if i < self.dims:
z = 1
else:
z = 1j
# alter and normalise phi
curr_phi = best_phi + z*a * \
(max_metric - global_metric)*self.basis[i % self.dims]
curr_phi = curr_phi / np.linalg.norm(curr_phi)
# iterate (angles would be same until theta is changed)
if algo == 'alternate':
res = self.__get_alternate_cost(
angles, curr_phi, backend, shots)
else:
res = self.__get_standard_cost(
angles, curr_phi, backend, shots)
curr_cost = res['cost']
curr_theta = res['theta']
# new
curr_metric = self.__get_metric_val(curr_cost, curr_theta)
# append these parameters
if curr_metric > global_metric: # then only add this cost in the cost and states list
thetas.append(float(curr_theta))
costs.append(float(curr_cost))
states.append(curr_phi)
# new
metrics.append(curr_metric)
found = True
# now each iteration would see the same state as the best phi
# is updated once at the end of the iteration
# also, the cost is also updated only once at the end of the iteration
if progress:
sys.stdout.write('\r')
sys.stdout.write("%f %%completed" %
(100*(i+1)/(2*self.dims)))
sys.stdout.flush()
# 1 iteration completes
if found == False:
# phi was not updated , change a
a = a/2
if progress:
print("\nNo change, updating a...")
else:
# if found is actually true, then only update
# new
# we need to update the best phi , theta max and cost on
# the basis of cost / theta_max value
index = np.argmax(metrics)
# update the parameters of the model
cost = costs[index]
theta_max = thetas[index]
best_phi = states[index]
global_metric = metrics[index]
angle_range /= 2 # updated phi and thus theta too -> refine theta range
# update the iterations
iters += 1
if progress:
print("Best Phi is :", best_phi)
print("Theta estimate :", theta_max)
print("Current cost :", cost)
print("Current Metric :", global_metric)
if iters >= self.iterations:
print(
"Maximum iterations reached for the estimation.\nTerminating algorithm...")
break
# add cost, eigenvector and theta to the dict
results['cost'] = cost
results['theta'] = theta_max
results['state'] = best_phi
results['metric'] = global_metric
return results
unit = unitary_group.rvs(8)
unit
# get the eigenvalues and eigenstates
eig_v, eig_vect = np.linalg.eig(unit)
eig_v = np.angle(eig_v)
eig = []
for k in eig_v:
if k < 0:
e = (k+2*np.pi)/(2*np.pi)
else:
e = (k)/(2*np.pi)
eig.append(e)
eig_v = np.array(eig)
# print("Eigenvalues :", eig_v)
# print("Eigenstates :", eig_vect)
def generate_plot1(actual_E, returned_E, experiments,function):
colors = ['blue', 'orange', 'red',
'green', 'brown', 'magenta', 'pink']
plt.figure(figsize=(9, 7))
plt.scatter(range(experiments), returned_E, marker='o', edgecolors='grey', color=np.random.choice(colors),
alpha=0.8,)
for i, k in enumerate(actual_E):
if i == 0:
plt.plot([0, experiments], [k, k], color='black',
linewidth=2, label='Actual Values')
else:
plt.plot([0, experiments], [k, k], color='black',
linewidth=2)
plt.xlabel("Experiment Number", fontsize=14)
plt.ylabel("Eigenvalues", fontsize=14)
plt.title("Scatter plot for returned eigenvalues with cost function "+str(function), fontsize=17)
plt.legend()
plt.grid()
spea1 = global_max_SPEA(unit, resolution=30, max_metric_val=0.92,cost_function='cos', max_iters=15)
simulator = Aer.get_backend('qasm_simulator')
eigen_vals_ret1 = []
while len(eigen_vals_ret1) != 40:
res = spea1.get_eigen_pair(
backend=simulator, algo='alternate')
if res['metric'] < 0.8:
continue
print("Result :", res)
eigen_vals_ret1.append(res['theta'])
generate_plot1(eig_v, eigen_vals_ret1, 40, "cos")
spea1 = global_max_SPEA(unit, resolution=30, max_metric_val=0.92,cost_function='cos2', max_iters=15)
simulator = Aer.get_backend('qasm_simulator')
eigen_vals_ret1 = []
while len(eigen_vals_ret1) != 40:
res = spea1.get_eigen_pair(
backend=simulator, algo='alternate')
if res['metric'] < 0.8:
continue
# print("Result :", res)
eigen_vals_ret1.append(res['theta'])
generate_plot1(eig_v, eigen_vals_ret1, 40, "cos2")
spea1 = global_max_SPEA(unit, resolution=30, max_metric_val=0.92,cost_function='exp', max_iters=15)
simulator = Aer.get_backend('qasm_simulator')
eigen_vals_ret1 = []
while len(eigen_vals_ret1) != 40:
res = spea1.get_eigen_pair(
backend=simulator, algo='alternate')
if res['metric'] < 0.8:
continue
# print("Result :", res)
eigen_vals_ret1.append(res['theta'])
generate_plot1(eig_v, eigen_vals_ret1, 40, "exp")
spea1 = global_max_SPEA(unit, resolution=30, max_metric_val=0.92,cost_function='exp2', max_iters=15)
simulator = Aer.get_backend('qasm_simulator')
eigen_vals_ret1 = []
while len(eigen_vals_ret1) != 40:
res = spea1.get_eigen_pair(
backend=simulator, algo='alternate')
if res['metric'] < 0.8:
continue
print("Result :", res)
eigen_vals_ret1.append(res['theta'])
generate_plot1(eig_v, eigen_vals_ret1, 40, "exp2")
spea1 = global_max_SPEA(unit, resolution=30, max_metric_val=4,cost_function='inv', max_iters=15)
simulator = Aer.get_backend('qasm_simulator')
eigen_vals_ret1 = []
while len(eigen_vals_ret1) != 40:
res = spea1.get_eigen_pair(
backend=simulator, algo='alternate')
if res['metric'] < 3:
continue
print("Result :", res)
eigen_vals_ret1.append(res['theta'])
generate_plot1(eig_v, eigen_vals_ret1, 40, "inv")
spea1 = global_max_SPEA(unit, resolution=30, max_metric_val=20,cost_function='inv2', max_iters=15)
simulator = Aer.get_backend('qasm_simulator')
eigen_vals_ret1 = []
while len(eigen_vals_ret1) != 40:
res = spea1.get_eigen_pair(
backend=simulator, algo='alternate')
if res['metric'] < 15:
continue
print("Result :", res)
eigen_vals_ret1.append(res['theta'])
generate_plot1(eig_v, eigen_vals_ret1, 40, "inv2")
|
https://github.com/qiskit-community/qiskit-translations-staging
|
qiskit-community
|
from qiskit import QuantumCircuit
top = QuantumCircuit(1)
top.x(0);
bottom = QuantumCircuit(2)
bottom.cry(0.2, 0, 1);
tensored = bottom.tensor(top)
tensored.draw('mpl')
|
https://github.com/jenglick/scipy22-qiskit-runtime-tutorial
|
jenglick
|
# import functionality from qiskit for building and running circuits
from qiskit import QuantumCircuit, BasicAer, transpile
import numpy as np
# define a quantum circuit with a single "X" gate
circuit = QuantumCircuit(1)
circuit.x(0)
circuit.measure_all()
circuit.draw()
# select a quantum backend and run the circuit
backend = BasicAer.get_backend('qasm_simulator')
shots = 2**13
job = backend.run(circuit, shots=shots)
result = job.result()
# get the measurement counts from the circuit
counts = result.get_counts()
print(counts)
print('probability to measure state 0: {}%'.format(counts.get('0', 0) / shots * 100))
print('probability to measure state 1: {}%'.format(counts.get('1', 0) / shots * 100))
# define a quantum circuit with a single Hadamard gate
circuit = QuantumCircuit(1)
circuit.h(0)
circuit.measure_all()
circuit.draw()
job = backend.run(transpile(circuit, backend), shots=shots)
result = job.result()
counts = result.get_counts()
print(counts)
print('probability to measure state 0: {}%'.format(counts.get('0', 0) / shots * 100))
print('probability to measure state 1: {}%'.format(counts.get('1', 0) / shots * 100))
# define the state |1>
a = 0
b = 1
state = np.array([a,b])
norm = np.linalg.norm(state)
normalized_state_ONE = state/norm
print(normalized_state_ONE)
# then, initialize this state as a quantum circuit and measure in computational (Z) basis
circuit = QuantumCircuit(1)
circuit.initialize(normalized_state_ONE)
circuit.measure_all()
print(circuit)
# run the circuit and then subtract the two probabilities p(0)-p(1)
job = backend.run(transpile(circuit, backend), shots=shots)
result = job.result()
counts = result.get_counts()
print(counts)
expval = (counts.get('0',0)-counts.get('1',0)) / shots
print('expectation value: {}'.format(expval))
# define the state |+>
a = 0.70710678 # 1/sqrt(2)
b = 0.70710678
state = np.array([a,b])
norm = np.linalg.norm(state)
normalized_state_PLUS = state/norm
print(normalized_state_PLUS)
# initialize as a quantum circuit and measure
circuit = QuantumCircuit(1)
circuit.initialize(normalized_state_PLUS)
circuit.measure_all()
print(circuit)
# run the circuit and then subtract the two probabilities p(0)-p(1)
job = backend.run(transpile(circuit, backend), shots=shots)
result = job.result()
counts = result.get_counts()
print(counts)
expval = (counts.get('0',0)-counts.get('1',0)) / shots
print('expectation value: {}'.format(expval))
# define the Pauli Z operator
z = np.array([[1, 0],
[0, -1]])
# what are the eigenvectors of this operator?
from qiskit.visualization import plot_state_city, plot_bloch_multivector
z_eigenvals, z_eigenvecs = np.linalg.eigh(z)
print('Eigensystem of Z:')
print(z_eigenvals)
print(z_eigenvecs)
plot_bloch_multivector(z_eigenvecs[:,0])
import qiskit.tools.jupyter
%qiskit_version_table
%qiskit_copyright
|
https://github.com/arnavdas88/QuGlassyIsing
|
arnavdas88
|
!pip install qiskit
J = 4.0
B_x = 0.0
B_z = 0.0
import numpy as np
from qiskit.providers.aer import AerSimulator, QasmSimulator
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit.library import TwoLocal
from qiskit.aqua.operators import *
from qiskit.aqua import set_qiskit_aqua_logging, QuantumInstance
from qiskit.aqua.algorithms import NumPyMinimumEigensolver, VQE, NumPyEigensolver
from qiskit.circuit import QuantumCircuit, ParameterVector
from qiskit.visualization import plot_histogram
Hamiltonian = J * 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- B_z * (( Z ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ Z ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ Z ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ Z ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ Z ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ Z ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ Z ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ Z ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ Z ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ Z ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ Z ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ Z ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ Z ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ Z ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ Z ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ Z ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ Z ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ Z ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ Z ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ Z ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ Z ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ Z ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ Z ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ Z ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ Z ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ Z ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ Z ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ Z ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ Z ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ Z ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ Z ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ Z ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ Z ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ Z ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ Z ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ Z )
) - B_x * ( ( X ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ X ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ X ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ X ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ X ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ X ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ X ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ X ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ X ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ X ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ X ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ X ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ X ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ X ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ X ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ X ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ X ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ X ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ X ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ X ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ X ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ X ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ X ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ X ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ X ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ X ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ X ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ X ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ X ^ I ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ X ^ I ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ X ^ I ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ X ^ I ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ X ^ I ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ X ^ I ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ X ^ I ) + ( I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ I ^ X ))
ansatz = TwoLocal(num_qubits=36, rotation_blocks=['ry', 'rz'], entanglement_blocks=None, entanglement='full', reps=1, skip_unentangled_qubits=False, skip_final_rotation_layer=True)
print(ansatz)
backend = AerSimulator(method='matrix_product_state')
quantum_instance = QuantumInstance(backend,
shots = 8192,
initial_layout = None,
optimization_level = 3)
optimizer = COBYLA(maxiter=10000, tol=0.000000001)
vqe = VQE(Hamiltonian, ansatz, optimizer, include_custom = False)
print('We are using:', quantum_instance.backend)
vqe_result = vqe.run(quantum_instance)
print(vqe['result'])
plot_histogram(vqe_result['eigenstate'])
import pickle
filename = "2D_Ising_Model_CountsAF1.pkl"
a = {'vqe_result': vqe_result}
#This saves the data
with open(filename, 'wb') as handle:
pickle.dump(a, handle, protocol=pickle.HIGHEST_PROTOCOL)
# This loads the data
with open(filename, 'rb') as handle:
b = pickle.load(handle)
|
https://github.com/urwin419/QiskitChecker
|
urwin419
|
from qiskit import QuantumCircuit
def create_bell_pair():
qc = QuantumCircuit(2)
qc.h(1)
### removed cx gate ###
return qc
def encode_message(qc, qubit, msg):
if len(msg) != 2 or not set([0,1]).issubset({0,1}):
raise ValueError(f"message '{msg}' is invalid")
if msg[1] == "1":
qc.x(qubit)
if msg[0] == "1":
qc.z(qubit)
return qc
def decode_message(qc):
qc.cx(1, 0)
### removed h gate ###
return qc
|
https://github.com/swe-bench/Qiskit__qiskit
|
swe-bench
|
# This code is part of Qiskit.
#
# (C) Copyright IBM 2017, 2020.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""Piecewise polynomial Chebyshev approximation to a given f(x)."""
from __future__ import annotations
from typing import Callable
import numpy as np
from numpy.polynomial.chebyshev import Chebyshev
from qiskit.circuit import QuantumRegister, AncillaRegister
from qiskit.circuit.library.blueprintcircuit import BlueprintCircuit
from qiskit.circuit.exceptions import CircuitError
from .piecewise_polynomial_pauli_rotations import PiecewisePolynomialPauliRotations
class PiecewiseChebyshev(BlueprintCircuit):
r"""Piecewise Chebyshev approximation to an input function.
For a given function :math:`f(x)` and degree :math:`d`, this class implements a piecewise
polynomial Chebyshev approximation on :math:`n` qubits to :math:`f(x)` on the given intervals.
All the polynomials in the approximation are of degree :math:`d`.
The values of the parameters are calculated according to [1] and see [2] for a more
detailed explanation of the circuit construction and how it acts on the qubits.
Examples:
.. plot::
:include-source:
import numpy as np
from qiskit import QuantumCircuit
from qiskit.circuit.library.arithmetic.piecewise_chebyshev import PiecewiseChebyshev
f_x, degree, breakpoints, num_state_qubits = lambda x: np.arcsin(1 / x), 2, [2, 4], 2
pw_approximation = PiecewiseChebyshev(f_x, degree, breakpoints, num_state_qubits)
pw_approximation._build()
qc = QuantumCircuit(pw_approximation.num_qubits)
qc.h(list(range(num_state_qubits)))
qc.append(pw_approximation.to_instruction(), qc.qubits)
qc.draw(output='mpl')
References:
[1]: Haener, T., Roetteler, M., & Svore, K. M. (2018).
Optimizing Quantum Circuits for Arithmetic.
`arXiv:1805.12445 <http://arxiv.org/abs/1805.12445>`_
[2]: Carrera Vazquez, A., Hiptmair, H., & Woerner, S. (2022).
Enhancing the Quantum Linear Systems Algorithm Using Richardson Extrapolation.
`ACM Transactions on Quantum Computing 3, 1, Article 2 <https://doi.org/10.1145/3490631>`_
"""
def __init__(
self,
f_x: float | Callable[[int], float],
degree: int | None = None,
breakpoints: list[int] | None = None,
num_state_qubits: int | None = None,
name: str = "pw_cheb",
) -> None:
r"""
Args:
f_x: the function to be approximated. Constant functions should be specified
as f_x = constant.
degree: the degree of the polynomials.
Defaults to ``1``.
breakpoints: the breakpoints to define the piecewise-linear function.
Defaults to the full interval.
num_state_qubits: number of qubits representing the state.
name: The name of the circuit object.
"""
super().__init__(name=name)
# define internal parameters
self._num_state_qubits = None
# Store parameters
self._f_x = f_x
self._degree = degree if degree is not None else 1
self._breakpoints = breakpoints if breakpoints is not None else [0]
self._polynomials: list[list[float]] | None = None
self.num_state_qubits = num_state_qubits
def _check_configuration(self, raise_on_failure: bool = True) -> bool:
"""Check if the current configuration is valid."""
valid = True
if self._f_x is None:
valid = False
if raise_on_failure:
raise AttributeError("The function to be approximated has not been set.")
if self._degree is None:
valid = False
if raise_on_failure:
raise AttributeError("The degree of the polynomials has not been set.")
if self._breakpoints is None:
valid = False
if raise_on_failure:
raise AttributeError("The breakpoints have not been set.")
if self.num_state_qubits is None:
valid = False
if raise_on_failure:
raise AttributeError("The number of qubits has not been set.")
if self.num_qubits < self.num_state_qubits + 1:
valid = False
if raise_on_failure:
raise CircuitError(
"Not enough qubits in the circuit, need at least "
"{}.".format(self.num_state_qubits + 1)
)
return valid
@property
def f_x(self) -> float | Callable[[int], float]:
"""The function to be approximated.
Returns:
The function to be approximated.
"""
return self._f_x
@f_x.setter
def f_x(self, f_x: float | Callable[[int], float] | None) -> None:
"""Set the function to be approximated.
Note that this may change the underlying quantum register, if the number of state qubits
changes.
Args:
f_x: The new function to be approximated.
"""
if self._f_x is None or f_x != self._f_x:
self._invalidate()
self._f_x = f_x
self._reset_registers(self.num_state_qubits)
@property
def degree(self) -> int:
"""The degree of the polynomials.
Returns:
The degree of the polynomials.
"""
return self._degree
@degree.setter
def degree(self, degree: int | None) -> None:
"""Set the error tolerance.
Note that this may change the underlying quantum register, if the number of state qubits
changes.
Args:
degree: The new degree.
"""
if self._degree is None or degree != self._degree:
self._invalidate()
self._degree = degree
self._reset_registers(self.num_state_qubits)
@property
def breakpoints(self) -> list[int]:
"""The breakpoints for the piecewise approximation.
Returns:
The breakpoints for the piecewise approximation.
"""
breakpoints = self._breakpoints
# it the state qubits are set ensure that the breakpoints match beginning and end
if self.num_state_qubits is not None:
num_states = 2**self.num_state_qubits
# If the last breakpoint is < num_states, add the identity polynomial
if breakpoints[-1] < num_states:
breakpoints = breakpoints + [num_states]
# If the first breakpoint is > 0, add the identity polynomial
if breakpoints[0] > 0:
breakpoints = [0] + breakpoints
return breakpoints
@breakpoints.setter
def breakpoints(self, breakpoints: list[int] | None) -> None:
"""Set the breakpoints for the piecewise approximation.
Note that this may change the underlying quantum register, if the number of state qubits
changes.
Args:
breakpoints: The new breakpoints for the piecewise approximation.
"""
if self._breakpoints is None or breakpoints != self._breakpoints:
self._invalidate()
self._breakpoints = breakpoints if breakpoints is not None else [0]
self._reset_registers(self.num_state_qubits)
@property
def polynomials(self) -> list[list[float]]:
"""The polynomials for the piecewise approximation.
Returns:
The polynomials for the piecewise approximation.
Raises:
TypeError: If the input function is not in the correct format.
"""
if self.num_state_qubits is None:
return [[]]
# note this must be the private attribute since we handle missing breakpoints at
# 0 and 2 ^ num_qubits here (e.g. if the function we approximate is not defined at 0
# and the user takes that into account we just add an identity)
breakpoints = self._breakpoints
# Need to take into account the case in which no breakpoints were provided in first place
if breakpoints == [0]:
breakpoints = [0, 2**self.num_state_qubits]
num_intervals = len(breakpoints)
# Calculate the polynomials
polynomials = []
for i in range(0, num_intervals - 1):
# Calculate the polynomial approximating the function on the current interval
try:
# If the function is constant don't call Chebyshev (not necessary and gives errors)
if isinstance(self.f_x, (float, int)):
# Append directly to list of polynomials
polynomials.append([self.f_x])
else:
poly = Chebyshev.interpolate(
self.f_x, self.degree, domain=[breakpoints[i], breakpoints[i + 1]]
)
# Convert polynomial to the standard basis and rescale it for the rotation gates
poly = 2 * poly.convert(kind=np.polynomial.Polynomial).coef
# Convert to list and append
polynomials.append(poly.tolist())
except ValueError as err:
raise TypeError(
" <lambda>() missing 1 required positional argument: '"
+ self.f_x.__code__.co_varnames[0]
+ "'."
+ " Constant functions should be specified as 'f_x = constant'."
) from err
# If the last breakpoint is < 2 ** num_qubits, add the identity polynomial
if breakpoints[-1] < 2**self.num_state_qubits:
polynomials = polynomials + [[2 * np.arcsin(1)]]
# If the first breakpoint is > 0, add the identity polynomial
if breakpoints[0] > 0:
polynomials = [[2 * np.arcsin(1)]] + polynomials
return polynomials
@polynomials.setter
def polynomials(self, polynomials: list[list[float]] | None) -> None:
"""Set the polynomials for the piecewise approximation.
Note that this may change the underlying quantum register, if the number of state qubits
changes.
Args:
polynomials: The new breakpoints for the piecewise approximation.
"""
if self._polynomials is None or polynomials != self._polynomials:
self._invalidate()
self._polynomials = polynomials
self._reset_registers(self.num_state_qubits)
@property
def num_state_qubits(self) -> int:
r"""The number of state qubits representing the state :math:`|x\rangle`.
Returns:
The number of state qubits.
"""
return self._num_state_qubits
@num_state_qubits.setter
def num_state_qubits(self, num_state_qubits: int | None) -> None:
"""Set the number of state qubits.
Note that this may change the underlying quantum register, if the number of state qubits
changes.
Args:
num_state_qubits: The new number of qubits.
"""
if self._num_state_qubits is None or num_state_qubits != self._num_state_qubits:
self._invalidate()
self._num_state_qubits = num_state_qubits
# Set breakpoints if they haven't been set
if num_state_qubits is not None and self._breakpoints is None:
self.breakpoints = [0, 2**num_state_qubits]
self._reset_registers(num_state_qubits)
def _reset_registers(self, num_state_qubits: int | None) -> None:
"""Reset the registers."""
self.qregs = []
if num_state_qubits is not None:
qr_state = QuantumRegister(num_state_qubits, "state")
qr_target = QuantumRegister(1, "target")
self.qregs = [qr_state, qr_target]
num_ancillas = num_state_qubits
if num_ancillas > 0:
qr_ancilla = AncillaRegister(num_ancillas)
self.add_register(qr_ancilla)
def _build(self):
"""Build the circuit if not already build. The operation is considered successful
when q_objective is :math:`|1>`"""
if self._is_built:
return
super()._build()
poly_r = PiecewisePolynomialPauliRotations(
self.num_state_qubits, self.breakpoints, self.polynomials, name=self.name
)
# qr_state = self.qubits[: self.num_state_qubits]
# qr_target = [self.qubits[self.num_state_qubits]]
# qr_ancillas = self.qubits[self.num_state_qubits + 1 :]
# Apply polynomial approximation
self.append(poly_r.to_gate(), self.qubits)
|
https://github.com/jaykomarraju/Quantum-Optimization-for-Solar-Farm
|
jaykomarraju
|
import numpy as np
import networkx as nx
from qiskit import Aer
from qiskit.algorithms import QAOA, NumPyMinimumEigensolver
from qiskit.algorithms.optimizers import COBYLA
from qiskit.utils import QuantumInstance
from qiskit_optimization import QuadraticProgram
from qiskit_optimization.algorithms import MinimumEigenOptimizer
num_time_slots = 24
# Define the QUBO problem
qubo = QuadraticProgram()
# Add binary variables for charging (c) and discharging (d) states
for t in range(num_time_slots):
qubo.binary_var(f'c_{t}')
qubo.binary_var(f'd_{t}')
# Define the objective function
# (In practice, you need to calculate Jij and hi based on the solar farm data)
Jij = np.random.rand(num_time_slots, num_time_slots) * 0.5
hi_c = -1 + np.random.rand(num_time_slots)
hi_d = 1 - np.random.rand(num_time_slots)
# Set linear and quadratic terms of the objective function
linear_terms = {}
quadratic_terms = {}
for t in range(num_time_slots):
linear_terms[f'c_{t}'] = hi_c[t]
linear_terms[f'd_{t}'] = hi_d[t]
for s in range(num_time_slots):
if t != s:
quadratic_terms[(f'c_{t}', f'c_{s}')] = Jij[t, s]
quadratic_terms[(f'd_{t}', f'd_{s}')] = Jij[t, s]
qubo.minimize(linear=linear_terms, quadratic=quadratic_terms)
# Set up the quantum instance
backend = Aer.get_backend('qasm_simulator')
quantum_instance = QuantumInstance(backend, seed_simulator=42, seed_transpiler=42, shots=10000)
# Set up the QAOA algorithm and optimizer
optimizer = COBYLA(maxiter=500)
qaoa = QAOA(optimizer=optimizer, reps=5, quantum_instance=quantum_instance)
# Set up the minimum eigen optimizer
min_eig_optimizer = MinimumEigenOptimizer(qaoa)
# Solve the problem
result = min_eig_optimizer.solve(qubo)
print("QAOA result:", result)
# Solve the problem using a classical solver (NumPyMinimumEigensolver)
exact_solver = MinimumEigenOptimizer(NumPyMinimumEigensolver())
exact_result = exact_solver.solve(qubo)
print("Classical result:", exact_result)
|
https://github.com/shell-raiser/Qiskit-Developer-Certification-Notes-and-Code
|
shell-raiser
|
from qiskit import *
%matplotlib inline
from qiskit.tools.visualization import plot_histogram
secretNumber = '101001'
circuit = QuantumCircuit(6+1, 6)
circuit.h((0,1,2,3,4,5))
circuit.x(6)
circuit.h(6)
circuit.barrier()
circuit.cx(5, 6)
circuit.cx(3, 6)
circuit.cx(0, 6)
circuit.barrier()
circuit.h((0,1,2,3,4,5))
circuit.draw()
circuit.barrier()
circuit.measure([0,1,2,3,4,5], [0,1,2,3,4,5])
circuit.draw()
simulator = Aer.get_backend('qasm_simulator')
result = execute(circuit, backend=simulator, shots = 1).result()
counts = result.get_counts()
print(counts)
circuit = QuantumCircuit(len(secretNumber)+1, len(secretNumber))
#circuit.h((0,1,2,3,4,5))
circuit.h(range(len(secretNumber)))
#circuit.x(6)
circuit.x(len(secretNumber))
#circuit.h(6)
circuit.x(len(secretNumber))
circuit.barrier()
for ii, yesno in enumerate(reversed(secretNumber)):
if yesno == '1':
circuit.cx (ii, len(secretNumber))
#circuit.cx(5, 6)
#circuit.cx(3, 6)
#circuit.cx(0, 6)
circuit.barrier()
#circuit.h((0,1,2,3,4,5))
circuit.h(range(len(secretNumber)))
circuit.barrier()
circuit.measure(range(len(secretNumber)), range(len(secretNumber)))
circuit.draw()
|
https://github.com/jayeshparashar/Bloch-sphere-
|
jayeshparashar
|
#Let's start with importing packages
from qiskit import QuantumRegister, ClassicalRegister,QuantumCircuit, Aer, assemble, execute
from qiskit.visualization import plot_histogram, plot_bloch_vector, plot_bloch_multivector
import numpy as np
# import random_statevector to get a random two state quantum system
from qiskit.quantum_info import random_statevector, Statevector
# Use random_statevector method to get a valid random statevector representing a qubit in superposition of two states
rand_sv = random_statevector(2).data
print(rand_sv)
plot_bloch_multivector(rand_sv)
# Define a function to calculate and return the expectation values of X, Y and Z coordinates of a qubit vector situated
# on a blochsphere, since qiskit measurement operation is set to measure in default 'Z' axis, in order to measure qubit
# in 'X' and 'Y' direction/axis we have to rotate/adjust the final vector as it is measured in X and Y axis while performing
# a default Z measurement.
def Exp_Value(axis):
shots=10000
qc = QuantumCircuit(1,1)
qc.initialize(rand_sv,0)
if (axis == 'Y') :
qc.sdg(0)
if (axis == 'X' or axis == 'Y') :
qc.h(0)
qc.measure(0,0)
bkend = Aer.get_backend('qasm_simulator')
cnts = execute(qc,bkend,shots=shots).result().get_counts()
exp_val = (cnts['0'] * 1 + cnts['1']* -1 )/shots
print('counts and exp. value for qubit system for', axis,' measurement ', cnts['0'], cnts['1'], exp_val)
return exp_val
# The expectation values of X, Y and Z on same initial state is an indicator of average energy which is calculated with
# the help of eigenvalues (discrete energy states) obtained during the measurement in same repeated experiment.
ev_X = Exp_Value('X')
ev_Y = Exp_Value('Y')
ev_Z = Exp_Value('Z')
print(ev_X, ev_Y, ev_Z)
# let's plot the vector on blochsphere with help of cartesian coordinates and see if we get same representation as
# rand_sv
plot_bloch_vector([ev_X, ev_Y, ev_Z])
# lets calculate the spherical coordinates theta and phi as radius is 1
# since x = sin(θ)cos(φ), y = sin(θ)sin(φ) and z = cos(θ)
theta = np.arccos(ev_Z)
phi = np.arcsin(ev_Y/np.sin(theta))
print('theta, phi ', theta, phi)
plot_bloch_vector([1, theta, phi], coord_type='spherical')
#lets calculate the vector associated with this value of theta and phi using standard qubit's representation
# on blochsphere
amp_zero = np.cos(theta/2)
amp_one = np.cos(phi)*np.sin(theta/2) + (np.sin(phi)*np.sin(theta/2)) * complex(0, 1)
q_sv = [amp_zero, amp_one]
print(q_sv)
plot_bloch_multivector(q_sv)
# lets measure the state_fidelity to check if both the states (rand_sv and q_sv) are in-fact same
from qiskit.quantum_info import state_fidelity
print(rand_sv, q_sv)
state_fidelity(rand_sv, q_sv)
# End of program #
|
https://github.com/qiskit-community/qiskit-translations-staging
|
qiskit-community
|
import numpy as np
from qiskit_nature.second_q.hamiltonians import QuadraticHamiltonian
# create Hamiltonian
hermitian_part = np.array(
[
[1.0, 2.0, 0.0, 0.0],
[2.0, 1.0, 2.0, 0.0],
[0.0, 2.0, 1.0, 2.0],
[0.0, 0.0, 2.0, 1.0],
]
)
antisymmetric_part = np.array(
[
[0.0, 3.0, 0.0, 0.0],
[-3.0, 0.0, 3.0, 0.0],
[0.0, -3.0, 0.0, 3.0],
[0.0, 0.0, -3.0, 0.0],
]
)
constant = 4.0
hamiltonian = QuadraticHamiltonian(
hermitian_part=hermitian_part,
antisymmetric_part=antisymmetric_part,
constant=constant,
)
# convert it to a FermionicOp and print it
hamiltonian_ferm = hamiltonian.second_q_op()
print(hamiltonian_ferm)
# get the transformation matrix W and orbital energies {epsilon_j}
(
transformation_matrix,
orbital_energies,
transformed_constant,
) = hamiltonian.diagonalizing_bogoliubov_transform()
print(f"Shape of matrix W: {transformation_matrix.shape}")
print(f"Orbital energies: {orbital_energies}")
print(f"Transformed constant: {transformed_constant}")
from qiskit_nature.second_q.circuit.library import FermionicGaussianState
occupied_orbitals = (0, 2)
eig = np.sum(orbital_energies[list(occupied_orbitals)]) + transformed_constant
print(f"Eigenvalue: {eig}")
circuit = FermionicGaussianState(transformation_matrix, occupied_orbitals=occupied_orbitals)
circuit.draw("mpl")
from qiskit.quantum_info import Statevector
from qiskit_nature.second_q.mappers import JordanWignerMapper
# simulate the circuit to get the final state
state = np.array(Statevector(circuit))
# convert the Hamiltonian to a matrix
hamiltonian_jw = JordanWignerMapper().map(hamiltonian_ferm).to_matrix()
# check that the state is an eigenvector with the expected eigenvalue
np.testing.assert_allclose(hamiltonian_jw @ state, eig * state, atol=1e-8)
# create Hamiltonian
hermitian_part = np.array(
[
[1.0, 2.0, 0.0, 0.0],
[2.0, 1.0, 2.0, 0.0],
[0.0, 2.0, 1.0, 2.0],
[0.0, 0.0, 2.0, 1.0],
]
)
constant = 4.0
hamiltonian = QuadraticHamiltonian(
hermitian_part=hermitian_part,
constant=constant,
)
print(f"Hamiltonian conserves particle number: {hamiltonian.conserves_particle_number()}")
# get the transformation matrix W and orbital energies {epsilon_j}
(
transformation_matrix,
orbital_energies,
transformed_constant,
) = hamiltonian.diagonalizing_bogoliubov_transform()
print(f"Shape of matrix W: {transformation_matrix.shape}")
print(f"Orbital energies: {orbital_energies}")
print(f"Transformed constant: {transformed_constant}")
from qiskit_nature.second_q.circuit.library import SlaterDeterminant
occupied_orbitals = (0, 2)
eig = np.sum(orbital_energies[list(occupied_orbitals)]) + transformed_constant
print(f"Eigenvalue: {eig}")
circuit = SlaterDeterminant(transformation_matrix[list(occupied_orbitals)])
circuit.draw("mpl")
from qiskit_nature.second_q.circuit.library import BogoliubovTransform
from qiskit import QuantumCircuit, QuantumRegister
from qiskit.quantum_info import random_hermitian, random_statevector, state_fidelity
from scipy.linalg import expm
# create Hamiltonian
n_modes = 5
hermitian_part = np.array(random_hermitian(n_modes))
hamiltonian = QuadraticHamiltonian(hermitian_part=hermitian_part)
# diagonalize Hamiltonian
(
transformation_matrix,
orbital_energies,
_,
) = hamiltonian.diagonalizing_bogoliubov_transform()
# set simulation time and construct time evolution circuit
time = 1.0
register = QuantumRegister(n_modes)
circuit = QuantumCircuit(register)
bog_circuit = BogoliubovTransform(transformation_matrix)
# change to the diagonal basis of the Hamiltonian
circuit.append(bog_circuit.inverse(), register)
# perform time evolution by applying z rotations
for q, energy in zip(register, orbital_energies):
circuit.rz(-energy * time, q)
# change back to the original basis
circuit.append(bog_circuit, register)
# simulate the circuit
initial_state = random_statevector(2**n_modes)
final_state = initial_state.evolve(circuit)
# compute the correct state by direct exponentiation
hamiltonian_jw = JordanWignerMapper().map(hamiltonian.second_q_op()).to_matrix()
exact_evolution_op = expm(-1j * time * hamiltonian_jw)
expected_state = exact_evolution_op @ np.array(initial_state)
# check that the simulated state is correct
fidelity = state_fidelity(final_state, expected_state)
np.testing.assert_allclose(fidelity, 1.0, atol=1e-8)
import qiskit.tools.jupyter
%qiskit_version_table
%qiskit_copyright
|
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(feature_range=(0, np.pi)).fit_transform(features)
train_features, test_features, train_labels, test_labels = train_test_split(
features, labels, train_size=15, shuffle=False
)
# number of qubits is equal to the number of features
num_qubits = 2
# number of steps performed during the training procedure
tau = 100
# regularization parameter
C = 1000
from qiskit import BasicAer
from qiskit.circuit.library import ZFeatureMap
from qiskit.utils import algorithm_globals
from qiskit_machine_learning.kernels import FidelityQuantumKernel
algorithm_globals.random_seed = 12345
feature_map = ZFeatureMap(feature_dimension=num_qubits, reps=1)
qkernel = FidelityQuantumKernel(feature_map=feature_map)
from qiskit_machine_learning.algorithms import PegasosQSVC
pegasos_qsvc = PegasosQSVC(quantum_kernel=qkernel, C=C, num_steps=tau)
# training
pegasos_qsvc.fit(train_features, train_labels)
# testing
pegasos_score = pegasos_qsvc.score(test_features, test_labels)
print(f"PegasosQSVC classification test score: {pegasos_score}")
grid_step = 0.2
margin = 0.2
grid_x, grid_y = np.meshgrid(
np.arange(-margin, np.pi + margin, grid_step), np.arange(-margin, np.pi + margin, grid_step)
)
meshgrid_features = np.column_stack((grid_x.ravel(), grid_y.ravel()))
meshgrid_colors = pegasos_qsvc.predict(meshgrid_features)
import matplotlib.pyplot as plt
plt.figure(figsize=(5, 5))
meshgrid_colors = meshgrid_colors.reshape(grid_x.shape)
plt.pcolormesh(grid_x, grid_y, meshgrid_colors, cmap="RdBu", shading="auto")
plt.scatter(
train_features[:, 0][train_labels == 0],
train_features[:, 1][train_labels == 0],
marker="s",
facecolors="w",
edgecolors="r",
label="A train",
)
plt.scatter(
train_features[:, 0][train_labels == 1],
train_features[:, 1][train_labels == 1],
marker="o",
facecolors="w",
edgecolors="b",
label="B train",
)
plt.scatter(
test_features[:, 0][test_labels == 0],
test_features[:, 1][test_labels == 0],
marker="s",
facecolors="r",
edgecolors="r",
label="A test",
)
plt.scatter(
test_features[:, 0][test_labels == 1],
test_features[:, 1][test_labels == 1],
marker="o",
facecolors="b",
edgecolors="b",
label="B test",
)
plt.legend(bbox_to_anchor=(1.05, 1), loc="upper left", borderaxespad=0.0)
plt.title("Pegasos Classification")
plt.show()
import qiskit.tools.jupyter
%qiskit_version_table
%qiskit_copyright
|
https://github.com/RedHatParichay/qiskit-lancasterleipzig-2023
|
RedHatParichay
|
## Blank Code Cell
## Use only if you need to install the grader and/or Qiskit
## If you are running this notebook in the IBM Quantum Lab - you can ignore this cell
!pip install qiskit
!pip install 'qc-grader[qiskit] @ git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git'
## Run this cell to make sure your grader is setup correctly
%set_env QC_GRADE_ONLY=true
%set_env QC_GRADING_ENDPOINT=https://qac-grading.quantum-computing.ibm.com
from qiskit import QuantumCircuit
qc = QuantumCircuit(3, 3)
## Write your code below this line ##
## Do not change the code below here ##
answer1 = qc
qc.draw()
# Grader Cell: Run this to submit your answer
from qc_grader.challenges.fall_fest23 import grade_ex2a
grade_ex2a(answer1)
from qiskit import QuantumCircuit
qc = QuantumCircuit(3, 3)
qc.barrier(0, 1, 2)
## Write your code below this line ##
## Do not change the code below this line ##
answer2 = qc
qc.draw()
# Grader Cell: Run this to submit your answer
from qc_grader.challenges.fall_fest23 import grade_ex2b
grade_ex2b(answer2)
answer3: bool
## Quiz: evaluate the results and decide if the following statement is True or False
q0 = 1
q1 = 0
q2 = 1
## Based on this, is it TRUE or FALSE that the Guard on the left is a liar?
## Assign your answer, either True or False, to answer3 below
answer3 =
from qc_grader.challenges.fall_fest23 import grade_ex2c
grade_ex2c(answer3)
## Quiz: Fill in the correct numbers to make the following statement true:
## The treasure is on the right, and the Guard on the left is the liar
q0 =
q1 =
q2 =
## HINT - Remember that Qiskit uses little-endian ordering
answer4 = [q0, q1, q2]
# Grader Cell: Run this to submit your answer
from qc_grader.challenges.fall_fest23 import grade_ex2d
grade_ex2d(answer4)
from qiskit import QuantumCircuit
qc = QuantumCircuit(3)
## in the code below, fill in the missing gates. Run the cell to see a drawing of the current circuit ##
qc.h(0)
qc.barrier(0, 1, 2)
qc.x(2)
qc.cx(2, 0)
qc.barrier(0, 1, 2)
qc.cx(2, 1)
qc.x(0)
## Do not change any of the code below this line ##
answer5 = qc
qc.draw(output="mpl")
# Grader Cell: Run this to submit your answer
from qc_grader.challenges.fall_fest23 import grade_ex2e
grade_ex2e(answer5)
from qiskit import QuantumCircuit, Aer, transpile
from qiskit.visualization import plot_histogram
## This is the full version of the circuit. Run it to see the results ##
quantCirc = QuantumCircuit(3)
quantCirc.h(0), quantCirc.h(2), quantCirc.cx(0, 1), quantCirc.barrier(0, 1, 2), quantCirc.cx(2, 1), quantCirc.x(2), quantCirc.cx(2, 0), quantCirc.x(2)
quantCirc.barrier(0, 1, 2), quantCirc.swap(0, 1), quantCirc.x(1), quantCirc.cx(2, 1), quantCirc.x(0), quantCirc.x(2), quantCirc.cx(2, 0), quantCirc.x(2)
# Execute the circuit and draw the histogram
measured_qc = quantCirc.measure_all(inplace=False)
backend = Aer.get_backend('qasm_simulator') # the device to run on
result = backend.run(transpile(measured_qc, backend), shots=1000).result()
counts = result.get_counts(measured_qc)
plot_histogram(counts)
from qiskit.primitives import Sampler
from qiskit.visualization import plot_distribution
sampler = Sampler()
result = sampler.run(measured_qc, shots=1000).result()
probs = result.quasi_dists[0].binary_probabilities()
plot_distribution(probs)
|
https://github.com/abbarreto/qiskit2
|
abbarreto
|
%run init.ipynb
from qiskit import *
nshots = 8192
IBMQ.load_account()
provider = IBMQ.get_provider(hub = 'ibm-q-research-2', group = 'federal-uni-sant-1', project = 'main')
simulator = Aer.get_backend('qasm_simulator')
device = provider.get_backend('ibm_nairobi')
from qiskit.tools.visualization import plot_histogram
from qiskit.tools.monitor import job_monitor, backend_overview, backend_monitor
qr = QuantumRegister(4); cr = ClassicalRegister(4); qc = QuantumCircuit(qr,cr)
qc.h([0,1])
qc.barrier()
qc.cx(0,2); qc.cx(0,3); qc.cx(1,2); qc.cx(1,3) # oraculo
qc.barrier()
qc.measure([2,3],[2,3])
qc.barrier()
qc.h([0,1]); qc.measure([0,1],[0,1])
qc.draw(output='mpl')
jobS = execute(qc, backend = simulator, shots = nshots)
jobE = execute(qc, backend = device, shots = nshots)
job_monitor(jobE)
plot_histogram([jobS.result().get_counts(0), jobE.result().get_counts(0)],
bar_labels = False, legend = ['sim', 'exp'])
|
https://github.com/vandnaChaturvedi/Qauntum_algorithms_Qiskit
|
vandnaChaturvedi
|
from qiskit import QuantumCircuit, Aer, transpile, assemble
from qiskit.visualization import plot_histogram, plot_bloch_multivector
from numpy.random import randint
import numpy as np
print("Imports Successful")
qc = QuantumCircuit(1,1)
# Alice prepares qubit in state |+>
qc.h(0)
qc.barrier()
# Alice now sends the qubit to Bob
# who measures it in the X-basis
qc.h(0)
qc.measure(0,0)
# Draw and simulate circuit
display(qc.draw())
aer_sim = Aer.get_backend('aer_simulator')
job = aer_sim.run(assemble(qc))
plot_histogram(job.result().get_counts())
qc = QuantumCircuit(1,1)
# Alice prepares qubit in state |+>
qc.h(0)
# Alice now sends the qubit to Bob
# but Eve intercepts and tries to read it
qc.measure(0, 0)
qc.barrier()
# Eve then passes this on to Bob
# who measures it in the X-basis
qc.h(0)
qc.measure(0,0)
# Draw and simulate circuit
display(qc.draw())
aer_sim = Aer.get_backend('aer_simulator')
job = aer_sim.run(assemble(qc))
plot_histogram(job.result().get_counts())
np.random.seed(seed=0)
n = 100
np.random.seed(seed=0)
n = 100
## Step 1
# Alice generates bits
alice_bits = randint(2, size=n)
print(alice_bits)
np.random.seed(seed=0)
n = 100
## Step 1
#Alice generates bits
alice_bits = randint(2, size=n)
## Step 2
# Create an array to tell us which qubits
# are encoded in which bases
alice_bases = randint(2, size=n)
print(alice_bases)
def encode_message(bits, bases):
message = []
for i in range(n):
qc = QuantumCircuit(1,1)
if bases[i] == 0: # Prepare qubit in Z-basis
if bits[i] == 0:
pass
else:
qc.x(0)
else: # Prepare qubit in X-basis
if bits[i] == 0:
qc.h(0)
else:
qc.x(0)
qc.h(0)
qc.barrier()
message.append(qc)
return message
np.random.seed(seed=0)
n = 100
## Step 1
# Alice generates bits
alice_bits = randint(2, size=n)
## Step 2
# Create an array to tell us which qubits
# are encoded in which bases
alice_bases = randint(2, size=n)
message = encode_message(alice_bits, alice_bases)
print('bit = %i' % alice_bits[0])
print('basis = %i' % alice_bases[0])
message[0].draw()
print('bit = %i' % alice_bits[4])
print('basis = %i' % alice_bases[4])
message[4].draw()
np.random.seed(seed=0)
n = 100
## Step 1
# Alice generates bits
alice_bits = randint(2, size=n)
## Step 2
# Create an array to tell us which qubits
# are encoded in which bases
alice_bases = randint(2, size=n)
message = encode_message(alice_bits, alice_bases)
## Step 3
# Decide which basis to measure in:
bob_bases = randint(2, size=n)
print(bob_bases)
def measure_message(message, bases):
backend = Aer.get_backend('aer_simulator')
measurements = []
for q in range(n):
if bases[q] == 0: # measuring in Z-basis
message[q].measure(0,0)
if bases[q] == 1: # measuring in X-basis
message[q].h(0)
message[q].measure(0,0)
aer_sim = Aer.get_backend('aer_simulator')
qobj = assemble(message[q], shots=1, memory=True)
result = aer_sim.run(qobj).result()
measured_bit = int(result.get_memory()[0])
measurements.append(measured_bit)
return measurements
np.random.seed(seed=0)
n = 100
## Step 1
# Alice generates bits
alice_bits = randint(2, size=n)
## Step 2
# Create an array to tell us which qubits
# are encoded in which bases
alice_bases = randint(2, size=n)
message = encode_message(alice_bits, alice_bases)
## Step 3
# Decide which basis to measure in:
bob_bases = randint(2, size=n)
bob_results = measure_message(message, bob_bases)
message[0].draw()
message[6].draw()
print(bob_results)
def remove_garbage(a_bases, b_bases, bits):
good_bits = []
for q in range(n):
if a_bases[q] == b_bases[q]:
# If both used the same basis, add
# this to the list of 'good' bits
good_bits.append(bits[q])
return good_bits
np.random.seed(seed=0)
n = 100
## Step 1
# Alice generates bits
alice_bits = randint(2, size=n)
## Step 2
# Create an array to tell us which qubits
# are encoded in which bases
alice_bases = randint(2, size=n)
message = encode_message(alice_bits, alice_bases)
## Step 3
# Decide which basis to measure in:
bob_bases = randint(2, size=n)
bob_results = measure_message(message, bob_bases)
## Step 4
alice_key = remove_garbage(alice_bases, bob_bases, alice_bits)
print(alice_key)
np.random.seed(seed=0)
n = 100
## Step 1
# Alice generates bits
alice_bits = randint(2, size=n)
## Step 2
# Create an array to tell us which qubits
# are encoded in which bases
alice_bases = randint(2, size=n)
message = encode_message(alice_bits, alice_bases)
## Step 3
# Decide which basis to measure in:
bob_bases = randint(2, size=n)
bob_results = measure_message(message, bob_bases)
## Step 4
alice_key = remove_garbage(alice_bases, bob_bases, alice_bits)
bob_key = remove_garbage(alice_bases, bob_bases, bob_results)
print(bob_key)
def sample_bits(bits, selection):
sample = []
for i in selection:
# use np.mod to make sure the
# bit we sample is always in
# the list range
i = np.mod(i, len(bits))
# pop(i) removes the element of the
# list at index 'i'
sample.append(bits.pop(i))
return sample
np.random.seed(seed=0)
n = 100
## Step 1
# Alice generates bits
alice_bits = randint(2, size=n)
## Step 2
# Create an array to tell us which qubits
# are encoded in which bases
alice_bases = randint(2, size=n)
message = encode_message(alice_bits, alice_bases)
## Step 3
# Decide which basis to measure in:
bob_bases = randint(2, size=n)
bob_results = measure_message(message, bob_bases)
## Step 4
alice_key = remove_garbage(alice_bases, bob_bases, alice_bits)
bob_key = remove_garbage(alice_bases, bob_bases, bob_results)
## Step 5
sample_size = 15
bit_selection = randint(n, size=sample_size)
bob_sample = sample_bits(bob_key, bit_selection)
print(" bob_sample = " + str(bob_sample))
alice_sample = sample_bits(alice_key, bit_selection)
print("alice_sample = "+ str(alice_sample))
bob_sample == alice_sample
print(bob_key)
print(alice_key)
print("key length = %i" % len(alice_key))
np.random.seed(seed=3)
np.random.seed(seed=3)
## Step 1
alice_bits = randint(2, size=n)
print(alice_bits)
np.random.seed(seed=3)
## Step 1
alice_bits = randint(2, size=n)
## Step 2
alice_bases = randint(2, size=n)
message = encode_message(alice_bits, alice_bases)
print(alice_bases)
message[0].draw()
np.random.seed(seed=3)
## Step 1
alice_bits = randint(2, size=n)
## Step 2
alice_bases = randint(2, size=n)
message = encode_message(alice_bits, alice_bases)
## Interception!!
eve_bases = randint(2, size=n)
intercepted_message = measure_message(message, eve_bases)
print(intercepted_message)
message[0].draw()
np.random.seed(seed=3)
## Step 1
alice_bits = randint(2, size=n)
## Step 2
alice_bases = randint(2, size=n)
message = encode_message(alice_bits, alice_bases)
## Interception!!
eve_bases = randint(2, size=n)
intercepted_message = measure_message(message, eve_bases)
## Step 3
bob_bases = randint(2, size=n)
bob_results = measure_message(message, bob_bases)
message[0].draw()
np.random.seed(seed=3)
## Step 1
alice_bits = randint(2, size=n)
## Step 2
alice_bases = randint(2, size=n)
message = encode_message(alice_bits, alice_bases)
## Interception!!
eve_bases = randint(2, size=n)
intercepted_message = measure_message(message, eve_bases)
## Step 3
bob_bases = randint(2, size=n)
bob_results = measure_message(message, bob_bases)
## Step 4
bob_key = remove_garbage(alice_bases, bob_bases, bob_results)
alice_key = remove_garbage(alice_bases, bob_bases, alice_bits)
np.random.seed(seed=3)
## Step 1
alice_bits = randint(2, size=n)
## Step 2
alice_bases = randint(2, size=n)
message = encode_message(alice_bits, alice_bases)
## Interception!!
eve_bases = randint(2, size=n)
intercepted_message = measure_message(message, eve_bases)
## Step 3
bob_bases = randint(2, size=n)
bob_results = measure_message(message, bob_bases)
## Step 4
bob_key = remove_garbage(alice_bases, bob_bases, bob_results)
alice_key = remove_garbage(alice_bases, bob_bases, alice_bits)
## Step 5
sample_size = 15
bit_selection = randint(n, size=sample_size)
bob_sample = sample_bits(bob_key, bit_selection)
print(" bob_sample = " + str(bob_sample))
alice_sample = sample_bits(alice_key, bit_selection)
print("alice_sample = "+ str(alice_sample))
bob_sample == alice_sample
n = 100
# Step 1
alice_bits = randint(2, size=n)
alice_bases = randint(2, size=n)
# Step 2
message = encode_message(alice_bits, alice_bases)
# Interception!
eve_bases = randint(2, size=n)
intercepted_message = measure_message(message, eve_bases)
# Step 3
bob_bases = randint(2, size=n)
bob_results = measure_message(message, bob_bases)
# Step 4
bob_key = remove_garbage(alice_bases, bob_bases, bob_results)
alice_key = remove_garbage(alice_bases, bob_bases, alice_bits)
# Step 5
sample_size = 15 # Change this to something lower and see if
# Eve can intercept the message without Alice
# and Bob finding out
bit_selection = randint(n, size=sample_size)
bob_sample = sample_bits(bob_key, bit_selection)
alice_sample = sample_bits(alice_key, bit_selection)
if bob_sample != alice_sample:
print("Eve's interference was detected.")
else:
print("Eve went undetected!")
import qiskit.tools.jupyter
%qiskit_version_table
|
https://github.com/yh08037/quantum-neural-network
|
yh08037
|
# !pip install qiskit-aer-gpu
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
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
# from torchsummary import summary
import qiskit
from qiskit.visualization import *
from qiskit.circuit.random import random_circuit
from itertools import combinations
if torch.cuda.is_available():
DEVICE = torch.device('cuda')
else:
DEVICE = torch.device('cpu')
print('Using PyTorch version:', torch.__version__, ' Device:', DEVICE)
print('cuda index:', torch.cuda.current_device())
print('GPU 이름:', torch.cuda.get_device_name())
BATCH_SIZE = 10
EPOCHS = 10 # Number of optimization epochs
n_layers = 1 # Number of random layers
n_train = 50 # Size of the train dataset
n_test = 30 # Size of the test dataset
SAVE_PATH = "quanvolution/" # Data saving folder
PREPROCESS = True # If False, skip quantum processing and load data from SAVE_PATH
seed = 47
np.random.seed(seed) # Seed for NumPy random number generator
torch.manual_seed(seed) # Seed for TensorFlow random number generator
train_dataset = datasets.MNIST(root = "./data",
train = True,
download = True,
transform = transforms.ToTensor())
train_dataset.data = train_dataset.data[:n_train]
train_dataset.targets = train_dataset.targets[:n_train]
test_dataset = datasets.MNIST(root = "./data",
train = False,
transform = transforms.ToTensor())
test_dataset.data = test_dataset.data[:n_test]
test_dataset.targets = test_dataset.targets[:n_test]
train_loader = torch.utils.data.DataLoader(dataset = train_dataset,
batch_size = BATCH_SIZE,
shuffle = True)
test_loader = torch.utils.data.DataLoader(dataset = test_dataset,
batch_size = BATCH_SIZE,
shuffle = False)
for (X_train, y_train) in train_loader:
print('X_train:', X_train.size(), 'type:', X_train.type())
print('y_train:', y_train.size(), 'type:', y_train.type())
break
pltsize = 1
plt.figure(figsize=(10 * pltsize, pltsize))
for i in range(10):
plt.subplot(1, 10, i + 1)
plt.axis('off')
plt.imshow(X_train[i, :, :, :].numpy().reshape(28, 28), cmap = "gray_r")
plt.title('Class: ' + str(y_train[i].item()))
class QuanvCircuit:
"""
This class defines filter circuit of Quanvolution layer
"""
def __init__(self, kernel_size, backend, shots, threshold):
# --- Circuit definition start ---
self.n_qubits = kernel_size ** 2
self._circuit = qiskit.QuantumCircuit(self.n_qubits)
self.theta = [qiskit.circuit.Parameter('theta{}'.format(i)) for i in range(self.n_qubits)]
for i in range(self.n_qubits):
self._circuit.rx(self.theta[i], i)
self._circuit.barrier()
self._circuit += random_circuit(self.n_qubits, 2)
self._circuit.measure_all()
# ---- Circuit definition end ----
self.backend = backend
self.shots = shots
self.threshold = threshold
def run(self, data):
# data shape: tensor (1, 5, 5)
# val > self.threshold : |1> - rx(pi)
# val <= self.threshold : |0> - rx(0)
# reshape input data
# [1, kernel_size, kernel_size] -> [1, self.n_qubits]
data = torch.reshape(data, (1, self.n_qubits))
# encoding data to parameters
thetas = []
for dat in data:
theta = []
for val in dat:
if val > self.threshold:
theta.append(np.pi)
else:
theta.append(0)
thetas.append(theta)
param_dict = dict()
for theta in thetas:
for i in range(self.n_qubits):
param_dict[self.theta[i]] = theta[i]
param_binds = [param_dict]
# execute random quantum circuit
job = qiskit.execute(self._circuit,
self.backend,
shots = self.shots,
parameter_binds = param_binds)
result = job.result().get_counts(self._circuit)
# decoding the result
counts = 0
for key, val in result.items():
cnt = sum([int(char) for char in key])
counts += cnt * val
# Compute probabilities for each state
probabilities = counts / (self.shots * self.n_qubits)
# probabilities = counts / self.shots
return probabilities
# backend = qiskit.Aer.get_backend('qasm_simulator')
backend = qiskit.providers.aer.QasmSimulator(method = "statevector_gpu")
filter_size = 2
circ = QuanvCircuit(filter_size, backend, 100, 127)
data = torch.tensor([[0, 200], [100, 255]])
# data = torch.tensor([[0, 200, 128, 192,168], [100, 255,53,47,29],[100, 255,53,47,29],[0, 200, 128, 192,168],[0, 200, 128, 192,168]])
print(data.size())
print(circ.run(data))
circ._circuit.draw(output='mpl')
'''
def quanv_feed(image):
"""
Convolves the input image with many applications
of the same quantum circuit.
In the standard language of CNN, this would correspond to
a convolution with a 5×5 kernel and a stride equal to 1.
"""
out = np.zeros((24, 24, 25))
# Loop over the coordinates of the top-left pixel of 5X5 squares
for j in range(24):
for k in range(24):
# Process a squared 5x5 region of the image with a quantum circuit
circuit_input = []
for a in range(5):
for b in range(5):
circuit_input.append(image[j + a, k + b, 0])
q_results = circuit(circuit_input)
# Assign expectation values to different channels of the output pixel (j/2, k/2)
for c in range(25):
out[24, 24, c] = q_results[c]
return out
'''
class QuanvFunction(Function):
""" Quanv function definition """
@staticmethod
def forward(ctx, inputs, in_channels, out_channels, kernel_size, quantum_circuits, shift):
""" Forward pass computation """
# input shape : (-1, 1, 28, 28)
# otuput shape : (-1, 6, 24, 24)
ctx.in_channels = in_channels
ctx.out_channels = out_channels
ctx.kernel_size = kernel_size
ctx.quantum_circuits = quantum_circuits
ctx.shift = shift
_, _, len_x, len_y = inputs.size()
len_x = len_x - kernel_size + 1
len_y = len_y - kernel_size + 1
outputs = torch.zeros((len(inputs), len(quantum_circuits), len_x, len_y)).to(DEVICE)
for i in range(len(inputs)):
print(f"batch{i}")
input = inputs[i]
for c in range(len(quantum_circuits)):
circuit = quantum_circuits[c]
print(f"channel{c}")
for h in range(len_y):
for w in range(len_x):
data = input[0, h:h+kernel_size, w:w+kernel_size]
outputs[i, c, h, w] = circuit.run(data)
# print(f"({h},{w})")
ctx.save_for_backward(inputs, outputs)
return outputs
'''
features = []
for input in inputs:
feature = []
for circuit in quantum_circuits:
xys = []
for x in range(len_x):
ys = []
for y in range(len_y):
data = input[0, x:x+kernel_size, y:y+kernel_size]
ys.append(circuit.run(data))
xys.append(ys)
feature.append(xys)
features.append(feature)
result = torch.tensor(features)
ctx.save_for_backward(inputs, result)
return result
'''
@staticmethod
def backward(ctx, grad_output): # 확인 필요(검증 x)
""" Backward pass computation """
input, expectation_z = ctx.saved_tensors
input_list = np.array(input.tolist())
shift_right = input_list + np.ones(input_list.shape) * ctx.shift
shift_left = input_list - np.ones(input_list.shape) * ctx.shift
gradients = []
for i in range(len(input_list)):
expectation_right = ctx.quantum_circuit.run(shift_right[i])
expectation_left = ctx.quantum_circuit.run(shift_left[i])
gradient = torch.tensor([expectation_right]) - torch.tensor([expectation_left])
gradients.append(gradient)
gradients = np.array([gradients]).T
return torch.tensor([gradients]).float() * grad_output.float(), None, None
class Quanv(nn.Module):
""" Quanvolution(Quantum convolution) layer definition """
def __init__(self, in_channels, out_channels, kernel_size,
backend=qiskit.providers.aer.QasmSimulator(method="statevector_gpu"),
shots=100, shift=np.pi/2):
super(Quanv, self).__init__()
self.quantum_circuits = [QuanvCircuit(kernel_size=kernel_size,
backend=backend, shots=shots, threshold=127)
for i in range(out_channels)]
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.shift = shift
def forward(self, inputs):
return QuanvFunction.apply(inputs, self.in_channels, self.out_channels, self.kernel_size,
self.quantum_circuits, self.shift)
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.quanv = Quanv(in_channels=1, out_channels=1, kernel_size = 3) # <- Quanv!!!!
self.conv = nn.Conv2d(1, 16, kernel_size=5)
self.dropout = nn.Dropout2d()
self.fc1 = nn.Linear(256, 64)
self.fc2 = nn.Linear(64, 10)
def forward(self, x):
x = F.relu(self.quanv(x))
x = F.max_pool2d(x, 2)
x = F.relu(self.conv(x))
x = F.max_pool2d(x, 2)
x = self.dropout(x)
# x = x.view(-1, 256)
x = torch.flatten(x, start_dim=1)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return F.softmax(x)
model = Net().to(DEVICE)
optimizer = optim.Adam(model.parameters(), lr=0.001)
loss_func = nn.CrossEntropyLoss()
epochs = 20
loss_list = []
model.train().to(DEVICE)
for epoch in range(epochs):
total_loss = []
for batch_idx, (data, target) in enumerate(train_loader):
target = target.to(DEVICE)
optimizer.zero_grad()
# Forward pass
output = model(data).to(DEVICE)
# Calculating loss
loss = loss_func(output, target).to(DEVICE)
# Backward pass
loss.backward()
# Optimize the weights
optimizer.step()
total_loss.append(loss.item())
loss_list.append(sum(total_loss)/len(total_loss))
print('Training [{:.0f}%]\tLoss: {:.4f}'.format(
100. * (epoch + 1) / epochs, loss_list[-1]))
model.eval()
with torch.no_grad():
correct = 0
for batch_idx, (data, target) in enumerate(test_loader):
data = data.cuda()
target = target.cuda()
output = model(data).cuda()
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
loss = loss_func(output, target)
total_loss.append(loss.item())
print('Performance on test data:\n\tLoss: {:.4f}\n\tAccuracy: {:.1f}%'.format(
sum(total_loss) / len(total_loss),
correct / len(test_loader) * 100 / batch_size)
)
|
https://github.com/qiskit-community/qiskit-translations-staging
|
qiskit-community
|
from qiskit.visualization.timeline import draw as timeline_draw
from qiskit import QuantumCircuit, transpile
from qiskit.providers.fake_provider import FakeBoeblingen
backend = FakeBoeblingen()
ghz = QuantumCircuit(5)
ghz.h(0)
ghz.cx(0,range(1,5))
circ = transpile(ghz, backend, scheduling_method="asap")
timeline_draw(circ)
|
https://github.com/nielsaNTNU/qiskit_utilities
|
nielsaNTNU
|
import numpy as np
import pylab as pl
import qiskit.tools.jupyter
from qiskit import execute, QuantumCircuit, QuantumRegister, ClassicalRegister
from qiskit.quantum_info import Kraus, SuperOp
from qiskit.providers.aer import QasmSimulator
from qiskit.tools.visualization import plot_histogram
# Import from Qiskit Aer noise module
from qiskit import *
from qiskit.providers.aer.noise import NoiseModel
from qiskit.providers.aer.noise import QuantumError, ReadoutError
from qiskit.providers.aer.noise import depolarizing_error
from qiskit.providers.aer.noise import thermal_relaxation_error
backendname_sim = Aer.get_backend('qasm_simulator')
circ = QuantumCircuit(2,2)
circ.h(0)
circ.cx(0,1)
circ.measure(0,0)
circ.measure(1,1)
circ.draw(output='mpl')
circs=[]
for i in range(1024):
circs.append(circ)
job_clean = execute(circs, backendname_sim, shots=1024)
results_clean=job_clean.result().results
# counts_clean=job.result().get_counts()
# plot_histogram(counts_clean)
# T1 and T2 values for qubits 0-4
T1s = np.random.normal(50e2, 10e2, 5) # Sampled from normal distribution mean 50 microsec
T2s = np.random.normal(70e2, 10e2, 5) # Sampled from normal distribution mean 50 microsec
# Truncate random T2s <= T1s
T2s = np.array([min(T2s[j], 2 * T1s[j]) for j in range(5)])
# Instruction times (in nanoseconds)
time_u1 = 0 # virtual gate
time_u2 = 50 # (single X90 pulse)
time_u3 = 100 # (two X90 pulses)
time_cx = 300
time_reset = 1000 # 1 microsecond
time_measure = 1000 # 1 microsecond
# QuantumError objects
errors_reset = [thermal_relaxation_error(t1, t2, time_reset)
for t1, t2 in zip(T1s, T2s)]
errors_measure = [thermal_relaxation_error(t1, t2, time_measure)
for t1, t2 in zip(T1s, T2s)]
errors_u1 = [thermal_relaxation_error(t1, t2, time_u1)
for t1, t2 in zip(T1s, T2s)]
errors_u2 = [thermal_relaxation_error(t1, t2, time_u2)
for t1, t2 in zip(T1s, T2s)]
errors_u3 = [thermal_relaxation_error(t1, t2, time_u3)
for t1, t2 in zip(T1s, T2s)]
errors_cx = [[thermal_relaxation_error(t1a, t2a, time_cx).expand(
thermal_relaxation_error(t1b, t2b, time_cx))
for t1a, t2a in zip(T1s, T2s)]
for t1b, t2b in zip(T1s, T2s)]
# Add errors to noise model
noise_thermal = NoiseModel()
for j in range(5):
noise_thermal.add_quantum_error(errors_reset[j], "reset", [j])
noise_thermal.add_quantum_error(errors_measure[j], "measure", [j])
noise_thermal.add_quantum_error(errors_u1[j], "u1", [j])
noise_thermal.add_quantum_error(errors_u2[j], "u2", [j])
noise_thermal.add_quantum_error(errors_u3[j], "u3", [j])
for k in range(5):
noise_thermal.add_quantum_error(errors_cx[j][k], "cx", [j, k])
print(noise_thermal)
job_thermal = execute(circs, backendname_sim,
basis_gates=noise_thermal.basis_gates,
noise_model=noise_thermal,shots=1024)
results_thermal=job_thermal.result().results
# result_thermal = job.result()
counts_clean = job_clean.result().get_counts(0)
counts_thermal = job_thermal.result().get_counts(0)
# Plot noisy output
plot_histogram([counts_thermal,counts_clean],legend=['thermal','clean'])
numones=np.zeros((2**5,1))
for i in range(2**5):
numones[i]=bin(i).count("1")
def expectationValue(results):
#num_qubits = results[0].header.n_qubits
E=np.zeros((len(results),1))
for item in range(0,len(results)):
shots = results[item].shots
counts = results[item].data.counts
for key in list(counts.__dict__.keys()):
c=getattr(counts, key)#number of counts
E[item] += numones[int(key,0)]*c/shots
E_conv = np.zeros_like(E)
for j in range(1,len(results)+1):
E_conv[j-1] = sum(E[0:j])/j
return E, E_conv
E_clean, E_conv_clean = expectationValue(job_clean.result().results)
E_thermal, E_conv_thermal = expectationValue(job_thermal.result().results)
pl.plot(E_conv_clean)
pl.plot(E_conv_thermal)
|
https://github.com/hephaex/Quantum-Computing-Awesome-List
|
hephaex
|
from qiskit import *
from math import pi
import numpy as np
from qiskit.visualization import plot_bloch_multivector, plot_histogram
qc = QuantumCircuit(3)
for qubit in range(3):
qc.h(qubit)
qc.draw()
backend = Aer.get_backend('statevector_simulator')
final_state = execute(qc, backend).result().get_statevector()
print(final_state)
qc = QuantumCircuit(2)
qc.h(0)
qc.x(1)
qc.draw()
backend = Aer.get_backend('unitary_simulator')
unitary = execute(qc, backend).result().get_unitary()
print(unitary)
qiskit.__qiskit_version__
qc = QuantumCircuit(3)
qc.x(0)
qc.z(1)
qc.h(2)
qc.draw()
unitary = execute(qc, backend).result().get_unitary()
print(unitary)
qc = QuantumCircuit(2)
# Apply H-gate to the first:
qc.h(0)
qc.draw()
backend = Aer.get_backend('statevector_simulator')
final_state = execute(qc,backend).result().get_statevector()
print(final_state)
qc = QuantumCircuit(2)
# Apply H-gate to the first:
qc.h(0)
# Apply a CNOT:
qc.cx(0,1)
qc.draw()
final_state = Aer.get_backend('statevector_simulator')
final_state = execute(qc, backend).result().get_statevector()
print(final_state)
results = execute(qc,backend).result().get_counts()
plot_histogram(results)
qc = QuantumCircuit(2)
qc.h(0)
qc.x(1)
qc.cx(0,1)
qc.draw()
backend = Aer.get_backend('statevector_simulator')
result = execute(qc, backend).result().get_statevector()
print(result)
result = execute(qc, backend).result().get_counts()
plot_histogram(result)
unitary = execute(qc, backend).result().get_unitary()
print(unitary)
|
https://github.com/qiskit-community/qiskit-translations-staging
|
qiskit-community
|
from qiskit import BasicAer, transpile, QuantumRegister, ClassicalRegister, QuantumCircuit
qr = QuantumRegister(1)
cr = ClassicalRegister(1)
qc = QuantumCircuit(qr, cr)
qc.h(0)
qc.measure(0, 0)
qc.x(0).c_if(cr, 0)
qc.measure(0, 0)
qc.draw('mpl')
|
https://github.com/qiskit-community/qiskit-translations-staging
|
qiskit-community
|
from qiskit import QuantumCircuit, transpile
from qiskit.visualization import plot_circuit_layout
from qiskit.providers.fake_provider import FakeVigo
backend = FakeVigo()
ghz = QuantumCircuit(3, 3)
ghz.h(0)
ghz.cx(0,range(1,3))
ghz.barrier()
ghz.measure(range(3), range(3))
new_circ_lv0 = transpile(ghz, backend=backend, optimization_level=0)
plot_circuit_layout(new_circ_lv0, backend)
|
https://github.com/martynscn/Masters-Thesis-on-Quantum-Cryptography
|
martynscn
|
# This code has been adapted and modified from IBM Qiskit 2021 and also from https://github.com/ttlion/ShorAlgQiskit.
# It uses the implementation as contained in the work of Stephane Beauregard (https://arxiv.org/abs/quant-ph/0205095)
# Many thanks to IBM Qiskit team, Tiago Miguel (ttlion), Qubit by Qubit, Peter Shor and Stephane Beauregard.
from typing import Optional, Union, Tuple, List
import math
import array
import fractions
import logging
import numpy as np
from qiskit import ClassicalRegister, QuantumCircuit, QuantumRegister, execute, IBMQ, transpile,BasicAer, Aer, assemble
from qiskit.circuit import Gate, Instruction, ParameterVector
from qiskit.circuit.library import QFT
from qiskit.providers import BaseBackend, Backend
from qiskit.quantum_info import partial_trace
from qiskit.utils import summarize_circuits
from qiskit.utils.arithmetic import is_power
from qiskit.utils.validation import validate_min
from qiskit.utils.quantum_instance import QuantumInstance
import qiskit.visualization
from qiskit.providers.aer import QasmSimulator
from datetime import datetime
import csv
# provider = IBMQ.enable_account("PUT TOKEN HERE")
backend = QasmSimulator()
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all" #"last_expr" or "all"
# """ Function to check if N is of type q^p"""
def check_if_power(N):
# """ Check if N is a perfect power in O(n^3) time, n=ceil(logN) """
b=2
while (2**b) <= N:
a = 1
c = N
while (c-a) >= 2:
m = int( (a+c)/2 )
if (m**b) < (N+1):
p = int( (m**b) )
else:
p = int(N+1)
if int(p) == int(N):
print('N is {0}^{1}'.format(int(m),int(b)) )
return True
if p<N:
a = int(m)
else:
c = int(m)
b=b+1
return False
def egcd(a, b):
if a == 0:
return (b, 0, 1)
else:
g, y, x = egcd(b % a, a)
return (g, x - (b // a) * y, y)
def modinv(a, m):
g, x, y = egcd(a, m)
if g != 1:
raise Exception('modular inverse does not exist')
else:
return x % m
def create_QFT(circuit,up_reg,n,with_swaps):
i=n-1
while i>=0:
circuit.h(up_reg[i])
j=i-1
while j>=0:
if (np.pi)/(pow(2,(i-j))) > 0:
circuit.cu1( (np.pi)/(pow(2,(i-j))) , up_reg[i] , up_reg[j] )
j=j-1
i=i-1
if with_swaps==1:
i=0
while i < ((n-1)/2):
circuit.swap(up_reg[i], up_reg[n-1-i])
i=i+1
def create_inverse_QFT(circuit,up_reg,n,with_swaps):
if with_swaps==1:
i=0
while i < ((n-1)/2):
circuit.swap(up_reg[i], up_reg[n-1-i])
i=i+1
i=0
while i<n:
circuit.h(up_reg[i])
if i != n-1:
j=i+1
y=i
while y>=0:
if (np.pi)/(pow(2,(j-y))) > 0:
circuit.cu1( - (np.pi)/(pow(2,(j-y))) , up_reg[j] , up_reg[y] )
y=y-1
i=i+1
def getAngle(a, N):
s=bin(int(a))[2:].zfill(N)
angle = 0
for i in range(0, N):
if s[N-1-i] == '1':
angle += math.pow(2, -(N-i))
angle *= np.pi
return angle
def getAngles(a,N):
s=bin(int(a))[2:].zfill(N)
angles=np.zeros([N])
for i in range(0, N):
for j in range(i,N):
if s[j]=='1':
angles[N-i-1]+=math.pow(2, -(j-i))
angles[N-i-1]*=np.pi
return angles
def ccphase(circuit, angle, ctl1, ctl2, tgt):
circuit.cu1(angle/2,ctl1,tgt)
circuit.cx(ctl2,ctl1)
circuit.cu1(-angle/2,ctl1,tgt)
circuit.cx(ctl2,ctl1)
circuit.cu1(angle/2,ctl2,tgt)
def phiADD(circuit, q, a, N, inv):
angle=getAngles(a,N)
for i in range(0,N):
if inv==0:
circuit.u1(angle[i],q[i])
else:
circuit.u1(-angle[i],q[i])
def cphiADD(circuit, q, ctl, a, n, inv):
angle=getAngles(a,n)
for i in range(0,n):
if inv==0:
circuit.cu1(angle[i],ctl,q[i])
else:
circuit.cu1(-angle[i],ctl,q[i])
def ccphiADD(circuit,q,ctl1,ctl2,a,n,inv):
angle=getAngles(a,n)
for i in range(0,n):
if inv==0:
ccphase(circuit,angle[i],ctl1,ctl2,q[i])
else:
ccphase(circuit,-angle[i],ctl1,ctl2,q[i])
def ccphiADDmodN(circuit, q, ctl1, ctl2, aux, a, N, n):
ccphiADD(circuit, q, ctl1, ctl2, a, n, 0)
phiADD(circuit, q, N, n, 1)
# phiADD(circuit, q, a,N, 1)
create_inverse_QFT(circuit, q, n, 0)
circuit.cx(q[n-1],aux)
create_QFT(circuit,q,n,0)
cphiADD(circuit, q, aux, N, n, 0)
# cphiADD(circuit, q, aux, a, n, 0)
ccphiADD(circuit, q, ctl1, ctl2, a, n, 1)
create_inverse_QFT(circuit, q, n, 0)
circuit.x(q[n-1])
circuit.cx(q[n-1], aux)
circuit.x(q[n-1])
create_QFT(circuit,q,n,0)
ccphiADD(circuit, q, ctl1, ctl2, a, n, 0)
def ccphiADDmodN_inv(circuit, q, ctl1, ctl2, aux, a, N, n):
ccphiADD(circuit, q, ctl1, ctl2, a, n, 1)
create_inverse_QFT(circuit, q, n, 0)
circuit.x(q[n-1])
circuit.cx(q[n-1],aux)
circuit.x(q[n-1])
create_QFT(circuit, q, n, 0)
ccphiADD(circuit, q, ctl1, ctl2, a, n, 0)
cphiADD(circuit, q, aux, N, n, 1)
# cphiADD(circuit, q, aux, a, n, 1)
create_inverse_QFT(circuit, q, n, 0)
circuit.cx(q[n-1], aux)
create_QFT(circuit, q, n, 0)
phiADD(circuit, q, N, n, 0)
# phiADD(circuit, q, a, N, 0)
ccphiADD(circuit, q, ctl1, ctl2, a, n, 1)
def cMULTmodN(circuit, ctl, q, aux, a, N, n):
# up_reg = QuantumRegister(1, name = "up_reg")
# down_reg = QuantumRegister(n, name = "down_reg")
# up_classic = ClassicalRegister(2*n, name="up_classic")
# c_aux = ClassicalRegister(1, name = "aux_classic")
# cMULTmodN_circuit = QuantumCircuit(
# up_reg ,down_reg , aux,up_classic, c_aux,
# name=r"${0}^{{{1}^{{{2}}}}} mod{3}$".format(2,2,int(math.log(math.log(a,2),2)), N)
# )
# create_QFT(cMULTmodN_circuit,aux,n+1,0)
# for i in range(0, n):
# ccphiADDmodN(cMULTmodN_circuit, aux, q[i], ctl, aux[n+1], (2**i)*a % N, N, n+1)
# create_inverse_QFT(cMULTmodN_circuit, aux, n+1, 0)
# for i in range(0, n):
# circuit.cswap(ctl,q[i],aux[i])
# cMULTmodN_circuit.cswap(ctl,q[i],aux[i])
# create_QFT(cMULTmodN_circuit, aux, n+1, 0)
# ccphiADDmodN_inv(cMULTmodN_circuit, aux, q[i], ctl, aux[n+1], math.pow(2,i)*a_inv % N, N, n+1)
# create_inverse_QFT(cMULTmodN_circuit, aux, n+1, 0)
# cMULTmodN_circuit_instruction = cMULTmodN_circuit.to_instruction()
# circuit.append(cMULTmodN_circuit_instruction, [ctl, *down_reg, *aux])
create_QFT(circuit,aux,n+1,0)
for i in range(0, n):
ccphiADDmodN(circuit, aux, q[i], ctl, aux[n+1], (2**i)*a % N, N, n+1)
create_inverse_QFT(circuit, aux, n+1, 0)
for i in range(0, n):
circuit.cswap(ctl,q[i],aux[i])
a_inv = modinv(a, N)
create_QFT(circuit, aux, n+1, 0)
i = n-1
while i >= 0:
ccphiADDmodN_inv(circuit, aux, q[i], ctl, aux[n+1], math.pow(2,i)*a_inv % N, N, n+1)
i -= 1
create_inverse_QFT(circuit, aux, n+1, 0)
def calculate_continued_fraction(b: array.array) -> int:
# """Calculate the continued fraction of x/T from the current terms of expansion b."""
x_over_T = 0
for i in reversed(range(len(b) - 1)):
x_over_T = 1 / (b[i + 1] + x_over_T)
x_over_T += b[0]
frac = fractions.Fraction(x_over_T).limit_denominator()
return frac.denominator
def get_factors(N: int, a: int, measurement: str) -> Optional[List[int]]:
# """Apply the continued fractions to find r and the gcd to find the desired factors."""
x_final = int(measurement, 2)
#print('In decimal, x_final value for this result is: {}.'.format(x_final))
if x_final <= 0:
fail_reason = 'x_final value is <= 0, there are no continued fractions.'
else:
fail_reason = None
#print('Running continued fractions for this case.')
# Calculate T and x/T
T_upper = len(measurement)
T = pow(2, T_upper)
x_over_T = x_final / T ## this is our theta
# Cycle in which each iteration corresponds to putting one more term in the
# calculation of the Continued Fraction (CF) of x/T
# Initialize the first values according to CF rule
i = 0
b = array.array('i')
t = array.array('f')
b.append(math.floor(x_over_T))
t.append(x_over_T - b[i])
exponential = 0.0
while i < N and fail_reason is None:
# From the 2nd iteration onwards, calculate the new terms of the CF based on the previous terms as the rule suggests
if i > 0:
try:
b_temp = math.floor(1 / t[i - 1])
except ZeroDivisionError as err:
b_temp = 0
b.append(b_temp)
try:
t_temp = (1 / t[i - 1]) - b[i]
except ZeroDivisionError as err:
t_temp = 0
t.append(t_temp) # type: ignore
# Calculate the denominator of the CF using the known terms
denominator = calculate_continued_fraction(b)
# Increment i for next iteration
i += 1
if denominator % 2 == 1:
#print('Odd denominator, will try next iteration of continued fractions.')
continue
# Denominator is even, try to get factors of N. Get the exponential a^(r/2)
if denominator < 1000:
try:
exponential = pow(a, denominator / 2)
except OverflowError as err:
exponential = 999999999
# Check if the value is too big or not
if exponential > 1000000:
if exponential == 999999999:
fail_reason = 'OverflowError'
else:
fail_reason = 'denominator of continued fraction is too big (> 10^3).'
else:
# The value is not too big, get the right values and do the proper gcd()
putting_plus = int(exponential + 1)
putting_minus = int(exponential - 1)
one_factor = math.gcd(putting_plus, N)
other_factor = math.gcd(putting_minus, N)
# Check if the factors found are trivial factors or are the desired factors
if any(factor in {1, N} for factor in (one_factor, other_factor)):
#print('Found just trivial factors, not good enough.')
# Check if the number has already been found, (use i - 1 because i was already incremented)
if t[i - 1] == 0:
fail_reason = 'the continued fractions found exactly x_final/(2^(2n)).'
else:
return sorted((one_factor, other_factor))
return None
def process_results(sim_result, circuit, shots, N, a, n):
counts_result = sim_result.get_counts(circuit)
total_counts = len(counts_result)
counts_result_sorted = sorted(counts_result.items(), key=lambda x: x[1], reverse=True)
counts_result_keys = list(counts_result.keys())
counts_result_values = list(counts_result.values())
prob_success=0
prob_failure=0
result_successful_counts = 0
result_failure_counts = 0
for initial_undesired_measurement, frequency in counts_result_sorted:
measurement = initial_undesired_measurement.split(" ")[1]
x_value = int(measurement, 2)
prob_this_result = 100 * frequency/shots
factors = get_factors(N, a, measurement)
if factors:
prob_success = prob_success + prob_this_result
result_successful_counts = result_successful_counts + 1
if factors not in result_factors:
result_factors.append(factors)
elif not factors:
prob_failure = prob_failure + prob_this_result
result_failure_counts = result_failure_counts + 1
return [result_factors, prob_success, prob_failure, total_counts, result_successful_counts,result_failure_counts]
def my_shor(a,N,shots):
start_time_number = datetime.now()
start_time = start_time_number.strftime("%H:%M:%S")
summary_result = dict()
validate_min('N', N, 3)
validate_min('a', a, 2)
if N < 1 or N % 2 == 0:
raise ValueError('The input needs to be an odd integer greater than 1.')
if a >= N or math.gcd(a, N) != 1:
raise ValueError('The integer a needs to satisfy a < N and gcd(a, N) = 1.')
n = math.ceil(math.log(N,2))
global result_factors
result_factors = []
tf, b, p = is_power(N, return_decomposition=True)
if tf:
print('The input integer is a power: {0}={1}^{2}.'.format(N, b, p))
result_factors.append(b)
# """auxilliary quantum register used in addition and multiplication"""
aux = QuantumRegister(size = n+2, name="aux_reg")
# """single qubit where the sequential QFT is performed"""
up_reg = QuantumRegister(1, name = "up_reg")
down_reg = QuantumRegister(n, name = "down_reg")
# """classical register where the measured values of the sequential QFT are stored"""
up_classic = ClassicalRegister(2*n, name="up_classic")
# """classical bit used to reset the state of the top qubit to 0 if the previous measurement was 1"""
c_aux = ClassicalRegister(1, name = "aux_classic")
# """ Create Quantum Circuit """
circuit = QuantumCircuit(up_reg ,down_reg , aux,up_classic, c_aux)
circuit.x(down_reg[0])
# circuit.draw(filename = "shor_semiclassical_QFT_initialization")
for i in range(0, 2*n):
circuit.x(up_reg).c_if(c_aux, 1)
circuit.h(up_reg)
cMULTmodN(circuit, up_reg[0], down_reg, aux, a**(2**(2*n-1-i)), N, n)
# later confirm if this should be up_reg[i] instead of up_reg[0]
for j in range(0, 2**i):
circuit.u1(getAngle(j, i), up_reg[0]).c_if(up_classic, j)
circuit.h(up_reg)
circuit.measure(up_reg[0], up_classic[i])
circuit.measure(up_reg[0], c_aux[0])
# circuit.draw(filename = "shor_semiclassical_QFT_final_circuit")
circuit.draw()
qc_compiled = transpile(circuit, backend, optimization_level = 3)
job_sim_1 = backend.run(qc_compiled, shots=shots)
sim_result=job_sim_1.result()
# counts_result = sim_result.get_counts(circuit)
# len(counts_result)
# measurement_plot = qiskit.visualization.plot_histogram(counts_result,figsize=(20, 12) ,number_to_keep = 30,bar_labels=True, title = "Measurement results from shor_standard_QFT circuit variant" )
# measurement_plot.savefig("shor_semiclassical_QFT_measurement_result")
# measurement_plot
processed_result = process_results(sim_result, circuit, shots, N, a, n)
end_time_number = datetime.now()
end_time = end_time_number.strftime("%H:%M:%S")
duration = end_time_number - start_time_number
print("Current Start Time =", start_time)
print(processed_result)
print("Current End Time =", end_time)
circuit_count_ops = circuit.count_ops()
circuit_decomposed = circuit.decompose()
circuit_decomposed_count_ops = circuit_decomposed.count_ops()
qc_compiled_count_ops = qc_compiled.count_ops()
summary_result["num_qubits"] = n
summary_result["Number(N)"] = N
summary_result["a"] = a
summary_result["start_time"] = start_time
summary_result["end_time"] = end_time
summary_result["duration"] = duration
summary_result["result_factors"] = processed_result[0]
summary_result["prob_success"] = processed_result[1]
summary_result["prob_failure"] = processed_result[2]
summary_result["total_counts"] = processed_result[3]
summary_result["result_successful_counts"] = processed_result[4]
summary_result["result_failure_counts"] = processed_result[5]
summary_result["circuit_width"] = circuit.width()
summary_result["circuit_depth"] = circuit.depth()
summary_result["circuit_size"] = circuit.size()
summary_result["circuit_num_nonlocal_gates"] = circuit.num_nonlocal_gates()
summary_result["circuit_num_ancillas"] = circuit.num_ancillas
summary_result["circuit_num_clbits"] = circuit.num_clbits
summary_result["circuit_num_qubits"] = circuit.num_qubits
summary_result["circuit_num_ancillas"] = circuit.num_ancillas
summary_result["circuit_num_of_count_ops"] = len(circuit_count_ops)
summary_result["circuit_num_of_x"] = circuit_count_ops.get('x')
summary_result["circuit_num_of_measure"] = circuit_count_ops.get('measure')
summary_result["circuit_num_of_h"] = circuit_count_ops.get('h')
summary_result["circuit_num_of_cswap"] = circuit_count_ops.get('cswap')
summary_result["circuit_num_of_swap"] = circuit_count_ops.get('swap')
summary_result["circuit_num_of_cx"] = circuit_count_ops.get('cx')
summary_result["circuit_num_of_toffoli"] = circuit_count_ops.get('toffoli')
summary_result["circuit_num_of_p"] = circuit_count_ops.get('p')
summary_result["circuit_num_of_t"] = circuit_count_ops.get('t')
summary_result["circuit_decomposed_width"] = circuit_decomposed.width()
summary_result["circuit_decomposed_depth"] = circuit_decomposed.depth()
summary_result["circuit_decomposed_size"] = circuit_decomposed.size()
summary_result["circuit_decomposed_num_nonlocal_gates"] = circuit_decomposed.num_nonlocal_gates()
summary_result["circuit_decomposed_num_ancillas"] = circuit_decomposed.num_ancillas
summary_result["circuit_decomposed_num_clbits"] = circuit_decomposed.num_clbits
summary_result["circuit_decomposed_num_qubits"] = circuit_decomposed.num_qubits
summary_result["circuit_decomposed_num_ancillas"] = circuit_decomposed.num_ancillas
summary_result["circuit_decomposed_num_of_count_ops"] = len(circuit_decomposed_count_ops)
summary_result["circuit_decomposed_num_of_x"] = circuit_decomposed_count_ops.get('x')
summary_result["circuit_decomposed_num_of_measure"] = circuit_decomposed_count_ops.get('measure')
summary_result["circuit_decomposed_num_of_h"] = circuit_decomposed_count_ops.get('h')
summary_result["circuit_decomposed_num_of_cswap"] = circuit_decomposed_count_ops.get('cswap')
summary_result["circuit_decomposed_num_of_swap"] = circuit_decomposed_count_ops.get('swap')
summary_result["circuit_decomposed_num_of_cx"] = circuit_decomposed_count_ops.get('cx')
summary_result["circuit_decomposed_num_of_toffoli"] = circuit_decomposed_count_ops.get('toffoli')
summary_result["circuit_decomposed_num_of_p"] = circuit_decomposed_count_ops.get('p')
summary_result["circuit_decomposed_num_of_t"] = circuit_decomposed_count_ops.get('t')
summary_result["qc_compiled_width"] = qc_compiled.width()
summary_result["qc_compiled_depth"] = qc_compiled.depth()
summary_result["qc_compiled_size"] = qc_compiled.size()
summary_result["qc_compiled_num_nonlocal_gates"] = qc_compiled.num_nonlocal_gates()
summary_result["qc_compiled_num_ancillas"] = qc_compiled.num_ancillas
summary_result["qc_compiled_num_clbits"] = qc_compiled.num_clbits
summary_result["qc_compiled_num_qubits"] = qc_compiled.num_qubits
summary_result["qc_compiled_num_ancillas"] = qc_compiled.num_ancillas
summary_result["qc_compiled_num_of_count_ops"] = len(qc_compiled_count_ops)
summary_result["qc_compiled_num_of_x"] = qc_compiled_count_ops.get('x')
summary_result["qc_compiled_num_of_measure"] = qc_compiled_count_ops.get('measure')
summary_result["qc_compiled_num_of_h"] = qc_compiled_count_ops.get('h')
summary_result["qc_compiled_num_of_cswap"] = qc_compiled_count_ops.get('cswap')
summary_result["qc_compiled_num_of_swap"] = qc_compiled_count_ops.get('swap')
summary_result["qc_compiled_num_of_cx"] = qc_compiled_count_ops.get('cx')
summary_result["qc_compiled_num_of_toffoli"] = qc_compiled_count_ops.get('toffoli')
summary_result["qc_compiled_num_of_p"] = qc_compiled_count_ops.get('p')
summary_result["qc_compiled_num_of_t"] = qc_compiled_count_ops.get('t')
return summary_result
# Run for just a single number N
%%time
N = 21
shots = 1024
global result_factors
all_summary_result_temp = []
for random_a in range(2, N):
if math.gcd(random_a,N) > 1:
continue
a = random_a
summary_result = my_shor(a,N,shots)
print("Finished running for a = {} and N = {}\n".format(a, N))
all_summary_result_temp.append(summary_result)
summary_result_list = []
for key, value in summary_result.items():
summary_result_list.append([key,value])
summary_result_list
with open("a({0})_N({1})_semiclassical.csv".format(a, N), 'a') as myfile:
write = csv.writer(myfile)
#write.writerow(fields)
write.writerows(summary_result_list)
all_summary_result_temp
# Run for many numbers N.
%%time
shots = 1024
global result_factors
all_summary_result = []
for N in [15, 21, 33, 35, 39, 51, 55, 57]:
for a in range(2, N):
if math.gcd(a,N) > 1:
continue
print("Beginning running for a = {} and N = {}".format(a, N))
summary_result = my_shor(a,N,shots)
print("Finished running for a = {} and N = {}\n\n".format(a, N))
all_summary_result.append(summary_result)
all_summary_result
%qiskit_copyright
|
https://github.com/qiskit-community/qiskit-translations-staging
|
qiskit-community
|
from qiskit.providers.fake_provider import FakeHanoi
backend = FakeHanoi()
config = backend.configuration()
# Basic Features
print("This backend is called {0}, and is on version {1}. It has {2} qubit{3}. It "
"{4} OpenPulse programs. The basis gates supported on this device are {5}."
"".format(config.backend_name,
config.backend_version,
config.n_qubits,
'' if config.n_qubits == 1 else 's',
'supports' if config.open_pulse else 'does not support',
config.basis_gates))
config.dt # units of seconds
config.meas_levels
config.dtm
config.meas_map
config.drive(0)
config.measure(0)
config.acquire(0)
props = backend.properties()
def describe_qubit(qubit, properties):
"""Print a string describing some of reported properties of the given qubit."""
# Conversion factors from standard SI units
us = 1e6
ns = 1e9
GHz = 1e-9
print("Qubit {0} has a \n"
" - T1 time of {1} microseconds\n"
" - T2 time of {2} microseconds\n"
" - U2 gate error of {3}\n"
" - U2 gate duration of {4} nanoseconds\n"
" - resonant frequency of {5} GHz".format(
qubit,
properties.t1(qubit) * us,
properties.t2(qubit) * us,
properties.gate_error('sx', qubit),
properties.gate_length('sx', qubit) * ns,
properties.frequency(qubit) * GHz))
describe_qubit(0, props)
defaults = backend.defaults()
q0_freq = defaults.qubit_freq_est[0] # Hz
q0_meas_freq = defaults.meas_freq_est[0] # Hz
GHz = 1e-9
print("DriveChannel(0) defaults to a modulation frequency of {} GHz.".format(q0_freq * GHz))
print("MeasureChannel(0) defaults to a modulation frequency of {} GHz.".format(q0_meas_freq * GHz))
calibrations = defaults.instruction_schedule_map
print(calibrations)
measure_schedule = calibrations.get('measure', range(config.n_qubits))
measure_schedule.draw(backend=backend)
# You can use `has` to see if an operation is defined. Ex: Does qubit 3 have an x gate defined?
calibrations.has('x', 3)
# Some circuit operations take parameters. U1 takes a rotation angle:
calibrations.get('u1', 0, P0=3.1415)
import qiskit.tools.jupyter
%qiskit_version_table
%qiskit_copyright
|
https://github.com/sohamch08/Qiskit-Quantum-Algo
|
sohamch08
|
from qiskit import ClassicalRegister, QuantumCircuit, QuantumRegister
import numpy as np
def step_1_circuit(qr: QuantumRegister, cr: ClassicalRegister) -> QuantumCircuit:
# qr is a quantum register with 2 qubits
# cr is a classical register with 2 bits
qc = QuantumCircuit(qr, cr)
########## your code goes here #######
##1 Initialization
q0, q1 = qr
# apply Hadamard on the auxiliary qubit
qc.h(q0)
# put the system qubit into the |1> state
qc.x(q1)
##2 Apply control-U operator as many times as needed to get the least significant phase bit
# controlled-S is equivalent to CPhase with angle pi / 2
s_angle = np.pi / 2
# we want to apply controlled-S 2^k times
k = 1
# calculate the angle of CPhase corresponding to 2^k applications of controlled-S
cphase_angle = s_angle * 2**k
# apply the controlled phase gate
qc.cp(cphase_angle, q0, q1)
##3 Measure the auxiliary qubit in x-basis into the first classical bit
# apply Hadamard to change to the X basis
qc.h(q0)
# measure the auxiliary qubit into the first classical bit
c0, _ = cr
qc.measure(q0, c0)
return qc
qr = QuantumRegister(2, "q")
cr = ClassicalRegister(2, "c")
qc = QuantumCircuit(qr, cr)
qc = step_1_circuit(qr, cr)
qc.draw("mpl")
# Submit your circuit
from qc_grader.challenges.spring_2023 import grade_ex3a
grade_ex3a(qc)
def step_2_circuit(qr: QuantumRegister, cr: ClassicalRegister) -> QuantumCircuit:
# qr is a quantum register with 2 qubits
# cr is a classical register with 2 bits
# begin with the circuit from Step 1
qc = step_1_circuit(qr, cr)
########## your code goes here #######
##1 Reset and re-initialize the auxiliary qubit
q0, q1 = qr
# reset the auxiliary qubit
qc.reset(q0)
# apply Hadamard on the auxiiliary qubit
qc.h(q0)
##2 Apply phase correction conditioned on the first classical bit
c0, c1 = cr
with qc.if_test((c0, 1)):
qc.p(-np.pi / 2, q0)
##3 Apply control-U operator as many times as needed to get the next phase bit
# controlled-S is equivalent to CPhase with angle pi / 2
s_angle = np.pi / 2
# we want to apply controlled-S 2^k times
k = 0
# calculate the angle of CPhase corresponding to 2^k applications of controlled-S
cphase_angle = s_angle * 2**k
# apply the controlled phase gate
qc.cp(cphase_angle, q0, q1)
##4 Measure the auxiliary qubit in x-basis into the second classical bit
# apply Hadamard to change to the X basis
qc.h(q0)
# measure the auxiliary qubit into the first classical bit
qc.measure(q0, c1)
return qc
qr = QuantumRegister(2, "q")
cr = ClassicalRegister(2, "c")
qc = QuantumCircuit(qr, cr)
qc = step_2_circuit(qr, cr)
qc.draw("mpl")
# Submit your circuit
from qc_grader.challenges.spring_2023 import grade_ex3b
grade_ex3b(qc)
from qiskit_aer import AerSimulator
sim = AerSimulator()
job = sim.run(qc, shots=1000)
result = job.result()
counts = result.get_counts()
counts
from qiskit import ClassicalRegister, QuantumCircuit, QuantumRegister
import numpy as np
def t_gate_ipe_circuit(qr: QuantumRegister, cr: ClassicalRegister) -> QuantumCircuit:
# qr is a quantum register with 2 qubits
# cr is a classical register with 3 bits
qc = QuantumCircuit(qr, cr)
########## your code goes here #######
# Initialization
q0, q1 = qr
qc.h(q0)
qc.x(q1)
# Apply control-U operator as many times as needed to get the least significant phase bit
t_angle = np.pi / 4
k = 2
cphase_angle = t_angle * 2**k
qc.cp(cphase_angle, q0, q1)
# Measure the auxiliary qubit in x-basis into the first classical bit
qc.h(q0)
c0, c1, c2 = cr
qc.measure(q0, c0)
# Reset and re-initialize the auxiliary qubit
qc.reset(q0)
qc.h(q0)
# Apply phase correction conditioned on the first classical bit
with qc.if_test((c0, 1)):
qc.p(-np.pi / 2, q0)
# Apply control-U operator as many times as needed to get the next phase bit
k = 1
cphase_angle = t_angle * 2**k
qc.cp(cphase_angle, q0, q1)
# Measure the auxiliary qubit in x-basis into the second classical bit
qc.h(q0)
qc.measure(q0, c1)
# Reset and re-initialize the auxiliary qubit
qc.reset(q0)
qc.h(q0)
# Apply phase correction conditioned on the first and second classical bits
with qc.if_test((c0, 1)):
qc.p(-np.pi / 4, q0)
with qc.if_test((c1, 1)):
qc.p(-np.pi / 2, q0)
# Apply control-U operator as many times as needed to get the next phase bit
k = 0
cphase_angle = t_angle * 2**k
qc.cp(cphase_angle, q0, q1)
# Measure the auxiliary qubit in x-basis into the third classical bit
qc.h(q0)
qc.measure(q0, c2)
return qc
qr = QuantumRegister(2, "q")
cr = ClassicalRegister(3, "c")
qc = QuantumCircuit(qr, cr)
qc = t_gate_ipe_circuit(qr, cr)
qc.draw("mpl")
from qiskit_aer import AerSimulator
sim = AerSimulator()
job = sim.run(qc, shots=1000)
result = job.result()
counts = result.get_counts()
counts
# Submit your circuit
from qc_grader.challenges.spring_2023 import grade_ex3c
grade_ex3c(qc)
from qiskit import ClassicalRegister, QuantumCircuit, QuantumRegister
import numpy as np
def u_circuit(qr: QuantumRegister, cr: ClassicalRegister) -> QuantumCircuit:
# qr is a quantum register with 2 qubits
# cr is a classical register with 2 bits
qc = QuantumCircuit(qr, cr)
# Initialization
q0, q1 = qr
qc.h(q0)
qc.x(q1)
# Apply control-U operator as many times as needed to get the least significant phase bit
u_angle = 2 * np.pi / 3
k = 1
cphase_angle = u_angle * 2**k
qc.cp(cphase_angle, q0, q1)
# Measure the auxiliary qubit in x-basis into the first classical bit
qc.h(q0)
c0, c1 = cr
qc.measure(q0, c0)
# Reset and re-initialize the auxiliary qubit
qc.reset(q0)
qc.h(q0)
# Apply phase correction conditioned on the first classical bit
with qc.if_test((c0, 1)):
qc.p(-np.pi / 2, q0)
# Apply control-U operator as many times as needed to get the next phase bit
k = 0
cphase_angle = u_angle * 2**k
qc.cp(cphase_angle, q0, q1)
# Measure the auxiliary qubit in x-basis into the second classical bit
qc.h(q0)
qc.measure(q0, c1)
return qc
qr = QuantumRegister(2, "q")
cr = ClassicalRegister(2, "c")
qc = QuantumCircuit(qr, cr)
qc = u_circuit(qr, cr)
qc.draw("mpl")
from qiskit_aer import AerSimulator
sim = AerSimulator()
job = sim.run(qc, shots=1000)
result = job.result()
counts = result.get_counts()
print(counts)
success_probability = counts["01"] / counts.shots()
print(f"Success probability: {success_probability}")
from qiskit import ClassicalRegister, QuantumCircuit, QuantumRegister
import numpy as np
def u_circuit(qr: QuantumRegister, cr: ClassicalRegister) -> QuantumCircuit:
# qr is a quantum register with 2 qubits
# cr is a classical register with 1 bits
qc = QuantumCircuit(qr, cr)
# Initialization
q0, q1 = qr
qc.h(q0)
qc.x(q1)
# Apply control-U operator as many times as needed to get the least significant phase bit
u_angle = 2 * np.pi / 3
k = 1
cphase_angle = u_angle * 2**k
qc.cp(cphase_angle, q0, q1)
# Measure the auxiliary qubit in x-basis
qc.h(q0)
(c0,) = cr
qc.measure(q0, c0)
return qc
qr = QuantumRegister(2, "q")
cr = ClassicalRegister(1, "c")
qc = QuantumCircuit(qr, cr)
qc = u_circuit(qr, cr)
qc.draw("mpl")
job = sim.run(qc, shots=15)
result = job.result()
counts = result.get_counts()
print(counts)
step1_bit = 1
####### your code goes here #######
print(step1_bit)
# Submit your result
from qc_grader.challenges.spring_2023 import grade_ex3d
grade_ex3d(step1_bit)
from qiskit import ClassicalRegister, QuantumCircuit, QuantumRegister
import numpy as np
def u_circuit(qr: QuantumRegister, cr: ClassicalRegister) -> QuantumCircuit:
# qr is a quantum register with 2 qubits
# cr is a classical register with 2 bits
qc = QuantumCircuit(qr, cr)
########## your code goes here #######
# Initialization
q0, q1 = qr
if step1_bit:
qc.x(q0)
qc.x(q1)
# Measure the auxiliary qubit
c0, c1 = cr
qc.measure(q0, c0)
# Reset and re-initialize the auxiliary qubit
qc.reset(q0)
qc.h(q0)
# Apply phase correction conditioned on the first classical bit
with qc.if_test((c0, 1)):
qc.p(-np.pi / 2, q0)
# Apply control-U operator as many times as needed to get the next phase bit
u_angle = 2 * np.pi / 3
k = 0
cphase_angle = u_angle * 2**k
qc.cp(cphase_angle, q0, q1)
# Measure the auxiliary qubit in x-basis into the second classical bit
qc.h(q0)
qc.measure(q0, c1)
return qc
qr = QuantumRegister(2, "q")
cr = ClassicalRegister(2, "c")
qc = QuantumCircuit(qr, cr)
qc = u_circuit(qr, cr)
qc.draw("mpl")
# Submit your result
from qc_grader.challenges.spring_2023 import grade_ex3e
grade_ex3e(qc)
from qiskit_aer import AerSimulator
sim = AerSimulator()
job = sim.run(qc, shots=1000)
result = job.result()
counts = result.get_counts()
print(counts)
success_probability = counts["01"] / counts.shots()
print(f"Success probability: {success_probability}")
from qiskit_ibm_provider import IBMProvider
provider = IBMProvider()
hub = "qc-spring-23-1"
group = "group-1"
project = "recIvcxUcc27LvvSh"
backend_name = "ibm_peekskill"
backend = provider.get_backend(backend_name, instance=f"{hub}/{group}/{project}")
from qiskit import transpile
qr = QuantumRegister(2, "q")
cr = ClassicalRegister(2, "c")
qc = QuantumCircuit(qr, cr)
qc = step_2_circuit(qr, cr)
qc_transpiled = transpile(qc, backend)
job = backend.run(qc_transpiled, shots=1000, dynamic=True)
job_id = job.job_id()
print(job_id)
retrieve_job = provider.retrieve_job(job_id)
retrieve_job.status()
from qiskit.tools.visualization import plot_histogram
counts = retrieve_job.result().get_counts()
plot_histogram(counts)
|
https://github.com/arian-code/nptel_quantum_assignments
|
arian-code
|
from sympy import *
import numpy as np
init_printing(use_unicode=True)
a, b, d, f = symbols('alpha beta theta phi')
a, b, d, f
p1=exp(I*f)
H=Matrix([[1/sqrt(2), 1/sqrt(2)],[1/sqrt(2), -1/sqrt(2)]])
S=Matrix([[0, 1],[1, I]])
X=Matrix([[0, 1],[1, 0]])
Y=Matrix([[0, -I],[I, 0]])
Z=Matrix([[1, 0],[0, -1]])
H, S, X, Y, Z
#option A
lhs=H*X*H
rhs=-1*Z
print (lhs)
print (rhs)
if (lhs==rhs):
print ("It is true")
else:
print ("It is false")
#option B
lhs=H*Y*H
rhs=-1*Y
print (lhs)
print (rhs)
if (lhs==rhs):
print ("It is true")
else:
print ("It is false")
#option C
lhs=H
rhs=1/sqrt(2)*(X+Z)
print (lhs)
print (rhs)
if (lhs==rhs):
print ("It is true")
else:
print ("It is false")
#option D
lhs=H*Z*H
rhs=X
print (lhs)
print (rhs)
if (lhs==rhs):
print ("It is true")
else:
print ("It is false")
|
https://github.com/sergiogh/qpirates-qiskit-notebooks
|
sergiogh
|
#initialization
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import math
# importing Qiskit
from qiskit import IBMQ, Aer
from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister, execute
# import basic plot tools
from qiskit.visualization import plot_histogram
from qiskit.circuit.library import QFT
def qft_dagger(circ, qubits, n):
circ.append(QFT(n).inverse(), qubits)
# Add UROTx based on CU1 gates
def apply_crot(circ, control_qubit, target_qubit, theta, exponent):
circ.cu1(2*math.pi*theta*exponent, control_qubit, target_qubit)
def prepare_circuit(n, theta):
qpe = QuantumCircuit(n+1,n)
qpe.x(n)
qpe.h(range(n))
for x in range(n):
exponent = 2**(n-x-1)
apply_crot(qpe, x, n, theta, exponent)
qpe.barrier()
qft_dagger(qpe, range(n), n)
qpe.barrier()
for i in range(n):
qpe.measure(i,i)
return qpe
# The idea is that we are able to estimate theta (the angle) thanks to the QFT Dagger.
# The more qubits we apply on top, the more accuracy we get. Take a look
import operator
backend = Aer.get_backend('qasm_simulator')
shots = 2048
theta = 0.24125
for qubits in range(2, 15):
circuit = prepare_circuit(qubits, theta)
results = execute(circuit, backend=backend, shots=shots).result()
counts = results.get_counts(circuit)
highest_probability_outcome = max(counts.items(), key=operator.itemgetter(1))[0][::-1]
measured_theta = int(highest_probability_outcome, 2)/2**qubits
print("Using %d qubits with theta = %.6f, measured_theta = %.6f." % (qubits, theta, measured_theta))
## Look at the lovely circuit
circuit.draw('text')
|
https://github.com/geduardo/Q-Snake-Qiskitcamp-Europe-2019
|
geduardo
|
import pew_tunnel as pew
import random
import pygame
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister, execute, Aer
import numpy as np
#########################################################################
#FUNCTIONS
#########################################################################
simulator = Aer.get_backend('statevector_simulator')
#################################################################
###################
#IBMQ real backends
###################
from qiskit import IBMQ
from qiskit.providers.ibmq import least_busy
# Load local account information
provider = IBMQ.load_account()
def ibmq_qrand(nbits, N):
# Get the least busy real quantum system
backend = least_busy(provider.backends(simulator=False))
print(backend, backend.status().pending_jobs)
circ = QuantumCircuit(1, 1)
circ.h(0)
circ.measure(0, 0)
job = execute(circ, backend, memory=True, shots=int(N*nbits))
res = job.result()
val = res.get_memory()
print(val)
integ = np.zeros(N)
for k in range(N):
#convert val array into bitstring b and then into integer
b = ''
shift = int(nbits * k)
for i in range(nbits):
b += str(val[i+shift])
integ[k] = int(b, 2)
return integ
def get_rand_from_list(counter, list):
return list[counter]
###############################################################
def qrand(nbits):
"""generates nbits real random numbers using quantum state measurements in qiskit."""
circ = QuantumCircuit(1, 1)
circ.h(0)
circ.measure(0, 0)
val = np.zeros(nbits)
for i in range(nbits):
job=execute(circ, simulator)
res = job.result()
vec = res.get_statevector()
val[i] = vec[0] * 1 + vec[1] * 0
#convert val array into bitstring b and then into integer
b = ''
for i in range(nbits):
b += str(int(val[i]))
integ = int(b, 2)
return integ
def Pt(U0, E, L, betac, gamma_sqc):
"""return tunneling probability for square barrier"""
return 1/ (np.cosh(betac * L)**2 + gamma_sqc * np.sinh(betac * L)**2)
def beta(U0, E):
"""supply function for Pt"""
return np.sqrt(2* (U0 - E))
def gamma_sq(U0, E):
"""supply function for Pt"""
return 0.25 * ((1 - E/U0)/(E/U0) + (E/U0)/(1-E/U0) - 2)
def theta(p_tunnel):
"""returns rotation angle corresponding to tunneling prob. p_tunnel"""
return 2 * np.arcsin(np.sqrt(p_tunnel))
def tunnelres(U0, length_snake, L, betac, gamma_sqc):
"""returns 0 if tunnel, returns 1 if no tunnel"""
P_t = Pt(U0, length_snake, L, betac, gamma_sqc) #get tunneling prob depending on current snake length
theta_rot = theta(P_t) #get rot angle
#qcirc
qr = QuantumRegister(1)
cr = ClassicalRegister(1)
circ = QuantumCircuit(qr, cr)
circ.rx(theta_rot, qr[0])
circ.measure(qr, cr)
job = execute(circ, simulator)
res = job.result()
vec = res.get_statevector()
val = vec[0] * 1 + vec[1] * 0
if val == 1:
return 0
else:
return 1
#r= random.randint(0, 1)
#return round(r)
##########################################################################
#MAIN
##########################################################################
#initialize pew
dis = pew.init()
screen = pew.Pix()
#set size
bits = 3
ds= 2**bits #displazsize
#set game starting parameters
game_speed = 4
snake = [(2, 4)]
dx, dy = 1, 0
apple_x, apple_y = 6, 4
screen.pixel(apple_x, apple_y, 2)
howmanyapples = 1 #marker for total number of eaten apples, used for scoring
#set graphics for probability display
pygame.font.init()
#gate backkgorund
font1 = pygame.font.Font(None, 33)
text = font1.render('Probability for tunneling is', True, (255, 0, 0))
dis.blit(text, (20, 330))
font2 = pygame.font.Font(None, 45)
text2 = font2.render('100%', True, (255, 0, 0))
dis.blit(text2, (130, 360))
ima = pygame.image.load('pewblack.jpg')
#tunneling parameters
U0=37 #max snake length = 6x6 = 36
E=1
L=0.05 #optimal barrier size for nice tunneling probabilities
num_list = ibmq_qrand(3, 250) #list of random numbers for ibmq function
num_list_x = num_list[:125]
num_list_y = num_list[125:]
#initialize tunneling tracker
tunnel=0 #don't see other side as second barrier
snakepos=1 #marker of snakepos, 1=head, increase towards tail
headtunnel=0 #let the head tunnel again through other even if tail still in process
while True: #snake runs
#create barrier
bar= []
for i in range(ds):
screen.pixel(0, i, 1)
screen.pixel(ds-1, i, 1)
screen.pixel(i, 0, 1)
screen.pixel(i, ds-1, 1)
bar.append((0, i))
bar.append((ds-1, i))
bar.append((i, 0))
bar.append((i, ds-1))
#find the head
if len(snake) > 1:
x, y = snake[-2]
screen.pixel(x, y, 1)
x, y = snake[-1]
screen.pixel(x, y, 3) #color the head yellow
pew.show(screen)
pew.tick(1 / game_speed)
#get commands
keys = pew.keys()
if headtunnel==0:
if keys & pew.K_UP and dy == 0:
dx, dy = 0, -1
elif keys & pew.K_LEFT and dx == 0:
dx, dy = -1, 0
elif keys & pew.K_RIGHT and dx == 0:
dx, dy = 1, 0
elif keys & pew.K_DOWN and dy == 0:
dx, dy = 0, 1
x = (x + dx) % 8
y = (y + dy) % 8
elif headtunnel==1: #steering not allowed during tunneling of the head (during two rounds)
x = (x + dx) % 8
y = (y + dy) % 8
headtunnel=2
elif headtunnel>=2:
x = (x + dx) % 8
y = (y + dy) % 8
headtunnel=0
##TUNNELING PROCESS
#snake tail tunnels
if tunnel>0 and snakepos<=len(snake):
#get segment for tunneling
sx, sy = snake[-snakepos]
E=len(snake)/2 #divide by two for lower tunnel prob for tail (lower mass->lower energy)
tunnels = tunnelres(U0, E, L, beta(U0, E), gamma_sq(U0, E))
if tunnels==1: #tunnels
snakepos+=1
else: #does not tunnel
del snake[-snakepos]
screen.pixel(sx, sy, 0)
#reset if last segment tunneled
if tunnel>0 and snakepos==(len(snake)+1):
tunnel=0
snakepos=1
#snake head tunnels
if headtunnel==0 and (x, y) in bar:
E=len(snake)
tunnel = tunnelres(U0, E, L, beta(U0, E), gamma_sq(U0, E))
if tunnel==0 and len(snake) != 1: #head doesn't tunnel --> game over
break
else:
snakepos+=1
headtunnel+=1
elif headtunnel==1 and (x, y) in bar:
headtunnel=0
#display tunneling prob.
E = len(snake)
if E > 1:
prob = Pt(U0, E, L, beta(U0, E), gamma_sq(U0, E))
text3 = font2.render(str(int(round(prob * 100))) + '%', True, (255, 0, 0))
dis.blit(ima, (130, 360))
dis.blit(text3, (130, 360))
else: #if length of snake ==1 (only head), tunneling prob = 100%
dis.blit(ima, (130, 360)) #cover the ultimate prob. display
dis.blit(text2, (130, 360)) #text2 = '100%'
#####TUNNEL END
if (x, y) in snake: #exit, game over condition
break
snake.append((x, y))
#apple generation
if x == apple_x and y == apple_y:
screen.pixel(apple_x, apple_y, 0)
apple_x, apple_y = snake[0]
while (apple_x, apple_y) in snake or (apple_x, apple_y) in bar:
apple_x = get_rand_from_list(howmanyapples, num_list_x) #random.getrandbits(3) #use this for pseudo random number gen, no qiskit needed
apple_y = get_rand_from_list(howmanyapples, num_list_y) #random.getrandbits(3)
screen.pixel(apple_x, apple_y, 2)
game_speed += 0.2 #increase game speed
howmanyapples += 1 #increase number of eaten apples, score +1
else:
x, y = snake.pop(0)
screen.pixel(x, y, 0)
text = pew.Pix.from_text("Game over!") #Game over message and closing
for dx in range(-8, text.width):
screen.blit(text, -dx, 1)
pew.show(screen)
pew.tick(1 / 12)
text = pew.Pix.from_text("Score:" + str(int(howmanyapples))) #Score message
for dx in range(-8, text.width):
screen.blit(text, -dx, 1)
pew.show(screen)
pew.tick(1 / 12)
|
https://github.com/hugoecarl/TSP-Problem-Study
|
hugoecarl
|
with open('in-exemplo.txt', 'r') as f:
print(f.read())
with open('out-exemplo.txt', 'r') as f:
print(f.read())
import time
import matplotlib.pyplot as plt
import pandas as pd
import os
import subprocess
%matplotlib inline
#Roda entradas
def roda_com_entrada(executavel, arquivo_in, envs = '1', deb = '0'):
with open(arquivo_in) as f:
start = time.perf_counter()
a = f.read()
proc = subprocess.run([executavel], input=a, text=True, capture_output=True, env=dict(OMP_NUM_THREADS=envs, DEBUG=deb, **os.environ))
end = time.perf_counter()
f.close()
ret = ''
for i in a:
if i == "\n":
break
ret += i
return (proc.stdout, end - start, int(ret))
#retorna tamanho do tour apartir do stdout
def tamanho_tour(out):
buf = ''
for i in out:
if i == " ":
return float(buf)
buf += i
#Cria resultados
tamanho_entradas = []
tempos = []
tempos_1 = []
tempos_2 = []
resultados1 = []
resultados2 = []
resultados3 = []
#Usando as mesmas entradas
for i in range(8):
print("Rodando entrada: "+str(i))
a = roda_com_entrada('./busca_local_antigo','busca-exaustiva/in-'+str(i)+'.txt')
b = roda_com_entrada('./busca-exaustiva/busca-exaustiva','busca-exaustiva/in-'+str(i)+'.txt')
c = roda_com_entrada('./heuristico/heuristico','busca-exaustiva/in-'+str(i)+'.txt')
tempos.append(a[1])
tempos_1.append(b[1])
tempos_2.append(c[1])
tamanho_entradas.append(a[2])
resultados1.append(tamanho_tour(a[0]))
resultados2.append(tamanho_tour(b[0]))
resultados3.append(tamanho_tour(c[0]))
#Teste com entrada um pouco maior
print("Rodando entrada: 8")
tempos.append(roda_com_entrada('./busca_local_antigo','maior.txt')[1])
tempos_1.append(roda_com_entrada('./busca-exaustiva/busca-exaustiva','maior.txt')[1])
tempos_2.append(roda_com_entrada('./heuristico/heuristico','maior.txt')[1])
tamanho_entradas.append(roda_com_entrada('./busca-local/busca-local','maior.txt')[2])
resultados1.append(tamanho_tour(roda_com_entrada('./busca_local_antigo','maior.txt')[0]))
resultados2.append(tamanho_tour(roda_com_entrada('./busca-exaustiva/busca-exaustiva','maior.txt')[0]))
resultados3.append(tamanho_tour(roda_com_entrada('./heuristico/heuristico','maior.txt')[0]))
plt.title("Comparacao Desempenho")
plt.ylabel("Tempo (s)")
plt.xlabel("Tamanho entradas")
plt.plot(tamanho_entradas, tempos_1)
plt.plot(tamanho_entradas, tempos)
plt.plot(tamanho_entradas, tempos_2)
plt.legend(["busca-exaustiva", "busca-local","heuristico"])
plt.show()
plt.title("Comparacao Desempenho")
plt.ylabel("Tempo (s)")
plt.xlabel("Tamanho entradas")
plt.plot(tamanho_entradas, tempos)
plt.plot(tamanho_entradas, tempos_2)
plt.legend(["busca-local","heuristico"])
plt.show()
df = pd.DataFrame()
df["Tamanho Entrada"] = pd.Series(tamanho_entradas)
df["busca-local-tempo"] = pd.Series(tempos)
df["busca-exaustiva-tempo"] = pd.Series(tempos_1)
df["heuristica-tempo"] = pd.Series(tempos_2)
df["busca-local-resultado"] = pd.Series(resultados1)
df["busca-exaustiva-resultado"] = pd.Series(resultados2)
df["heuristica-resultado"] = pd.Series(resultados3)
df
df.describe()
#Cria resultados
tamanho_entradas = []
tempos = []
tempos_1 = []
tempos_2 = []
tempos_3 = []
tempos_4 = []
tempos_5 = []
#Usando as mesmas entradas
for i in range(7):
print("Rodando entrada: "+str(i))
a = roda_com_entrada('./busca-local/busca-local-paralela','busca-local/in-'+str(i)+'.txt')
b = roda_com_entrada('./busca-local/busca-local-paralela','busca-local/in-'+str(i)+'.txt','2')
c = roda_com_entrada('./busca-local/busca-local-paralela','busca-local/in-'+str(i)+'.txt','3')
d = roda_com_entrada('./busca-local/busca-local-paralela','busca-local/in-'+str(i)+'.txt','4')
e = roda_com_entrada('./busca_local_antigo','busca-local/in-'+str(i)+'.txt')
f = roda_com_entrada('./busca-local/busca-local-gpu','busca-local/in-'+str(i)+'.txt')
tempos.append(a[1])
tempos_1.append(b[1])
tempos_2.append(c[1])
tempos_3.append(d[1])
tempos_4.append(e[1])
tempos_5.append(f[1])
tamanho_entradas.append(a[2])
plt.title("Comparacao Desempenho Busca Local")
plt.ylabel("Tempo (s)")
plt.xlabel("Tamanho entradas")
plt.plot(tamanho_entradas, tempos)
plt.plot(tamanho_entradas, tempos_1)
plt.plot(tamanho_entradas, tempos_2)
plt.plot(tamanho_entradas, tempos_3)
plt.legend(["1 thread otimizado", "2 threads otimizado","3 threads otimizado", "4 threads otimizado", "Sem otimizações"])
plt.show()
plt.title("Comparacao Desempenho Busca Local")
plt.ylabel("Tempo (s)")
plt.xlabel("Tamanho entradas")
plt.plot(tamanho_entradas, tempos_3)
plt.plot(tamanho_entradas, tempos_5)
plt.legend(["4 thread otimizado", "GPU"])
plt.show()
plt.title("Comparacao Desempenho Busca Local")
plt.ylabel("Tempo (s)")
plt.xlabel("Tamanho entradas")
plt.plot(tamanho_entradas, tempos)
plt.plot(tamanho_entradas, tempos_4)
plt.legend(["1 thread otimizado", "Sem otimizações"])
plt.show()
df = pd.DataFrame()
df["Tamanho Entrada"] = pd.Series(tamanho_entradas)
df["busca-local-1-thread"] = pd.Series(tempos)
df["busca-local-2-threads"] = pd.Series(tempos_1)
df["busca-local-3-threads"] = pd.Series(tempos_2)
df["busca-local-4-threads"] = pd.Series(tempos_3)
df["busca-local-gpu"] = pd.Series(tempos_5)
df["busca-local-semopt"] = pd.Series(tempos_4)
df
#Cria resultados
tamanho_entradas = []
tempos = []
tempos_1 = []
#Usando as mesmas entradas
for i in range(7):
print("Rodando entrada: "+str(i))
a = roda_com_entrada('./busca-exaustiva/busca-exaustiva','busca-exaustiva/in-'+str(i)+'.txt', '1', '1')
b = roda_com_entrada('./busca-exaustiva/busca-exaustiva','busca-exaustiva/in-'+str(i)+'.txt', '1', '0')
tempos.append(a[1])
tempos_1.append(b[1])
tamanho_entradas.append(a[2])
plt.title("Comparacao Desempenho Busca Exaustiva")
plt.ylabel("Tempo (s)")
plt.xlabel("Tamanho entradas")
plt.plot(tamanho_entradas, tempos)
plt.plot(tamanho_entradas, tempos_1)
plt.legend(["Exaustivo Simples", "Exaustivo Branch and Bound"])
plt.show()
df = pd.DataFrame()
df["Tamanho Entrada"] = pd.Series(tamanho_entradas)
df["busca-exaustiva-simples"] = pd.Series(tempos)
df["busca-exaustiva-branchnbound"] = pd.Series(tempos_1)
df
|
https://github.com/robinsonvs/tcc-information-systems
|
robinsonvs
|
from qiskit import QuantumCircuit as qc
from qiskit import QuantumRegister as qr
from qiskit import transpile
from qiskit.providers import Backend
from qiskit.quantum_info import DensityMatrix as dm
from qiskit.result import Counts
from qiskit.visualization import plot_histogram
from qiskit_ibm_runtime import QiskitRuntimeService
from math import pi, sqrt
from heapq import nlargest
"""Number of qubits."""
N: int = 5
"""Set of m nonnegative integers to search for using Grover's algorithm (i.e. TARGETS in base 10)."""
SEARCH_VALUES: set[int] = { 11, 9, 0, 3 }
"""Amount of times to simulate the algorithm."""
SHOTS: int = 10000
"""Set of m N-qubit binary strings representing target state(s) (i.e. SEARCH_VALUES in base 2)."""
TARGETS: set[str] = { f"{s:0{N}b}" for s in SEARCH_VALUES }
"""N-qubit quantum register."""
QUBITS: qr = qr(N, "qubit")
"""Name of backend to run the algorithm on."""
BACKEND_NAME: str = "ibmq_qasm_simulator"
"""Backend to run the algorithm on."""
BACKEND: Backend = QiskitRuntimeService().backend(BACKEND_NAME)
def oracle(targets: set[str] = TARGETS, name: str = "Oracle") -> qc:
"""Mark target state(s) with negative phase.
Args:
targets (set[str]): N-qubit binary string(s) representing target state(s). Defaults to TARGETS.
name (str, optional): Quantum circuit's name. Defaults to "Oracle".
Returns:
qc: Quantum circuit representation of oracle.
"""
# Create N-qubit quantum circuit for oracle
oracle = qc(QUBITS, name = name)
for target in targets:
# Reverse target state since Qiskit uses little-endian for qubit ordering
target = target[::-1]
# Flip zero qubits in target
for i in range(N):
if target[i] == "0":
oracle.x(i) # Pauli-X gate
# Simulate (N - 1)-control Z gate
oracle.h(N - 1) # Hadamard gate
oracle.mcx(list(range(N - 1)), N - 1) # (N - 1)-control Toffoli gate
oracle.h(N - 1) # Hadamard gate
# Flip back to original state
for i in range(N):
if target[i] == "0":
oracle.x(i) # Pauli-X gate
return oracle
# Generate and display quantum circuit for oracle
grover_oracle = oracle()
grover_oracle.draw("mpl", style = "iqp")
def diffuser(name: str = "Diffuser") -> qc:
"""Amplify target state(s) amplitude, which decreases the amplitudes of other states
and increases the probability of getting the correct solution (i.e. target state(s)).
Args:
name (str, optional): Quantum circuit's name. Defaults to "Diffuser".
Returns:
qc: Quantum circuit representation of diffuser (i.e. Grover's diffusion operator).
"""
# Create N-qubit quantum circuit for diffuser
diffuser = qc(QUBITS, name = name)
diffuser.h(QUBITS) # Hadamard gate
diffuser.append(oracle({ "0" * N }), list(range(N))) # Oracle with all zero target state
diffuser.h(QUBITS) # Hadamard gate
return diffuser
# Generate and display quantum circuit for diffuser
grover_diffuser = diffuser()
grover_diffuser.draw("mpl", style = "iqp")
def grover(oracle: qc = oracle(), diffuser: qc = diffuser(), name: str = "Grover Circuit") -> tuple[qc, dm]:
"""Create quantum circuit representation of Grover's algorithm,
which consists of 4 parts: (1) state preparation/initialization,
(2) oracle, (3) diffuser, and (4) measurement of resulting state.
Steps 2-3 are repeated an optimal number of times (i.e. Grover's
iterate) in order to maximize probability of success of Grover's algorithm.
Args:
oracle (qc, optional): Quantum circuit representation of oracle. Defaults to oracle().
diffuser (qc, optional): Quantum circuit representation of diffuser. Defaults to diffuser().
name (str, optional): Quantum circuit's name. Defaults to "Grover Circuit".
Returns:
tuple[qc, dm]: Quantum circuit representation of Grover's algorithm and its density matrix.
"""
# Create N-qubit quantum circuit for Grover's algorithm
grover = qc(QUBITS, name = name)
# Intialize qubits with Hadamard gate (i.e. uniform superposition)
grover.h(QUBITS)
# Apply barrier to separate steps
grover.barrier()
# Apply oracle and diffuser (i.e. Grover operator) optimal number of times
for _ in range(int((pi / 4) * sqrt((2 ** N) / len(TARGETS)))):
grover.append(oracle, list(range(N)))
grover.append(diffuser, list(range(N)))
# Generate density matrix representation of Grover's algorithm
density_matrix = dm(grover)
# Measure all qubits once finished
grover.measure_all()
return grover, density_matrix
# Save density matrix and generate and display quantum circuit for Grover's algorithm
grover_circuit, density_matrix = grover(grover_oracle, grover_diffuser)
grover_circuit.draw("mpl", style = "iqp")
def outcome(winners: list[str], counts: Counts) -> None:
"""Print top measurement(s) (state(s) with highest frequency)
and target state(s) in binary and decimal form, determine
if top measurement(s) equals target state(s), then print result.
Args:
winners (list[str]): State(s) (N-qubit binary string(s))
with highest probability of being measured.
counts (Counts): Each state and its respective frequency.
"""
print("WINNER(S):")
print(f"Binary = {winners}\nDecimal = {[ int(key, 2) for key in winners ]}\n")
print("TARGET(S):")
print(f"Binary = {TARGETS}\nDecimal = {SEARCH_VALUES}\n")
if not all(key in TARGETS for key in winners): print ("Target(s) not found...")
else:
winners_frequency, total = 0, 0
for value, frequency in counts.items():
if value in winners:
winners_frequency += frequency
total += frequency
print(f"Target(s) found with {winners_frequency / total:.2%} accuracy!")
# Simulate Grover's algorithm with Qiskit backend and get results
with BACKEND.open_session() as session:
results = BACKEND.run(transpile(grover_circuit, BACKEND, optimization_level = 2), shots = SHOTS).result()
# Get each state's frequency
counts = results.get_counts()
# Find winner(s) (i.e. state(s) with highest count)
winners = nlargest(len(TARGETS), counts, key = counts.get)
# Print outcome
outcome(winners, counts)
# Display simulation results as histogram
plot_histogram(data = counts, number_to_keep = len(TARGETS))
# City plot of density_matrix
density_matrix.draw("city")
# Bloch sphere representation of density_matrix
# reverse_bits = True since Qiskit uses little-endian for qubit ordering
density_matrix.draw("bloch", reverse_bits = True)
# Hinton plot of density_matrix
density_matrix.draw("hinton")
# Qsphere representation of density_matrix
density_matrix.draw("qsphere")
|
https://github.com/carstenblank/dc-qiskit-algorithms
|
carstenblank
|
# Copyright 2018 Carsten Blank
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import unittest
from typing import List
import numpy as np
import qiskit
from qiskit.providers import JobV1
from qiskit_aer import Aer, StatevectorSimulator, QasmSimulator
from qiskit import QuantumRegister, QuantumCircuit, ClassicalRegister
from ddt import ddt, data as test_data, unpack
from qiskit.result import Result
import dc_qiskit_algorithms.MottonenStatePreparation
logging.basicConfig(format=logging.BASIC_FORMAT, level='INFO')
log = logging.getLogger('test_DraperAdder')
# noinspection NonAsciiCharacters
@ddt
class MottonenStatePrepTests(unittest.TestCase):
def execute_test(self, vector: List[float]):
probability_vector = [np.absolute(e)**2 for e in vector]
qubits = int(np.log2(len(vector)))
reg = QuantumRegister(qubits, "reg")
c = ClassicalRegister(qubits, "c")
qc = QuantumCircuit(reg, c, name='state prep')
qc.state_prep_möttönen(vector, reg)
local_backend: StatevectorSimulator = Aer.get_backend('statevector_simulator')
job: JobV1 = qiskit.execute(qc, backend=local_backend, shots=1)
result: Result = job.result()
# State vector
result_state_vector = result.get_statevector('state prep')
print(["{0:.2f}".format(e) for e in result_state_vector])
# Try to find a global phase
global_phase = set([np.angle(v) - np.angle(rv) for v, rv in zip(vector, result_state_vector)
if abs(v) > 1e-3 and abs(rv) > 1e-3])
global_phase = global_phase.pop() or 0.0
result_state_vector = np.exp(1.0j * global_phase) * result_state_vector
for expected, actual in zip(vector, result_state_vector):
self.assertAlmostEqual(actual.imag, 0.0, places=6)
self.assertAlmostEqual(expected, actual.real, places=6)
# Probability vector from state vector
result_probability_vector = [np.absolute(e)**2 for e in result_state_vector]
print(["{0:.3f}".format(e) for e in result_probability_vector])
for expected, actual in zip(probability_vector, result_probability_vector):
self.assertAlmostEqual(expected, actual, places=2)
# Probability Vector by Measurement
qc.measure(reg, c)
local_qasm_backend: QasmSimulator = Aer.get_backend('qasm_simulator')
shots = 2**12
job: JobV1 = qiskit.execute(qc, backend=local_qasm_backend, shots=shots)
result = job.result() # type: Result
counts = result.get_counts('state prep')
measurement_probability_vector = [0.0 for e in result_state_vector]
for binary, count in sorted(counts.items()):
index = int(binary, 2)
probability = float(count) / float(shots)
print("%s (%d): %.3f" % (binary, index, probability))
measurement_probability_vector[index] = probability
print(["{0:.3f}".format(e) for e in measurement_probability_vector])
for expected, actual in zip(probability_vector, measurement_probability_vector):
self.assertAlmostEqual(expected, actual, delta=0.02)
@unpack
@test_data(
{'vector': [-0.1, 0.2, -0.3, 0.4, -0.5, 0.6, -0.7, 0.8]},
{'vector': [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]},
{'vector': [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]},
{'vector': [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0]},
{'vector': [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0]},
{'vector': [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]},
{'vector': [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0]},
{'vector': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0]},
{'vector': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]}
)
def test_state_preparation(self, vector):
vector = np.asarray(vector)
vector = (1 / np.linalg.norm(vector)) * vector
self.execute_test(list(vector))
def test_instantiation(self):
gate = dc_qiskit_algorithms.MottonenStatePreparationGate([1.0, 0.0])
self.assertIsInstance(gate, dc_qiskit_algorithms.MottonenStatePreparationGate)
if __name__ == '__main__':
unittest.main(verbosity=2)
|
https://github.com/qiskit-community/qiskit-translations-staging
|
qiskit-community
|
# load necessary Runtime libraries
from qiskit_ibm_runtime import QiskitRuntimeService, Sampler, Session
backend = "ibmq_qasm_simulator" # use the simulator
from qiskit.circuit import Parameter
from qiskit.opflow import I, X, Z
mu = Parameter('$\\mu$')
ham_pauli = mu * X
cc = Parameter('$c$')
ww = Parameter('$\\omega$')
ham_res = -(1/2)*ww*(I^Z) + cc*(X^X) + (ham_pauli^I)
tt = Parameter('$t$')
U_ham = (tt*ham_res).exp_i()
from qiskit import transpile
from qiskit.circuit import ClassicalRegister
from qiskit.opflow import PauliTrotterEvolution, Suzuki
import numpy as np
num_trot_steps = 5
total_time = 10
cr = ClassicalRegister(1, 'c')
spec_op = PauliTrotterEvolution(trotter_mode=Suzuki(order=2, reps=num_trot_steps)).convert(U_ham)
spec_circ = spec_op.to_circuit()
spec_circ_t = transpile(spec_circ, basis_gates=['sx', 'rz', 'cx'])
spec_circ_t.add_register(cr)
spec_circ_t.measure(0, cr[0])
spec_circ_t.draw('mpl')
# fixed Parameters
fixed_params = {
cc: 0.3,
mu: 0.7,
tt: total_time
}
# Parameter value for single circuit
param_keys = list(spec_circ_t.parameters)
# run through all the ww values to create a List of Lists of Parameter value
num_pts = 101
wvals = np.linspace(-2, 2, num_pts)
param_vals = []
for wval in wvals:
all_params = {**fixed_params, **{ww: wval}}
param_vals.append([all_params[key] for key in param_keys])
with Session(backend=backend):
sampler = Sampler()
job = sampler.run(
circuits=[spec_circ_t]*num_pts,
parameter_values=param_vals,
shots=1e5
)
result = job.result()
Zexps = []
for dist in result.quasi_dists:
if 1 in dist:
Zexps.append(1 - 2*dist[1])
else:
Zexps.append(1)
from qiskit.opflow import PauliExpectation, Zero
param_bind = {
cc: 0.3,
mu: 0.7,
tt: total_time
}
init_state = Zero^2
obsv = I^Z
Zexp_exact = (U_ham @ init_state).adjoint() @ obsv @ (U_ham @ init_state)
diag_meas_op = PauliExpectation().convert(Zexp_exact)
Zexact_values = []
for w_set in wvals:
param_bind[ww] = w_set
Zexact_values.append(np.real(diag_meas_op.bind_parameters(param_bind).eval()))
import matplotlib.pyplot as plt
plt.style.use('dark_background')
fig, ax = plt.subplots(dpi=100)
ax.plot([-param_bind[mu], -param_bind[mu]], [0, 1], ls='--', color='purple')
ax.plot([param_bind[mu], param_bind[mu]], [0, 1], ls='--', color='purple')
ax.plot(wvals, Zexact_values, label='Exact')
ax.plot(wvals, Zexps, label=f"{backend}")
ax.set_xlabel(r'$\omega$ (arb)')
ax.set_ylabel(r'$\langle Z \rangle$ Expectation')
ax.legend()
import qiskit_ibm_runtime
qiskit_ibm_runtime.version.get_version_info()
from qiskit.tools.jupyter import *
%qiskit_version_table
|
https://github.com/yatharth0610/Quantum-Algorithms-qiskit-
|
yatharth0610
|
from qiskit import *
from qiskit.compiler import *
from qiskit.tools.jupyter import *
from qiskit.visualization import *
import matplotlib.pyplot as plotter
import scipy
import numpy as np
from IPython.display import display, Math, Latex
import qiskit.quantum_info as qi
%matplotlib inline
# Loading your IBM Q account(s)
provider = IBMQ.load_account()
def qft_rotations(circuit, n):
"""Performs qft on the first n qubits in circuit (without swaps)"""
if n == 0:
return circuit
n -= 1
circuit.h(n)
for qubit in range(n):
circuit.cu1(np.pi/2**(n-qubit), qubit, n)
# At the end of our function, we call the same function again on
# the next qubits (we reduced n by one earlier in the function)
qft_rotations(circuit, n)
def swap_registers(circuit, n):
for qubit in range(n//2):
circuit.swap(qubit, n-qubit-1)
return circuit
def qft(circuit, n):
"""QFT on the first n qubits in circuit"""
qft_rotations(circuit, n)
swap_registers(circuit, n)
return circuit
# Let's see how it looks:
qc = QuantumCircuit(4,4)
qft(qc,4)
qc.draw(output = 'mpl')
def inverse_qft(circuit, n):
"""Does the inverse QFT on the first n qubits in circuit"""
# First we create a QFT circuit of the correct size:
qft_circ = qft(QuantumCircuit(n), n)
# Then we take the inverse of this circuit
invqft_circ = qft_circ.inverse()
# And add it to the first n qubits in our existing circuit
circuit.append(invqft_circ, circuit.qubits[:n])
return circuit.decompose() # .decompose() allows us to see the individual gates
qc = inverse_qft(qc,4)
qc.measure(range(4),range(4))
def run_circuit(qc):
backend = Aer.get_backend('qasm_simulator') # we choose the simulator as our backend
result = execute(qc, backend, shots = 10000).result() # we run the simulation
counts = result.get_counts() # we get the counts
return counts
counts = run_circuit(qc)
print(counts)
plot_histogram(counts)
# we should get the intial state '0000'
qc1 = QuantumCircuit(3,3)
qc1.x(0)
qc1.x(1)
qc1.x(2) # try for different initial values
for i in range(4):# choose the number of times you want to do qft)
qft(qc1,3)
qc1.measure(0,0)
qc1.measure(1,1)
qc1.measure(2,2)
def run_circuit(qc1):
backend = Aer.get_backend('qasm_simulator') # we choose the simulator as our backend
result = execute(qc1, backend, shots = 10000).result() # we run the simulation
counts = result.get_counts() # we get the counts
return counts
counts = run_circuit(qc1)
print(counts)
plot_histogram(counts)
qc2 = QuantumCircuit(2)
for i in range(2):
qft(qc2, 2)
matrix = qi.Operator(qc2).data
print(matrix)
def Modulo_increment(qc):
qft(qc,4)
for i in range(4):
qc.u1(np.pi/(2**(3-i)), i)
qft(qc, 4)
return qc
# checking for the case of '1000'
qc2 = QuantumCircuit(4,4)
qc2.x(3)
qc2 = Modulo_increment(qc2)
qc2.measure(range(4),range(4))
# qc2.draw('mpl')
def run_circuit(qc2):
backend = Aer.get_backend('qasm_simulator') # we choose the simulator as our backend
result = execute(qc2, backend, shots = 10000).result() # we run the simulation
counts = result.get_counts() # we get the counts
return counts
counts = run_circuit(qc2)
print(counts)
plot_histogram(counts)
# checking for the case of '1101'
qc3 = QuantumCircuit(4,4)
qc3.x(3)
qc3.x(2)
qc3.x(0)
qc3 = Modulo_increment(qc2)
qc3.measure(range(4),range(4))
def run_circuit(qc3):
backend = Aer.get_backend('qasm_simulator') # we choose the simulator as our backend
result = execute(qc3, backend, shots = 10000).result() # we run the simulation
counts = result.get_counts() # we get the counts
return counts
counts = run_circuit(qc3)
print(counts)
plot_histogram(counts)
# checking for the case of '1111'
qc4 = QuantumCircuit(4,4)
qc4.x(range(4))
qc4 = Modulo_increment(qc4)
qc4.measure(range(4),range(4))
def run_circuit(qc4):
backend = Aer.get_backend('qasm_simulator') # we choose the simulator as our backend
result = execute(qc4, backend, shots = 10000).result() # we run the simulation
counts = result.get_counts() # we get the counts
return counts
counts = run_circuit(qc4)
print(counts)
plot_histogram(counts)
|
https://github.com/qiskit-community/qiskit-translations-staging
|
qiskit-community
|
from qiskit import BasicAer, transpile, QuantumRegister, ClassicalRegister, QuantumCircuit
qr = QuantumRegister(1)
cr = ClassicalRegister(1)
qc = QuantumCircuit(qr, cr)
qc.h(0)
qc.measure(0, 0)
qc.x(0).c_if(cr, 0)
qc.measure(0, 0)
qc.draw('mpl')
|
https://github.com/aryashah2k/Quantum-Computing-Collection-Of-Resources
|
aryashah2k
|
from qiskit import *
q = QuantumRegister(2,"q")
c = ClassicalRegister(2,"c")
qc = QuantumCircuit(q,c)
qc.x(q[0])
qc.measure(q[0],c[0])
qc.h(q[1]).c_if(c,0)
qc.measure(q,c)
print(qc)
job = execute(qc,Aer.get_backend('qasm_simulator'),shots=1024)
counts = job.result().get_counts(qc)
print(counts) # counts is a dictionary
q2 = QuantumRegister(2,"qreg")
c2 = ClassicalRegister(2,"creg")
qc2 = QuantumCircuit(q2,c2)
qc2.x(q2[1])
qc2.z(q2[1])
qc2.measure(q2,c2)
job = execute(qc2,Aer.get_backend('qasm_simulator'),shots=100)
counts = job.result().get_counts(qc2)
print(counts) # counts is a dictionary
from math import pi
q = QuantumRegister(1) # quantum register with a single qubit
c = ClassicalRegister(1) # classical register with a single bit
qc = QuantumCircuit(q,c) # quantum circuit with quantum and classical registers
# rotate the qubit with rotation_angle
qc.ry(2*(pi/3),q[0])
# measure the qubit
qc.measure(q,c)
job = execute(qc,Aer.get_backend('qasm_simulator'),shots=1000)
counts = job.result().get_counts(qc)
print(counts) # counts is a dictionary
q2 = QuantumRegister(1,"qreg")
c2 = ClassicalRegister(1,"creg")
qc2 = QuantumCircuit(q2,c2)
#Your code here1
qc2.x(q2[0])
#Your code here2
qc2.h(q2[0])
qc2.measure(q2,c2)
job = execute(qc2,Aer.get_backend('qasm_simulator'),shots=100)
counts = job.result().get_counts(qc2)
print(counts) # counts is a dictionary
|
https://github.com/rebajram/quantumteleportation
|
rebajram
|
from qiskit import *
import numpy as np
class teleprotocol:
def __init__(self,nq,ne0,ne1,input):
self.n=n
self.input=input
self.qubit = QuantumRegister(nq, "Q")
self.ebit0 = QuantumRegister(ne0, "Alice")
self.ebit1 = QuantumRegister(ne1, "Bob")
self.a = ClassicalRegister(nq, "Alice Q")
self.b = ClassicalRegister(ne0, "Alice A")
self.c = ClassicalRegister(ne1, "Bob B")
self.d = ClassicalRegister(ne1, "Spion")
def Qcirc(self):
qc = QuantumCircuit(self.qubit,self.ebit0,self.ebit1)
for i in range(0, self.n):
if self.input[i]==1:
qc.x(self.qubit[i])
qc.h(self.qubit[i])
if self.input[i]==0:
qc.h(self.qubit[i])
# qc.u(np.pi/2,np.pi/4,np.pi/8,self.qubit[i])
return qc
def Acirc(self):
qc = QuantumCircuit(self.qubit,self.ebit0,self.ebit1)
qc.h(self.ebit0)
qc.cx(self.ebit0, self.ebit1)
qc.barrier()
return qc
def QAcirc(self):
qc = QuantumCircuit(self.qubit,self.ebit0,self.ebit1)
qc.cx(self.qubit,self.ebit0)
qc.h(self.qubit)
qc.barrier()
return qc
def M0circ(self):
qc = QuantumCircuit(self.qubit,self.ebit0,self.ebit1,self.a,self.b,self.c,self.d)
qc.measure(self.ebit0, self.a)
qc.measure(self.qubit, self.b)
qc.barrier()
return qc
def M1circ(self):
qc = QuantumCircuit(self.qubit,self.ebit0,self.ebit1,self.a,self.b,self.c,self.d)
for i in range(0, self.n):
with qc.if_test((self.a[i], 1)):
qc.x(self.ebit1[i])
with qc.if_test((self.b[i], 1)):
qc.z(self.ebit1[i])
qc.barrier()
return qc,self.a,self.b
def SPcirc(self):
qc = QuantumCircuit(self.qubit,self.ebit0,self.ebit1,self.a,self.b,self.c,self.d)
qc.measure(self.ebit1, self.d)
qc.barrier()
return qc,self.d
def Bobmeasure(self):
qc = QuantumCircuit(self.qubit,self.ebit0,self.ebit1,self.a,self.b,self.c,self.d)
qc.h(self.ebit1)
qc.measure(self.ebit1, self.c)
qc.barrier()
return qc,self.c
n=6
input=[1,0,0,1,1,1]
qc=teleprotocol(n,0,0,input).Qcirc()
qc.draw('mpl')
backend = Aer.get_backend('statevector_simulator')
# Create a Quantum Program for execution
job = backend.run(qc)
result = job.result()
qubitstate = result.get_statevector(qc, decimals=3)
qubitstate.draw('latex')
qc=teleprotocol(0,n,n,input).Acirc()
qc.draw('mpl')
backend = Aer.get_backend('statevector_simulator')
# Create a Quantum Program for execution
job = backend.run(qc)
result = job.result()
qcstate = result.get_statevector(qc, decimals=3)
qcstate.draw('latex')
qc1=teleprotocol(n,n,n,input).Qcirc()
qc2=teleprotocol(n,n,n,input).Acirc()
qc3=teleprotocol(n,n,n,input).QAcirc()
qcSpion,c=teleprotocol(n,n,n,input).SPcirc()
qcnew = qc1.compose(qc2)
#qcnew = qcnew.compose(qc3)
#qcnew = qcnew.compose(qcSpion)
qcnew.draw('mpl')
backend = Aer.get_backend('statevector_simulator')
# Create a Quantum Program for execution
job = backend.run(qcnew)
result = job.result()
qcstate = result.get_statevector(qcnew, decimals=3)
qcstate.draw('latex')
qc1=teleprotocol(n,n,n,input).Qcirc()
qc2=teleprotocol(n,n,n,input).Acirc()
qc3=teleprotocol(n,n,n,input).QAcirc()
qc4=teleprotocol(n,n,n,input).M0circ()
qc5,a,b=teleprotocol(n,n,n,input).M1circ()
qcSpion,c=teleprotocol(n,n,n,input).SPcirc()
qc6,d=teleprotocol(n,n,n,input).Bobmeasure()
#qcnew = qc1
qcnew = qc1.compose(qc2)
qcnew = qcnew.compose(qc3)
qcnew = qcnew.compose(qc4)
qcnew.draw('mpl')
job=backend.run(qcnew,shots=1)
result=job.result()
qcstate = result.get_statevector(qcnew, decimals=3)
qcstate.draw('latex')
counts=result.get_counts(qcnew)
print(counts)
qc1=teleprotocol(n,n,n,input).Qcirc()
qc2=teleprotocol(n,n,n,input).Acirc()
qc3=teleprotocol(n,n,n,input).QAcirc()
qc4=teleprotocol(n,n,n,input).M0circ()
qc5,a,b=teleprotocol(n,n,n,input).M1circ()
qcSpion,c=teleprotocol(n,n,n,input).SPcirc()
qc6,d=teleprotocol(n,n,n,input).Bobmeasure()
#qcnew = qc1
qcnew = qc1.compose(qc2)
spion = qcnew.compose(qcSpion)
#qcnew = qcnew.compose(qcSpion)
qcnew = qcnew.compose(qc3)
qcnew = qcnew.compose(qc4)
qcnew = qcnew.compose(qc5)
qcnew.draw('mpl')
job=backend.run(qcnew,shots=1)
result=job.result()
qcstate = result.get_statevector(qcnew, decimals=3)
qcstate.draw('latex')
qc1=teleprotocol(n,n,n,input).Qcirc()
qc2=teleprotocol(n,n,n,input).Acirc()
qc3=teleprotocol(n,n,n,input).QAcirc()
qc4=teleprotocol(n,n,n,input).M0circ()
qc5,a,b=teleprotocol(n,n,n,input).M1circ()
qcSpion,c=teleprotocol(n,n,n,input).SPcirc()
qc6,d=teleprotocol(n,n,n,input).Bobmeasure()
#qcnew = qc1
qcnew = qc1.compose(qc2)
spion = qcnew.compose(qcSpion)
#qcnew = qcnew.compose(qcSpion)
qcnew = qcnew.compose(qc3)
qcnew = qcnew.compose(qc4)
qcnew = qcnew.compose(qc5)
qcnew = qcnew.compose(qc6)
qcnew.draw('mpl')
job=backend.run(qcnew,shots=1)
result=job.result()
qcstate = result.get_statevector(qcnew, decimals=3)
qcstate.draw('latex')
counts=result.get_counts(qcnew)
print(counts)
|
https://github.com/TRSasasusu/qiskit-quantum-zoo
|
TRSasasusu
|
from typing import Optional
from math import gcd
import numpy as np
import matplotlib.pyplot as plt
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister, Aer, transpile, assemble
from qiskit.visualization import plot_histogram
from sympy import Rational
from sympy.ntheory.continued_fraction import continued_fraction, continued_fraction_convergents
from qft import qft
from elementary import ax_modM
from classical_utils import lcm
def order_finding(x: int, N: int, show_hist: Optional[bool] = False) -> int:
#def order_finding(x: int, N: int, epsilon: Optional[float] = 0.2, show_hist: Optional[bool] = False) -> int:
r"""Order-finding algorithm: it finds $r$ of $x^r\equiv 1\pmod N$. It requires
Args:
x (int): $x$
N (int): $N$
Returns:
order $r$
Examples:
```
>>> order_finding(x=3, N=5, show_hist=True)
4
```
and get below image in `img` directory:
It represents $0/2^6=0$, $2^4/2^6=1/4$, $2^5/2^6=1/2$, and $(2^4+2^5)/2^6=3/4$ from the left.
This answer is $r=4$, so $1/2$ looks wrong.
However, $\tilde{r}=2$ is a factor of $r$, so we can get correct $r$ by lcm with another $\tilde{r}$.
"""
L = int(np.ceil(np.log2(N)))
t = 2 * L# + 1 + int(np.ceil(np.log2(3 + 1 / (2 * epsilon)))) # epsilon requires too many qubits to run this program...
first_register = QuantumRegister(t)
second_register = QuantumRegister(2 * t)
auxiliary_register_mid = QuantumRegister(t)
auxiliary_register_end = QuantumRegister(6 * t - 2)
classical_register = ClassicalRegister(len(first_register))
qc = QuantumCircuit(first_register, auxiliary_register_mid, second_register, auxiliary_register_end, classical_register)
# qc.add_register(first_register)
# qc.add_register(second_register)
# qc.add_register(classical_register)
qc.h(first_register)
#import pdb; pdb.set_trace()
#qc.append(ax_modM(a=x, M=N, N_len=len(first_register)), [first_register, auxiliary_register_mid, second_register, auxiliary_register_end])
qc.append(ax_modM(a=x, M=N, N_len=len(first_register)), qc.qubits[:10 * t - 2])
qc.append(qft(n=len(first_register)).inverse(), first_register)
qc.measure(first_register, classical_register)
backend = Aer.get_backend('aer_simulator_matrix_product_state')#('aer_simulator')
qc = transpile(qc, backend)
job = backend.run(qc, shots=10000)
hist = job.result().get_counts()
if show_hist:
plot_histogram(hist)
plt.savefig(f'img/order_finding_x{x}_N{N}.png', bbox_inches='tight')
#plt.savefig(f'img/order_finding_x{x}_N{N}_eps{epsilon}.png', bbox_inches='tight')
all_fractions = []
for measured_key, _ in sorted(hist.items(), key=lambda x: x[1], reverse=True):
tilde_s_per_r = Rational(int(measured_key[-t:], 2), 2 ** t)
if tilde_s_per_r == 0:
continue
fractions = []
for fraction in continued_fraction_convergents(continued_fraction(tilde_s_per_r)):
if pow(x, fraction.denominator, N) == 1:
return fraction.denominator
fractions.append(fraction)
for other_fraction in all_fractions:
if math.gcd(fraction.numerator, other_fraction.numerator) == 1:
r_candidate = lcm(fraction.denominator, other_fraction.denominator)
if pow(x, r_candidate, N) == 1:
return r_candidate
raise Exception('r is NOT found!')
if __name__ == '__main__':
#print(order_finding(x=5, N=21, show_hist=True))
print(order_finding(x=3, N=5, show_hist=True))
|
https://github.com/DRA-chaos/Solutions-to-the-Lab-Exercises---IBM-Qiskit-Summer-School-2021
|
DRA-chaos
|
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(ansatz)
import numpy as np
point = np.random.random(ansatz.num_parameters)
index = 2
from qiskit import Aer
from qiskit.utils import QuantumInstance
backend = Aer.get_backend('qasm_simulator')
q_instance = QuantumInstance(backend, shots = 8192, seed_simulator = 2718, seed_transpiler = 2718)
from qiskit.circuit import QuantumCircuit
from qiskit.opflow import Z, X
H = X ^ X
U = QuantumCircuit(2)
U.h(0)
U.cx(0, 1)
# YOUR CODE HERE
expectation = StateFn(hamiltonian, is_measurement=True) @ StateFn(U)
matmult_result =expectation.eval()
from qc_grader import grade_lab4_ex1
# Note that the grading function is expecting a complex number
grade_lab4_ex1(matmult_result)
from qiskit.opflow import CircuitSampler, PauliExpectation
sampler = CircuitSampler(q_instance)
# YOUR CODE HERE
sampler = CircuitSampler(q_instance) # q_instance is the QuantumInstance from the beginning of the notebook
expectation = StateFn(hamiltonian, is_measurement=True) @ StateFn(U)
in_pauli_basis = PauliExpectation().convert(expectation)
shots_result = sampler.convert(in_pauli_basis).eval()
from qc_grader import grade_lab4_ex2
# Note that the grading function is expecting a complex number
grade_lab4_ex2(shots_result)
from qiskit.opflow import PauliExpectation, CircuitSampler
expectation = StateFn(hamiltonian, is_measurement=True) @ StateFn(ansatz)
in_pauli_basis = PauliExpectation().convert(expectation)
sampler = CircuitSampler(q_instance)
def evaluate_expectation(x):
value_dict = dict(zip(ansatz.parameters, x))
result = sampler.convert(in_pauli_basis, params=value_dict).eval()
return np.real(result)
eps = 0.2
e_i = np.identity(point.size)[:, index] # identity vector with a 1 at index ``index``, otherwise 0
plus = point + eps * e_i
minus = point - eps * e_i
finite_difference = (evaluate_expectation(plus) - evaluate_expectation(minus)) / (2 * eps)
print(finite_difference)
from qiskit.opflow import Gradient
expectation = StateFn(hamiltonian, is_measurement=True) @ StateFn(ansatz)
shifter = Gradient('fin_diff', analytic=False, epsilon=eps)
grad = shifter.convert(expectation, params=ansatz.parameters[index])
print(grad)
value_dict = dict(zip(ansatz.parameters, point))
sampler.convert(grad, value_dict).eval().real
eps = np.pi / 2
e_i = np.identity(point.size)[:, index] # identity vector with a 1 at index ``index``, otherwise 0
plus = point + eps * e_i
minus = point - eps * e_i
finite_difference = (evaluate_expectation(plus) - evaluate_expectation(minus)) / 2
print(finite_difference)
expectation = StateFn(hamiltonian, is_measurement=True) @ StateFn(ansatz)
shifter = Gradient() # parameter-shift rule is the default
grad = shifter.convert(expectation, params=ansatz.parameters[index])
sampler.convert(grad, value_dict).eval().real
expectation = StateFn(hamiltonian, is_measurement=True) @ StateFn(ansatz)
shifter = Gradient('lin_comb') # parameter-shift rule is the default
grad = shifter.convert(expectation, params=ansatz.parameters[index])
sampler.convert(grad, value_dict).eval().real
# initial_point = np.random.random(ansatz.num_parameters)
initial_point = np.array([0.43253681, 0.09507794, 0.42805949, 0.34210341])
expectation = StateFn(hamiltonian, is_measurement=True).compose(StateFn(ansatz))
gradient = Gradient().convert(expectation)
gradient_in_pauli_basis = PauliExpectation().convert(gradient)
sampler = CircuitSampler(q_instance)
def evaluate_gradient(x):
value_dict = dict(zip(ansatz.parameters, x))
result = sampler.convert(gradient_in_pauli_basis, params=value_dict).eval() # add parameters in here!
return np.real(result)
# Note: The GradientDescent class will be released with Qiskit 0.28.0 and can then be imported as:
# from qiskit.algorithms.optimizers import GradientDescent
from qc_grader.gradient_descent import GradientDescent
gd_loss = []
def gd_callback(nfevs, x, fx, stepsize):
gd_loss.append(fx)
gd = GradientDescent(maxiter=300, learning_rate=0.01, callback=gd_callback)
x_opt, fx_opt, nfevs = gd.optimize(initial_point.size, # number of parameters
evaluate_expectation, # function to minimize
gradient_function=evaluate_gradient, # function to evaluate the gradient
initial_point=initial_point) # initial point
import matplotlib
import matplotlib.pyplot as plt
matplotlib.rcParams['font.size'] = 14
plt.figure(figsize=(12, 6))
plt.plot(gd_loss, label='vanilla gradient descent')
plt.axhline(-1, ls='--', c='tab:red', label='target')
plt.ylabel('loss')
plt.xlabel('iterations')
plt.legend();
from qiskit.opflow import NaturalGradient
expectation = StateFn(hamiltonian, is_measurement=True).compose(StateFn(ansatz))
natural_gradient = NaturalGradient(regularization='ridge').convert(expectation)
natural_gradient_in_pauli_basis = PauliExpectation().convert(natural_gradient)
sampler = CircuitSampler(q_instance, caching="all")
def evaluate_natural_gradient(x):
value_dict = dict(zip(ansatz.parameters, x))
result = sampler.convert(natural_gradient, params=value_dict).eval()
return np.real(result)
print('Vanilla gradient:', evaluate_gradient(initial_point))
print('Natural gradient:', evaluate_natural_gradient(initial_point))
qng_loss = []
def qng_callback(nfevs, x, fx, stepsize):
qng_loss.append(fx)
qng = GradientDescent(maxiter=300, learning_rate=0.01, callback=qng_callback)
x_opt, fx_opt, nfevs = qng.optimize(initial_point.size,
evaluate_expectation,
gradient_function=evaluate_natural_gradient,
initial_point=initial_point)
def plot_loss():
plt.figure(figsize=(12, 6))
plt.plot(gd_loss, 'tab:blue', label='vanilla gradient descent')
plt.plot(qng_loss, 'tab:green', label='quantum natural gradient')
plt.axhline(-1, c='tab:red', ls='--', label='target')
plt.ylabel('loss')
plt.xlabel('iterations')
plt.legend()
plot_loss()
from qc_grader.spsa import SPSA
spsa_loss = []
def spsa_callback(nfev, x, fx, stepsize, accepted):
spsa_loss.append(fx)
spsa = SPSA(maxiter=300, learning_rate=0.01, perturbation=0.01, callback=spsa_callback)
x_opt, fx_opt, nfevs = spsa.optimize(initial_point.size,
evaluate_expectation,
initial_point=initial_point)
def plot_loss():
plt.figure(figsize=(12, 6))
plt.plot(gd_loss, 'tab:blue', label='vanilla gradient descent')
plt.plot(qng_loss, 'tab:green', label='quantum natural gradient')
plt.plot(spsa_loss, 'tab:blue', ls='--', label='SPSA')
plt.axhline(-1, c='tab:red', ls='--', label='target')
plt.ylabel('loss')
plt.xlabel('iterations')
plt.legend()
plot_loss()
# Note: The QNSPSA class will be released with Qiskit 0.28.0 and can then be imported as:
# from qiskit.algorithms.optimizers import QNSPSA
from qc_grader.qnspsa import QNSPSA
qnspsa_loss = []
def qnspsa_callback(nfev, x, fx, stepsize, accepted):
qnspsa_loss.append(fx)
fidelity = QNSPSA.get_fidelity(ansatz, q_instance, expectation=PauliExpectation())
qnspsa = QNSPSA(fidelity, maxiter=300, learning_rate=0.01, perturbation=0.01, callback=qnspsa_callback)
x_opt, fx_opt, nfevs = qnspsa.optimize(initial_point.size,
evaluate_expectation,
initial_point=initial_point)
def plot_loss():
plt.figure(figsize=(12, 6))
plt.plot(gd_loss, 'tab:blue', label='vanilla gradient descent')
plt.plot(qng_loss, 'tab:green', label='quantum natural gradient')
plt.plot(spsa_loss, 'tab:blue', ls='--', label='SPSA')
plt.plot(qnspsa_loss, 'tab:green', ls='--', label='QN-SPSA')
plt.axhline(-1, c='tab:red', ls='--', label='target')
plt.ylabel('loss')
plt.xlabel('iterations')
plt.legend()
plot_loss()
autospsa_loss = []
def autospsa_callback(nfev, x, fx, stepsize, accepted):
autospsa_loss.append(fx)
autospsa = SPSA(maxiter=300, learning_rate=None, perturbation=None, callback=autospsa_callback)
x_opt, fx_opt, nfevs = autospsa.optimize(initial_point.size,
evaluate_expectation,
initial_point=initial_point)
def plot_loss():
plt.figure(figsize=(12, 6))
plt.plot(gd_loss, 'tab:blue', label='vanilla gradient descent')
plt.plot(qng_loss, 'tab:green', label='quantum natural gradient')
plt.plot(spsa_loss, 'tab:blue', ls='--', label='SPSA')
plt.plot(qnspsa_loss, 'tab:green', ls='--', label='QN-SPSA')
plt.plot(autospsa_loss, 'tab:red', label='Powerlaw SPSA')
plt.axhline(-1, c='tab:red', ls='--', label='target')
plt.ylabel('loss')
plt.xlabel('iterations')
plt.legend()
plot_loss()
H_tfi = -(Z^Z^I)-(I^Z^Z)-(X^I^I)-(I^X^I)-(I^I^X)
from qc_grader import grade_lab4_ex3
# Note that the grading function is expecting a Hamiltonian
grade_lab4_ex3(H_tfi)
from qiskit.circuit.library import EfficientSU2
efficient_su2 = EfficientSU2(3, entanglement="linear", reps=2)
tfi_sampler = CircuitSampler(q_instance)
def evaluate_tfi(parameters):
exp = StateFn(H_tfi, is_measurement=True).compose(StateFn(efficient_su2))
value_dict = dict(zip(efficient_su2.parameters, parameters))
result = tfi_sampler.convert(PauliExpectation().convert(exp), params=value_dict).eval()
return np.real(result)
# target energy
tfi_target = -3.4939592074349326
# initial point for reproducibility
tfi_init = np.array([0.95667807, 0.06192812, 0.47615196, 0.83809827, 0.89022282,
0.27140831, 0.9540853 , 0.41374024, 0.92595507, 0.76150126,
0.8701938 , 0.05096063, 0.25476016, 0.71807858, 0.85661325,
0.48311132, 0.43623886, 0.6371297 ])
tfi_result = SPSA(maxiter=300, learning_rate=None, perturbation=None)
tfi_result = tfi_result.optimize(tfi_init.size, evaluate_tfi, initial_point=tfi_init)
tfi_minimum = tfi_result[1]
print("Error:", np.abs(tfi_result[1] - tfi_target))
from qc_grader import grade_lab4_ex4
# Note that the grading function is expecting a floating point number
grade_lab4_ex4(tfi_minimum)
from qiskit_machine_learning.datasets import ad_hoc_data
training_features, training_labels, test_features, test_labels = ad_hoc_data(
training_size=20, test_size=10, n=2, one_hot=False, gap=0.5
)
# the training labels are in {0, 1} but we'll use {-1, 1} as class labels!
training_labels = 2 * training_labels - 1
test_labels = 2 * test_labels - 1
def plot_sampled_data():
from matplotlib.patches import Patch
from matplotlib.lines import Line2D
import matplotlib.pyplot as plt
plt.figure(figsize=(12,6))
for feature, label in zip(training_features, training_labels):
marker = 'o'
color = 'tab:green' if label == -1 else 'tab:blue'
plt.scatter(feature[0], feature[1], marker=marker, s=100, color=color)
for feature, label in zip(test_features, test_labels):
marker = 's'
plt.scatter(feature[0], feature[1], marker=marker, s=100, facecolor='none', edgecolor='k')
legend_elements = [
Line2D([0], [0], marker='o', c='w', mfc='tab:green', label='A', ms=15),
Line2D([0], [0], marker='o', c='w', mfc='tab:blue', label='B', ms=15),
Line2D([0], [0], marker='s', c='w', mfc='none', mec='k', label='test features', ms=10)
]
plt.legend(handles=legend_elements, bbox_to_anchor=(1, 0.6))
plt.title('Training & test data')
plt.xlabel('$x$')
plt.ylabel('$y$')
plot_sampled_data()
from qiskit.circuit.library import ZZFeatureMap
dim = 2
feature_map = ZZFeatureMap(dim, reps=1) # let's keep it simple!
feature_map.draw('mpl', style='iqx')
ansatz = RealAmplitudes(num_qubits=dim, entanglement='linear', reps=1) # also simple here!
ansatz.draw('mpl', style='iqx')
circuit = feature_map.compose(ansatz)
circuit.draw('mpl', style='iqx')
hamiltonian = Z ^ Z # global Z operators
gd_qnn_loss = []
def gd_qnn_callback(*args):
gd_qnn_loss.append(args[2])
gd = GradientDescent(maxiter=100, learning_rate=0.01, callback=gd_qnn_callback)
from qiskit_machine_learning.neural_networks import OpflowQNN
qnn_expectation = StateFn(hamiltonian, is_measurement=True) @ StateFn(circuit)
qnn = OpflowQNN(qnn_expectation,
input_params=list(feature_map.parameters),
weight_params=list(ansatz.parameters),
exp_val=PauliExpectation(),
gradient=Gradient(), # <-- Parameter-Shift gradients
quantum_instance=q_instance)
from qiskit_machine_learning.algorithms import NeuralNetworkClassifier
#initial_point = np.array([0.2, 0.1, 0.3, 0.4])
classifier = NeuralNetworkClassifier(qnn, optimizer=gd)
classifier.fit(training_features, training_labels);
predicted = classifier.predict(test_features)
def plot_predicted():
from matplotlib.lines import Line2D
plt.figure(figsize=(12, 6))
for feature, label in zip(training_features, training_labels):
marker = 'o'
color = 'tab:green' if label == -1 else 'tab:blue'
plt.scatter(feature[0], feature[1], marker=marker, s=100, color=color)
for feature, label, pred in zip(test_features, test_labels, predicted):
marker = 's'
color = 'tab:green' if pred == -1 else 'tab:blue'
if label != pred: # mark wrongly classified
plt.scatter(feature[0], feature[1], marker='o', s=500, linewidths=2.5,
facecolor='none', edgecolor='tab:red')
plt.scatter(feature[0], feature[1], marker=marker, s=100, color=color)
legend_elements = [
Line2D([0], [0], marker='o', c='w', mfc='tab:green', label='A', ms=15),
Line2D([0], [0], marker='o', c='w', mfc='tab:blue', label='B', ms=15),
Line2D([0], [0], marker='s', c='w', mfc='tab:green', label='predict A', ms=10),
Line2D([0], [0], marker='s', c='w', mfc='tab:blue', label='predict B', ms=10),
Line2D([0], [0], marker='o', c='w', mfc='none', mec='tab:red', label='wrongly classified', mew=2, ms=15)
]
plt.legend(handles=legend_elements, bbox_to_anchor=(1, 0.7))
plt.title('Training & test data')
plt.xlabel('$x$')
plt.ylabel('$y$')
plot_predicted()
qng_qnn_loss = []
def qng_qnn_callback(*args):
qng_qnn_loss.append(args[2])
gd = GradientDescent(maxiter=100, learning_rate=0.01, callback=qng_qnn_callback)
qnn = OpflowQNN(qnn_expectation,
input_params=list(feature_map.parameters),
weight_params=list(ansatz.parameters),
gradient=NaturalGradient(regularization='ridge'), # <-- using Natural Gradients!
quantum_instance=q_instance)
classifier = NeuralNetworkClassifier(qnn, optimizer=gd)#, initial_point=initial_point)
classifier.fit(training_features, training_labels);
def plot_losses():
plt.figure(figsize=(12, 6))
plt.plot(gd_qnn_loss, 'tab:blue', marker='o', label='vanilla gradients')
plt.plot(qng_qnn_loss, 'tab:green', marker='o', label='natural gradients')
plt.xlabel('iterations')
plt.ylabel('loss')
plt.legend(loc='best')
plot_losses()
from qiskit.opflow import I
def sample_gradients(num_qubits, reps, local=False):
"""Sample the gradient of our model for ``num_qubits`` qubits and ``reps`` repetitions.
We sample 100 times for random parameters and compute the gradient of the first RY rotation gate.
"""
index = num_qubits - 1
# you can also exchange this for a local operator and observe the same!
if local:
operator = Z ^ Z ^ (I ^ (num_qubits - 2))
else:
operator = Z ^ num_qubits
# real amplitudes ansatz
ansatz = RealAmplitudes(num_qubits, entanglement='linear', reps=reps)
# construct Gradient we want to evaluate for different values
expectation = StateFn(operator, is_measurement=True).compose(StateFn(ansatz))
grad = Gradient().convert(expectation, params=ansatz.parameters[index])
# evaluate for 100 different, random parameter values
num_points = 100
grads = []
for _ in range(num_points):
# points are uniformly chosen from [0, pi]
point = np.random.uniform(0, np.pi, ansatz.num_parameters)
value_dict = dict(zip(ansatz.parameters, point))
grads.append(sampler.convert(grad, value_dict).eval())
return grads
num_qubits = list(range(2, 13))
reps = num_qubits # number of layers = numbers of qubits
gradients = [sample_gradients(n, r) for n, r in zip(num_qubits, reps)]
fit = np.polyfit(num_qubits, np.log(np.var(gradients, axis=1)), deg=1)
x = np.linspace(num_qubits[0], num_qubits[-1], 200)
plt.figure(figsize=(12, 6))
plt.semilogy(num_qubits, np.var(gradients, axis=1), 'o-', label='measured variance')
plt.semilogy(x, np.exp(fit[0] * x + fit[1]), 'r--', label=f'exponential fit w/ {fit[0]:.2f}')
plt.xlabel('number of qubits')
plt.ylabel(r'$\mathrm{Var}[\partial_{\theta 1} \langle E(\theta) \rangle]$')
plt.legend(loc='best');
from qiskit.opflow import NaturalGradient
def sample_natural_gradients(num_qubits, reps):
index = num_qubits - 1
operator = Z ^ num_qubits
ansatz = RealAmplitudes(num_qubits, entanglement='linear', reps=reps)
expectation = StateFn(operator, is_measurement=True).compose(StateFn(ansatz))
grad = # TODO: ``grad`` should be the natural gradient for the parameter at index ``index``.
# Hint: Check the ``sample_gradients`` function, this one is almost the same.
grad = NaturalGradient().convert(expectation, params=ansatz.parameters[index])
num_points = 100
grads = []
for _ in range(num_points):
point = np.random.uniform(0, np.pi, ansatz.num_parameters)
value_dict = dict(zip(ansatz.parameters, point))
grads.append(sampler.convert(grad, value_dict).eval())
return grads
num_qubits = list(range(2, 13))
reps = num_qubits # number of layers = numbers of qubits
natural_gradients = [sample_natural_gradients(n, r) for n, r in zip(num_qubits, reps)]
fit = np.polyfit(num_qubits, np.log(np.var(natural_gradients, axis=1)), deg=1)
x = np.linspace(num_qubits[0], num_qubits[-1], 200)
plt.figure(figsize=(12, 6))
plt.semilogy(num_qubits, np.var(gradients, axis=1), 'o-', label='vanilla gradients')
plt.semilogy(num_qubits, np.var(natural_gradients, axis=1), 's-', label='natural gradients')
plt.semilogy(x, np.exp(fit[0] * x + fit[1]), 'r--', label=f'exponential fit w/ {float(fit[0]):.2f}')
plt.xlabel('number of qubits')
plt.ylabel(r'$\mathrm{Var}[\partial_{\theta 1} \langle E(\theta) \rangle]$')
plt.legend(loc='best');
num_qubits = list(range(2, 13))
fixed_depth_global_gradients = [sample_gradients(n, 1) for n in num_qubits]
fit = np.polyfit(num_qubits, np.log(np.var(fixed_depth_global_gradients, axis=1)), deg=1)
x = np.linspace(num_qubits[0], num_qubits[-1], 200)
plt.figure(figsize=(12, 6))
plt.semilogy(num_qubits, np.var(gradients, axis=1), 'o-', label='global cost, linear depth')
plt.semilogy(num_qubits, np.var(fixed_depth_global_gradients, axis=1), 'o-', label='global cost, constant depth')
plt.semilogy(x, np.exp(fit[0] * x + fit[1]), 'r--', label=f'exponential fit w/ {fit[0]:.2f}')
plt.xlabel('number of qubits')
plt.ylabel(r'$\mathrm{Var}[\partial_{\theta 1} \langle E(\theta) \rangle]$')
plt.legend(loc='best');
num_qubits = list(range(2, 13))
linear_depth_local_gradients = [sample_gradients(n, n, local=True) for n in num_qubits]
fit = np.polyfit(num_qubits, np.log(np.var(linear_depth_local_gradients, axis=1)), deg=1)
x = np.linspace(num_qubits[0], num_qubits[-1], 200)
plt.figure(figsize=(12, 6))
plt.semilogy(num_qubits, np.var(gradients, axis=1), 'o-', label='global cost, linear depth')
plt.semilogy(num_qubits, np.var(fixed_depth_global_gradients, axis=1), 'o-', label='global cost, constant depth')
plt.semilogy(num_qubits, np.var(linear_depth_local_gradients, axis=1), 'o-', label='local cost, linear depth')
plt.semilogy(x, np.exp(fit[0] * x + fit[1]), 'r--', label=f'exponential fit w/ {fit[0]:.2f}')
plt.xlabel('number of qubits')
plt.ylabel(r'$\mathrm{Var}[\partial_{\theta 1} \langle E(\theta) \rangle]$')
plt.legend(loc='best');
num_qubits = list(range(2, 13))
fixed_depth_local_gradients = [sample_gradients(n, 1, local=True) for n in num_qubits]
fit = np.polyfit(num_qubits, np.log(np.var(fixed_depth_local_gradients, axis=1)), deg=1)
x = np.linspace(num_qubits[0], num_qubits[-1], 200)
plt.figure(figsize=(12, 6))
plt.semilogy(num_qubits, np.var(gradients, axis=1), 'o-', label='global cost, linear depth')
plt.semilogy(num_qubits, np.var(fixed_depth_global_gradients, axis=1), 'o-', label='global cost, constant depth')
plt.semilogy(num_qubits, np.var(linear_depth_local_gradients, axis=1), 'o-', label='local cost, linear depth')
plt.semilogy(num_qubits, np.var(fixed_depth_local_gradients, axis=1), 'o-', label='local cost, constant depth')
plt.semilogy(x, np.exp(fit[0] * x + fit[1]), 'r--', label=f'exponential fit w/ {fit[0]:.2f}')
plt.xlabel('number of qubits')
plt.ylabel(r'$\mathrm{Var}[\partial_{\theta 1} \langle E(\theta) \rangle]$')
plt.legend(loc='best');
num_qubits = 6
operator = Z ^ Z ^ (I ^ (num_qubits - 4))
def minimize(circuit, optimizer):
initial_point = np.random.random(circuit.num_parameters)
exp = StateFn(operator, is_measurement=True) @ StateFn(circuit)
grad = Gradient().convert(exp)
# pauli basis
exp = PauliExpectation().convert(exp)
grad = PauliExpectation().convert(grad)
sampler = CircuitSampler(q_instance, caching="all")
def loss(x):
values_dict = dict(zip(circuit.parameters, x))
return np.real(sampler.convert(exp, values_dict).eval())
def gradient(x):
values_dict = dict(zip(circuit.parameters, x))
return np.real(sampler.convert(grad, values_dict).eval())
return optimizer.optimize(circuit.num_parameters, loss, gradient, initial_point=initial_point)
circuit = RealAmplitudes(4, reps=1, entanglement='linear')
circuit.draw('mpl', style='iqx')
circuit.reps = 5
circuit.draw('mpl', style='iqx')
def layerwise_training(ansatz, max_num_layers, optimizer):
optimal_parameters = []
fopt = None
for reps in range(1, max_num_layers):
ansatz.reps = reps
# bind already optimized parameters
values_dict = dict(zip(ansatz.parameters, optimal_parameters))
partially_bound = ansatz.bind_parameters(values_dict)
xopt, fopt, _ = minimize(partially_bound, optimizer)
print('Circuit depth:', ansatz.depth(), 'best value:', fopt)
optimal_parameters += list(xopt)
return fopt, optimal_parameters
ansatz = RealAmplitudes(4, entanglement='linear')
optimizer = GradientDescent(maxiter=50)
np.random.seed(12)
fopt, optimal_parameters = layerwise_training(ansatz, 4, optimizer)
|
https://github.com/jacobwatkins1/rodeo-algorithm
|
jacobwatkins1
|
import numpy as np
from sympy import *
from qiskit import *
from qiskit.quantum_info import Kraus, SuperOp
from qiskit.providers.aer import AerSimulator
from qiskit.tools.visualization import plot_histogram
# Import from Qiskit Aer noise module
from qiskit.providers.aer.noise import NoiseModel
from qiskit.providers.aer.noise import QuantumError, ReadoutError
from qiskit.providers.aer.noise import pauli_error
from qiskit.providers.aer.noise import depolarizing_error
from qiskit.providers.aer.noise import thermal_relaxation_error
from numpy import linalg as la
Id = np.matrix([[1,0],[0,1]],dtype=complex)
X = np.matrix([[0,1],[1,0]],dtype=complex)
Y = np.matrix([[0,-1j],[1j,0]],dtype=complex)
Z = np.matrix([[1,0],[0,-1]],dtype=complex)
# The values used in our paper.
a= -0.08496099317164885
b= -0.8913449722923625
c= 0.2653567873681286
d= 0.5720506205204723
# The values used in our paper.
c_i= -0.8453688208879107
c_x= 0.006726766902514836
c_y= -0.29354208134440296
c_z= 0.18477436355081123
# The Hamiltonian.
H = a*Id+b*X+c*Y+d*Z
print(H)
# Find the exact eigenstates and eigenvalues.
Awh, Avh = la.eig(H)
Aeval0h = float(re(Awh[0]))
Aeval1h = float(re(Awh[1]))
Aevec0h = Avh[:,0]
Aevec1h = Avh[:,1]
print('The energy eigenvalues of H are:',Aeval0h, 'and', Aeval1h)
print('The corresponding eigenvectors are:', '\n', Aevec0h,'\n', 'and','\n', Aevec1h)
N = 3
Aevec0h1 = [0.8729044694074841+0j, -0.4676094626792474+0.13920911500783384j]
def qc1(E_targ,tstep,phi):
ao = a + phi*c_i
bo = b + phi*c_x
co = c + phi*c_y
do = d + phi*c_z
sigo = Matrix([[bo],[co],[do]])
n_hato = np.dot(1/sqrt(bo**2+co**2+do**2),sigo)
nxo, nyo, nzo = [float(i.item()) for i in n_hato]
theta = 2*tstep*sqrt(bo**2+co**2+do**2) % (4*pi)
delta = float(re(atan(nzo*tan(theta/2))+atan(nxo/nyo)))
beta = float(re(atan(nzo*tan(theta/2))-atan(nxo/nyo)))
gamma = float(2*re(acos(cos(theta/2)/cos((delta+beta)/2))))
if nyo > 0 and theta > 2*pi:
gamma *= (-1)
elif nyo < 0 and theta <= 2*pi:
gamma *= (-1)
quanc1t = QuantumCircuit(2)
quanc1t.h(1)
quanc1t.cu(gamma,beta,delta,0,1,0)
quanc1t.p(tstep*(E_targ-ao)-0.5*(delta+beta),1) # The Energy Sensor.
quanc1t.h(1)
quanc1t.barrier()
return quanc1t
def qct(E_targ,sigma,phi):
qrz = QuantumRegister(2,'q')
crz = ClassicalRegister(N,'c')
qc = QuantumCircuit(qrz,crz)
t = np.random.normal(0, sigma, N).tolist()
qc.initialize(Aevec0h1, 0)
for i in range (N):
# sub_inst = qc1(E_targ,t[i],epsilon).to_instruction()
# qc.append(sub_inst,[obj,arena])
qc.append(qc1(E_targ,t[i],phi),[0,1]) # 0:Obj, 1:Arena.
qc.measure(1,i) # Mid-circuit measurement, arena is measured.
qc.barrier()
# qc = transpile(qc,backend=backend,optimization_level=2,initial_layout = [obj,arena])
# qc = transpile(qc,backend=backend,optimization_level=2,layout_method = "noise_adaptive")
# qc.barrier()
return qc
def depolarizing_noise(one_qubit_noise, two_qubit_noise):
depolarizing_model = NoiseModel()
error_1 = depolarizing_error(one_qubit_noise, 1)
error_2 = depolarizing_error(two_qubit_noise, 2)
depolarizing_model.add_all_qubit_quantum_error(error_1, ['u1', 'u2', 'u3'])
depolarizing_model.add_all_qubit_quantum_error(error_2, ['cx'])
print(depolarizing_model)
return depolarizing_model
test_circuit = qct(Aeval0h,3,0)
test_circuit.draw('mpl')
# Create noisy simulator backend
sim_noise = AerSimulator(noise_model = depolarizing_noise(0.9,0.9))
# Transpile circuit for noisy basis gates
circ_tnoise = transpile(test_circuit, sim_noise)
# Run and get counts
result_bit_flip = sim_noise.run(circ_tnoise).result()
counts_bit_flip = result_bit_flip.get_counts(0)
# Plot noisy output
plot_histogram(counts_bit_flip)
def numsli_expc (L,rang1,rang2,nuni,nshots,sigma,k,phi):
val0, val1,_,_,_,_ = H_obj(phi)
EW = np.linspace(rang1,rang2,L) # The energy spectrum we're interested in.
circuits = [] # The 'circuit matrix'.
for i in range(L):
circuitsi = []
for j in range (nuni):
circuit = qct(EW[i],sigma,phi)
circuitsi.append(circuit)
circuits.append(circuitsi)
all_circuits = []
for i in range(L):
all_circuits += circuits[i][:]
MExperiments = job_manager.run(all_circuits, backend=backend, shots = nshots)
results = MExperiments.results()
# cresults = results.combine_results() # We are not doing read-out error mitigation here.
# mitigated_results = meas_filter.apply(cresults)
probsu = []
succs = '0'*N
for i in range (L):
probsui = []
for j in range (nuni):
# counts = mitigated_results.get_counts((nuni*i)+j)
counts = results.get_counts((nuni*i)+j)
if succs in counts:
prob = counts[succs]/sum(counts.values())
else:
prob = 0
probsui.append(prob)
probsu.append(np.average(probsui))
spec = dict(zip(EW,probsu))
plt.figure(figsize=(16.18,10))
plt.xlabel(r'$Energy\ Spectrum\ (E_{target})$',fontsize = 15)
plt.ylabel('Probability to measure the success state',fontsize = 15)
hist = plt.bar(spec.keys(), spec.values(), width=np.abs((rang2-rang1)/L), color='lightseagreen',alpha = 0.7)
plt.legend([hist],['Results from qasm'])
plt.grid()
plt.show()
# Weighted avg based on the highest bins.
#Larg = dict(sorted(spec.items(), key = itemgetter(1), reverse = True)[:Hisk])
#Wavg = np.average(list(Larg.keys()), weights=list(Larg.values()))
# Weighted avg of the highest consecutive bins.
keys = np.fromiter(spec.keys(), float, count=len(spec))
vals = np.fromiter(spec.values(), float, count=len(spec))
idx = np.convolve(vals, np.ones(k), 'valid').argmax()
Wavg = np.average(keys[idx:idx + k], weights=vals[idx:idx + k])
print('Range for the next scan', keys[idx:idx + k])
print('The estimated Eigenvalue is', Wavg)
print('The precise values are',val0,'and',val1)
return spec
|
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(feature_range=(0, np.pi)).fit_transform(features)
train_features, test_features, train_labels, test_labels = train_test_split(
features, labels, train_size=15, shuffle=False
)
# number of qubits is equal to the number of features
num_qubits = 2
# number of steps performed during the training procedure
tau = 100
# regularization parameter
C = 1000
from qiskit import BasicAer
from qiskit.circuit.library import ZFeatureMap
from qiskit.utils import algorithm_globals
from qiskit_machine_learning.kernels import FidelityQuantumKernel
algorithm_globals.random_seed = 12345
feature_map = ZFeatureMap(feature_dimension=num_qubits, reps=1)
qkernel = FidelityQuantumKernel(feature_map=feature_map)
from qiskit_machine_learning.algorithms import PegasosQSVC
pegasos_qsvc = PegasosQSVC(quantum_kernel=qkernel, C=C, num_steps=tau)
# training
pegasos_qsvc.fit(train_features, train_labels)
# testing
pegasos_score = pegasos_qsvc.score(test_features, test_labels)
print(f"PegasosQSVC classification test score: {pegasos_score}")
grid_step = 0.2
margin = 0.2
grid_x, grid_y = np.meshgrid(
np.arange(-margin, np.pi + margin, grid_step), np.arange(-margin, np.pi + margin, grid_step)
)
meshgrid_features = np.column_stack((grid_x.ravel(), grid_y.ravel()))
meshgrid_colors = pegasos_qsvc.predict(meshgrid_features)
import matplotlib.pyplot as plt
plt.figure(figsize=(5, 5))
meshgrid_colors = meshgrid_colors.reshape(grid_x.shape)
plt.pcolormesh(grid_x, grid_y, meshgrid_colors, cmap="RdBu", shading="auto")
plt.scatter(
train_features[:, 0][train_labels == 0],
train_features[:, 1][train_labels == 0],
marker="s",
facecolors="w",
edgecolors="r",
label="A train",
)
plt.scatter(
train_features[:, 0][train_labels == 1],
train_features[:, 1][train_labels == 1],
marker="o",
facecolors="w",
edgecolors="b",
label="B train",
)
plt.scatter(
test_features[:, 0][test_labels == 0],
test_features[:, 1][test_labels == 0],
marker="s",
facecolors="r",
edgecolors="r",
label="A test",
)
plt.scatter(
test_features[:, 0][test_labels == 1],
test_features[:, 1][test_labels == 1],
marker="o",
facecolors="b",
edgecolors="b",
label="B test",
)
plt.legend(bbox_to_anchor=(1.05, 1), loc="upper left", borderaxespad=0.0)
plt.title("Pegasos Classification")
plt.show()
import qiskit.tools.jupyter
%qiskit_version_table
%qiskit_copyright
|
https://github.com/abhishekchak52/quantum-computing-course
|
abhishekchak52
|
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
# Importing standard Qiskit libraries
from qiskit import QuantumCircuit, execute
from qiskit.providers.aer import QasmSimulator
from qiskit.visualization import *
num_qubits = 32
alice_basis = np.random.randint(2, size=num_qubits)
alice_state = np.random.randint(2, size=num_qubits)
bob_basis = np.random.randint(2, size=num_qubits)
oscar_basis = np.random.randint(2, size=num_qubits)
print(f"Alice's State:\t {np.array2string(alice_state, separator='')}")
print(f"Alice's Bases:\t {np.array2string(alice_basis, separator='')}")
print(f"Oscar's Bases:\t {np.array2string(oscar_basis, separator='')}")
print(f"Bob's Bases:\t {np.array2string(bob_basis, separator='')}")
def make_bb84_circ(enc_state, enc_basis, meas_basis):
'''
enc_state: array of 0s and 1s denoting the state to be encoded
enc_basis: array of 0s and 1s denoting the basis to be used for encoding
0 -> Computational Basis
1 -> Hadamard Basis
meas_basis: array of 0s and 1s denoting the basis to be used for measurement
0 -> Computational Basis
1 -> Hadamard Basis
'''
num_qubits = len(enc_state)
bb84_circ = QuantumCircuit(num_qubits)
# Sender prepares qubits
for index in range(len(enc_basis)):
if enc_state[index] == 1:
bb84_circ.x(index)
if enc_basis[index] == 1:
bb84_circ.h(index)
bb84_circ.barrier()
# Receiver measures the received qubits
for index in range(len(meas_basis)):
if meas_basis[index] == 1:
bb84_circ.h(index)
bb84_circ.measure_all()
return bb84_circ
bb84_AO = make_bb84_circ(alice_state, alice_basis, oscar_basis)
oscar_result = execute(bb84_AO.reverse_bits(),
backend=QasmSimulator(),
shots=1).result().get_counts().most_frequent()
print(f"Oscar's results:\t {oscar_result}")
# Converting string to array
oscar_state = np.array(list(oscar_result), dtype=int)
print(f"Oscar's State:\t\t{np.array2string(oscar_state, separator='')}")
bb84_OB = make_bb84_circ(oscar_state, oscar_basis, bob_basis)
temp_key = execute(bb84_OB.reverse_bits(),
backend=QasmSimulator(),
shots=1).result().get_counts().most_frequent()
print(f"Bob's results:\t\t {temp_key}")
alice_key = ''
bob_key = ''
oscar_key = ''
for i in range(num_qubits):
if alice_basis[i] == bob_basis[i]: # Only choose bits where Alice and Bob chose the same basis
alice_key += str(alice_state[i])
bob_key += str(temp_key[i])
oscar_key += str(oscar_result[i])
print(f"The length of the key is {len(bob_key)}")
print(f"Alice's key contains\t {(alice_key).count('0')} zeroes and {(alice_key).count('1')} ones")
print(f"Bob's key contains\t {(bob_key).count('0')} zeroes and {(bob_key).count('1')} ones")
print(f"Oscar's key contains\t {(oscar_key).count('0')} zeroes and {(oscar_key).count('1')} ones")
print(f"Alice's Key:\t {alice_key}")
print(f"Bob's Key:\t {bob_key}")
print(f"Oscar's Key:\t {oscar_key}")
|
https://github.com/idriss-hamadi/Qiskit-Quantaum-codes-
|
idriss-hamadi
|
from qiskit import * #qiskit framework
# Quantum_Register=QuantumRegister(2)
# classical_Register=ClassicalRegister(2)
# circut=QuantumCircuit(Quantum_Register,classical_Register) #those 3 lines are the same as the next line
circut=QuantumCircuit(2,2)
circut.draw(output='mpl') #drawing function
circut.h(0) #handmard function
circut.cx(0,1) #Cnot function
circut.measure([0,1],[0,1]) #measuring between the qubits and calssical bits
circut.draw(output='mpl') #drawing
simulator=Aer.get_backend('qasm_simulator') #using simulator of quantaum comuter called qasm
resault=execute(circut,backend=simulator).result() #storing the resault of computation
from qiskit.visualization import plot_histogram #importing function of ploting histogram
plot_histogram(resault.get_counts()) #plot
from qiskit import *
# IBMQ.save_account(open("token.txt","r").read()) #signing in using my token in my txt file
IBMQ.load_account() #lading account
provider=IBMQ.get_provider("ibm-q")
quantaum_computer=provider.get_backend('ibmq_quito')
import qiskit.tools.jupyter
%qiskit_job_watcher
job =execute(circut,backend=quantaum_computer)
from qiskit.tools.monitor import job_monitor
job_monitor(job)
quantaum_resault=job.result()
plot_histogram(quantaum_resault.get_counts(circut))
%qiskit_disable_job_watcher
|
https://github.com/qiskit-community/qiskit-translations-staging
|
qiskit-community
|
import numpy as np
from qiskit import *
from qiskit.circuit import Gate
my_gate = Gate(name='my_gate', num_qubits=2, params=[])
qr = QuantumRegister(3, 'q')
circ = QuantumCircuit(qr)
circ.append(my_gate, [qr[0], qr[1]])
circ.append(my_gate, [qr[1], qr[2]])
circ.draw()
# Build a sub-circuit
sub_q = QuantumRegister(2)
sub_circ = QuantumCircuit(sub_q, name='sub_circ')
sub_circ.h(sub_q[0])
sub_circ.crz(1, sub_q[0], sub_q[1])
sub_circ.barrier()
sub_circ.id(sub_q[1])
sub_circ.u(1, 2, -2, sub_q[0])
# Convert to a gate and stick it into an arbitrary place in the bigger circuit
sub_inst = sub_circ.to_instruction()
qr = QuantumRegister(3, 'q')
circ = QuantumCircuit(qr)
circ.h(qr[0])
circ.cx(qr[0], qr[1])
circ.cx(qr[1], qr[2])
circ.append(sub_inst, [qr[1], qr[2]])
circ.draw()
decomposed_circ = circ.decompose() # Does not modify original circuit
decomposed_circ.draw()
from qiskit.circuit import Parameter
theta = Parameter('θ')
n = 5
qc = QuantumCircuit(5, 1)
qc.h(0)
for i in range(n-1):
qc.cx(i, i+1)
qc.barrier()
qc.rz(theta, range(5))
qc.barrier()
for i in reversed(range(n-1)):
qc.cx(i, i+1)
qc.h(0)
qc.measure(0, 0)
qc.draw('mpl')
print(qc.parameters)
import numpy as np
theta_range = np.linspace(0, 2 * np.pi, 128)
circuits = [qc.bind_parameters({theta: theta_val})
for theta_val in theta_range]
circuits[-1].draw()
backend = BasicAer.get_backend('qasm_simulator')
job = backend.run(transpile(circuits, backend))
counts = job.result().get_counts()
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)
ax.plot(theta_range, list(map(lambda c: c.get('0', 0), counts)), '.-', label='0')
ax.plot(theta_range, list(map(lambda c: c.get('1', 0), counts)), '.-', label='1')
ax.set_xticks([i * np.pi / 2 for i in range(5)])
ax.set_xticklabels(['0', r'$\frac{\pi}{2}$', r'$\pi$', r'$\frac{3\pi}{2}$', r'$2\pi$'], fontsize=14)
ax.set_xlabel('θ', fontsize=14)
ax.set_ylabel('Counts', fontsize=14)
ax.legend(fontsize=14)
import time
from itertools import combinations
from qiskit.compiler import assemble
from qiskit.test.mock import FakeVigo
start = time.time()
qcs = []
theta_range = np.linspace(0, 2*np.pi, 32)
for n in theta_range:
qc = QuantumCircuit(5)
for k in range(8):
for i,j in combinations(range(5), 2):
qc.cx(i,j)
qc.rz(n, range(5))
for i,j in combinations(range(5), 2):
qc.cx(i,j)
qcs.append(qc)
compiled_circuits = transpile(qcs, backend=FakeVigo())
qobj = assemble(compiled_circuits, backend=FakeVigo())
end = time.time()
print('Time compiling over set of bound circuits: ', end-start)
start = time.time()
qc = QuantumCircuit(5)
theta = Parameter('theta')
for k in range(8):
for i,j in combinations(range(5), 2):
qc.cx(i,j)
qc.rz(theta, range(5))
for i,j in combinations(range(5), 2):
qc.cx(i,j)
transpiled_qc = transpile(qc, backend=FakeVigo())
qobj = assemble([transpiled_qc.bind_parameters({theta: n})
for n in theta_range], backend=FakeVigo())
end = time.time()
print('Time compiling over parameterized circuit, then binding: ', end-start)
phi = Parameter('phi')
sub_circ1 = QuantumCircuit(2, name='sc_1')
sub_circ1.rz(phi, 0)
sub_circ1.rx(phi, 1)
sub_circ2 = QuantumCircuit(2, name='sc_2')
sub_circ2.rx(phi, 0)
sub_circ2.rz(phi, 1)
qc = QuantumCircuit(4)
qr = qc.qregs[0]
qc.append(sub_circ1.to_instruction(), [qr[0], qr[1]])
qc.append(sub_circ2.to_instruction(), [qr[0], qr[1]])
qc.append(sub_circ2.to_instruction(), [qr[2], qr[3]])
print(qc.draw())
# The following raises an error: "QiskitError: 'Name conflict on adding parameter: phi'"
# phi2 = Parameter('phi')
# qc.u3(0.1, phi2, 0.3, 0)
p = Parameter('p')
qc = QuantumCircuit(3, name='oracle')
qc.rz(p, 0)
qc.cx(0, 1)
qc.rz(p, 1)
qc.cx(1, 2)
qc.rz(p, 2)
theta = Parameter('theta')
phi = Parameter('phi')
gamma = Parameter('gamma')
qr = QuantumRegister(9)
larger_qc = QuantumCircuit(qr)
larger_qc.append(qc.to_instruction({p: theta}), qr[0:3])
larger_qc.append(qc.to_instruction({p: phi}), qr[3:6])
larger_qc.append(qc.to_instruction({p: gamma}), qr[6:9])
print(larger_qc.draw())
print(larger_qc.decompose().draw())
import qiskit.tools.jupyter
%qiskit_version_table
%qiskit_copyright
|
https://github.com/qiskit-community/qiskit-translations-staging
|
qiskit-community
|
import random
from qiskit.quantum_info import Statevector
secret = random.randint(0,7) # the owner is randomly picked
secret_string = format(secret, '03b') # format the owner in 3-bit string
oracle = Statevector.from_label(secret_string) # let the oracle know the owner
from qiskit.algorithms import AmplificationProblem
problem = AmplificationProblem(oracle, is_good_state=secret_string)
from qiskit.algorithms import Grover
grover_circuits = []
for iteration in range(1,3):
grover = Grover(iterations=iteration)
circuit = grover.construct_circuit(problem)
circuit.measure_all()
grover_circuits.append(circuit)
# Grover's circuit with 1 iteration
grover_circuits[0].draw()
# Grover's circuit with 2 iterations
grover_circuits[1].draw()
from qiskit_ibm_runtime import QiskitRuntimeService
service = QiskitRuntimeService()
backend = "ibmq_qasm_simulator" # use the simulator
from qiskit_ibm_runtime import Sampler, Session
with Session(service=service, backend=backend):
sampler = Sampler()
job = sampler.run(circuits=grover_circuits, shots=1000)
result = job.result()
print(result)
from qiskit.tools.visualization import plot_histogram
# Extract bit string with highest probability from results as the answer
result_dict = result.quasi_dists[1].binary_probabilities()
answer = max(result_dict, key=result_dict.get)
print(f"As you can see, the quantum computer returned '{answer}' as the answer with highest probability.\n"
"And the results with 2 iterations have higher probability than the results with 1 iteration."
)
# Plot the results
plot_histogram(result.quasi_dists, legend=['1 iteration', '2 iterations'])
# Print the results and the correct answer.
print(f"Quantum answer: {answer}")
print(f"Correct answer: {secret_string}")
print('Success!' if answer == secret_string else 'Failure!')
import qiskit_ibm_runtime
qiskit_ibm_runtime.version.get_version_info()
import qiskit.tools.jupyter
%qiskit_version_table
%qiskit_copyright
|
https://github.com/qiskit-community/qiskit-translations-staging
|
qiskit-community
|
from qiskit_nature.units import DistanceUnit
from qiskit_nature.second_q.drivers import PySCFDriver
driver = PySCFDriver(
atom="H 0 0 0; H 0 0 0.735",
basis="sto3g",
charge=0,
spin=0,
unit=DistanceUnit.ANGSTROM,
)
es_problem = driver.run()
from qiskit_nature.second_q.mappers import JordanWignerMapper
mapper = JordanWignerMapper()
from qiskit.algorithms.eigensolvers import NumPyEigensolver
numpy_solver = NumPyEigensolver(filter_criterion=es_problem.get_default_filter_criterion())
from qiskit.algorithms.minimum_eigensolvers import VQE
from qiskit.algorithms.optimizers import SLSQP
from qiskit.primitives import Estimator
from qiskit_nature.second_q.algorithms import GroundStateEigensolver, QEOM
from qiskit_nature.second_q.circuit.library import HartreeFock, UCCSD
ansatz = UCCSD(
es_problem.num_spatial_orbitals,
es_problem.num_particles,
mapper,
initial_state=HartreeFock(
es_problem.num_spatial_orbitals,
es_problem.num_particles,
mapper,
),
)
estimator = Estimator()
# This first part sets the ground state solver
# see more about this part in the ground state calculation tutorial
solver = VQE(estimator, ansatz, SLSQP())
solver.initial_point = [0.0] * ansatz.num_parameters
gse = GroundStateEigensolver(mapper, solver)
# The qEOM algorithm is simply instantiated with the chosen ground state solver and Estimator primitive
qeom_excited_states_solver = QEOM(gse, estimator, "sd")
from qiskit_nature.second_q.algorithms import ExcitedStatesEigensolver
numpy_excited_states_solver = ExcitedStatesEigensolver(mapper, numpy_solver)
numpy_results = numpy_excited_states_solver.solve(es_problem)
qeom_results = qeom_excited_states_solver.solve(es_problem)
print(numpy_results)
print("\n\n")
print(qeom_results)
import numpy as np
def filter_criterion(eigenstate, eigenvalue, aux_values):
return np.isclose(aux_values["ParticleNumber"][0], 2.0)
new_numpy_solver = NumPyEigensolver(filter_criterion=filter_criterion)
new_numpy_excited_states_solver = ExcitedStatesEigensolver(mapper, new_numpy_solver)
new_numpy_results = new_numpy_excited_states_solver.solve(es_problem)
print(new_numpy_results)
import qiskit.tools.jupyter
%qiskit_version_table
%qiskit_copyright
|
https://github.com/mmetcalf14/Hamiltonian_Downfolding_IBM
|
mmetcalf14
|
# -*- coding: utf-8 -*-
# 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 derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""
Functions used for the analysis of randomized benchmarking results.
"""
from scipy.optimize import curve_fit
import numpy as np
from qiskit import QiskitError
from ..tomography import marginal_counts
from ...characterization.fitters import build_counts_dict_from_list
try:
from matplotlib import pyplot as plt
HAS_MATPLOTLIB = True
except ImportError:
HAS_MATPLOTLIB = False
class RBFitter:
"""
Class for fitters for randomized benchmarking
"""
def __init__(self, backend_result, cliff_lengths,
rb_pattern=None):
"""
Args:
backend_result: list of results (qiskit.Result).
cliff_lengths: the Clifford lengths, 2D list i x j where i is the
number of patterns, j is the number of cliffords lengths
rb_pattern: the pattern for the rb sequences.
"""
if rb_pattern is None:
rb_pattern = [[0]]
self._cliff_lengths = cliff_lengths
self._rb_pattern = rb_pattern
self._raw_data = []
self._ydata = []
self._fit = []
self._nseeds = 0
self._result_list = []
self.add_data(backend_result)
@property
def raw_data(self):
"""Return raw data."""
return self._raw_data
@property
def cliff_lengths(self):
"""Return clifford lengths."""
return self.cliff_lengths
@property
def ydata(self):
"""Return ydata (means and std devs)."""
return self._ydata
@property
def fit(self):
"""Return fit."""
return self._fit
@property
def seeds(self):
"""Return the number of loaded seeds."""
return self._nseeds
@property
def results(self):
"""Return all the results."""
return self._result_list
def add_data(self, new_backend_result, rerun_fit=True):
"""
Add a new result. Re calculate the raw data, means and
fit.
Args:
new_backend_result: list of rb results
rerun_fit: re caculate the means and fit the result
Additional information:
Assumes that 'result' was executed is
the output of circuits generated by randomized_becnhmarking_seq,
"""
if new_backend_result is None:
return
if not isinstance(new_backend_result, list):
new_backend_result = [new_backend_result]
for result in new_backend_result:
self._result_list.append(result)
# update the number of seeds *if* new ones
# added. Note, no checking if we've done all the
# cliffords
for rbcirc in result.results:
nseeds_circ = int(rbcirc.header.name.split('_')[-1])
if (nseeds_circ+1) > self._nseeds:
self._nseeds = nseeds_circ+1
for result in self._result_list:
if not len(result.results) == len(self._cliff_lengths[0]):
raise ValueError(
"The number of clifford lengths must match the number of "
"results")
if rerun_fit:
self.calc_data()
self.calc_statistics()
self.fit_data()
@staticmethod
def _rb_fit_fun(x, a, alpha, b):
"""Function used to fit rb."""
# pylint: disable=invalid-name
return a * alpha ** x + b
def calc_data(self):
"""
Retrieve probabilities of success from execution results. Outputs
results into an internal variable _raw_data which is a 3-dimensional
list, where item (i,j,k) is the probability to measure the ground state
for the set of qubits in pattern "i" for seed no. j and vector length
self._cliff_lengths[i][k].
Additional information:
Assumes that 'result' was executed is
the output of circuits generated by randomized_becnhmarking_seq,
"""
circ_counts = {}
circ_shots = {}
for seedidx in range(self._nseeds):
for circ, _ in enumerate(self._cliff_lengths[0]):
circ_name = 'rb_length_%d_seed_%d' % (circ, seedidx)
count_list = []
for result in self._result_list:
try:
count_list.append(result.get_counts(circ_name))
except (QiskitError, KeyError):
pass
circ_counts[circ_name] = \
build_counts_dict_from_list(count_list)
circ_shots[circ_name] = sum(circ_counts[circ_name].values())
self._raw_data = []
startind = 0
for patt_ind in range(len(self._rb_pattern)):
string_of_0s = ''
string_of_0s = string_of_0s.zfill(len(self._rb_pattern[patt_ind]))
self._raw_data.append([])
endind = startind+len(self._rb_pattern[patt_ind])
for i in range(self._nseeds):
self._raw_data[-1].append([])
for k, _ in enumerate(self._cliff_lengths[patt_ind]):
circ_name = 'rb_length_%d_seed_%d' % (k, i)
counts_subspace = marginal_counts(
circ_counts[circ_name],
np.arange(startind, endind))
self._raw_data[-1][i].append(
counts_subspace.get(string_of_0s, 0)
/ circ_shots[circ_name])
startind += (endind)
def calc_statistics(self):
"""
Extract averages and std dev from the raw data (self._raw_data).
Assumes that self._calc_data has been run. Output into internal
_ydata variable:
ydata is a list of dictionaries (length number of patterns).
Dictionary ydata[i]:
ydata[i]['mean'] is a numpy_array of length n;
entry j of this array contains the mean probability of
success over seeds, for vector length
self._cliff_lengths[i][j].
And ydata[i]['std'] is a numpy_array of length n;
entry j of this array contains the std
of the probability of success over seeds,
for vector length self._cliff_lengths[i][j].
"""
self._ydata = []
for patt_ind in range(len(self._rb_pattern)):
self._ydata.append({})
self._ydata[-1]['mean'] = np.mean(self._raw_data[patt_ind], 0)
if len(self._raw_data[patt_ind]) == 1: # 1 seed
self._ydata[-1]['std'] = None
else:
self._ydata[-1]['std'] = np.std(self._raw_data[patt_ind], 0)
def fit_data(self):
"""
Fit the RB results to an exponential curve.
Fit each of the patterns
Puts the results into a list of fit dictionaries:
where each dictionary corresponds to a pattern and has fields:
'params' - three parameters of rb_fit_fun. The middle one is the
exponent.
'err' - the error limits of the parameters.
'epc' - error per Clifford
"""
self._fit = []
for patt_ind, (lens, qubits) in enumerate(zip(self._cliff_lengths,
self._rb_pattern)):
# if at least one of the std values is zero, then sigma is replaced
# by None
if not self._ydata[patt_ind]['std'] is None:
sigma = self._ydata[patt_ind]['std'].copy()
if len(sigma) - np.count_nonzero(sigma) > 0:
sigma = None
else:
sigma = None
params, pcov = curve_fit(self._rb_fit_fun, lens,
self._ydata[patt_ind]['mean'],
sigma=sigma,
p0=(1.0, 0.95, 0.0),
bounds=([-2, 0, -2], [2, 1, 2]))
alpha = params[1] # exponent
params_err = np.sqrt(np.diag(pcov))
alpha_err = params_err[1]
nrb = 2 ** len(qubits)
epc = (nrb-1)/nrb*(1-alpha)
epc_err = epc*alpha_err/alpha
self._fit.append({'params': params, 'params_err': params_err,
'epc': epc, 'epc_err': epc_err})
def plot_rb_data(self, pattern_index=0, ax=None,
add_label=True, show_plt=True):
"""
Plot randomized benchmarking data of a single pattern.
Args:
pattern_index: which RB pattern to plot
ax (Axes or None): plot axis (if passed in).
add_label (bool): Add an EPC label
show_plt (bool): display the plot.
Raises:
ImportError: If matplotlib is not installed.
"""
fit_function = self._rb_fit_fun
if not HAS_MATPLOTLIB:
raise ImportError('The function plot_rb_data needs matplotlib. '
'Run "pip install matplotlib" before.')
if ax is None:
plt.figure()
ax = plt.gca()
xdata = self._cliff_lengths[pattern_index]
# Plot the result for each sequence
for one_seed_data in self._raw_data[pattern_index]:
ax.plot(xdata, one_seed_data, color='gray', linestyle='none',
marker='x')
# Plot the mean with error bars
ax.errorbar(xdata, self._ydata[pattern_index]['mean'],
yerr=self._ydata[pattern_index]['std'],
color='r', linestyle='--', linewidth=3)
# Plot the fit
ax.plot(xdata,
fit_function(xdata, *self._fit[pattern_index]['params']),
color='blue', linestyle='-', linewidth=2)
ax.tick_params(labelsize=14)
ax.set_xlabel('Clifford Length', fontsize=16)
ax.set_ylabel('Ground State Population', fontsize=16)
ax.grid(True)
if add_label:
bbox_props = dict(boxstyle="round,pad=0.3",
fc="white", ec="black", lw=2)
ax.text(0.6, 0.9,
"alpha: %.3f(%.1e) EPC: %.3e(%.1e)" %
(self._fit[pattern_index]['params'][1],
self._fit[pattern_index]['params_err'][1],
self._fit[pattern_index]['epc'],
self._fit[pattern_index]['epc_err']),
ha="center", va="center", size=14,
bbox=bbox_props, transform=ax.transAxes)
if show_plt:
plt.show()
|
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 derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""Tests preset pass manager API"""
import unittest
from test import combine
from ddt import ddt, data
import numpy as np
import qiskit
from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister
from qiskit.circuit import Qubit, Gate, ControlFlowOp, ForLoopOp
from qiskit.compiler import transpile, assemble
from qiskit.transpiler import CouplingMap, Layout, PassManager, TranspilerError, Target
from qiskit.circuit.library import U2Gate, U3Gate, QuantumVolume, CXGate, CZGate, XGate
from qiskit.transpiler.passes import (
ALAPScheduleAnalysis,
PadDynamicalDecoupling,
RemoveResetInZeroState,
)
from qiskit.test import QiskitTestCase
from qiskit.providers.fake_provider import (
FakeBelem,
FakeTenerife,
FakeMelbourne,
FakeJohannesburg,
FakeRueschlikon,
FakeTokyo,
FakePoughkeepsie,
FakeLagosV2,
)
from qiskit.converters import circuit_to_dag
from qiskit.circuit.library import GraphState
from qiskit.quantum_info import random_unitary
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
from qiskit.transpiler.preset_passmanagers import level0, level1, level2, level3
from qiskit.transpiler.passes import Collect2qBlocks, GatesInBasis
from qiskit.transpiler.preset_passmanagers.builtin_plugins import OptimizationPassManager
def mock_get_passmanager_stage(
stage_name,
plugin_name,
pm_config,
optimization_level=None, # pylint: disable=unused-argument
) -> PassManager:
"""Mock function for get_passmanager_stage."""
if stage_name == "translation" and plugin_name == "custom_stage_for_test":
pm = PassManager([RemoveResetInZeroState()])
return pm
elif stage_name == "scheduling" and plugin_name == "custom_stage_for_test":
dd_sequence = [XGate(), XGate()]
pm = PassManager(
[
ALAPScheduleAnalysis(pm_config.instruction_durations),
PadDynamicalDecoupling(pm_config.instruction_durations, dd_sequence),
]
)
return pm
elif stage_name == "init":
return PassManager([])
elif stage_name == "routing":
return PassManager([])
elif stage_name == "optimization":
return OptimizationPassManager().pass_manager(pm_config, optimization_level)
elif stage_name == "layout":
return PassManager([])
else:
raise Exception("Failure, unexpected stage plugin combo for test")
def emptycircuit():
"""Empty circuit"""
return QuantumCircuit()
def circuit_2532():
"""See https://github.com/Qiskit/qiskit-terra/issues/2532"""
circuit = QuantumCircuit(5)
circuit.cx(2, 4)
return circuit
@ddt
class TestPresetPassManager(QiskitTestCase):
"""Test preset passmanagers work as expected."""
@combine(level=[0, 1, 2, 3], name="level{level}")
def test_no_coupling_map_with_sabre(self, level):
"""Test that coupling_map can be None with Sabre (level={level})"""
q = QuantumRegister(2, name="q")
circuit = QuantumCircuit(q)
circuit.cz(q[0], q[1])
result = transpile(
circuit,
coupling_map=None,
layout_method="sabre",
routing_method="sabre",
optimization_level=level,
)
self.assertEqual(result, circuit)
@combine(level=[0, 1, 2, 3], name="level{level}")
def test_no_coupling_map(self, level):
"""Test that coupling_map can be None (level={level})"""
q = QuantumRegister(2, name="q")
circuit = QuantumCircuit(q)
circuit.cz(q[0], q[1])
result = transpile(circuit, basis_gates=["u1", "u2", "u3", "cx"], optimization_level=level)
self.assertIsInstance(result, QuantumCircuit)
def test_layout_3239(self, level=3):
"""Test final layout after preset level3 passmanager does not include diagonal gates
See: https://github.com/Qiskit/qiskit-terra/issues/3239
"""
qc = QuantumCircuit(5, 5)
qc.h(0)
qc.cx(range(3), range(1, 4))
qc.z(range(4))
qc.measure(range(4), range(4))
result = transpile(
qc,
basis_gates=["u1", "u2", "u3", "cx"],
layout_method="trivial",
optimization_level=level,
)
dag = circuit_to_dag(result)
op_nodes = [node.name for node in dag.topological_op_nodes()]
self.assertNotIn("u1", op_nodes) # Check if the diagonal Z-Gates (u1) were removed
@combine(level=[0, 1, 2, 3], name="level{level}")
def test_no_basis_gates(self, level):
"""Test that basis_gates can be None (level={level})"""
q = QuantumRegister(2, name="q")
circuit = QuantumCircuit(q)
circuit.h(q[0])
circuit.cz(q[0], q[1])
result = transpile(circuit, basis_gates=None, optimization_level=level)
self.assertEqual(result, circuit)
def test_level0_keeps_reset(self):
"""Test level 0 should keep the reset instructions"""
q = QuantumRegister(2, name="q")
circuit = QuantumCircuit(q)
circuit.reset(q[0])
circuit.reset(q[0])
result = transpile(circuit, basis_gates=None, optimization_level=0)
self.assertEqual(result, circuit)
@combine(level=[0, 1, 2, 3], name="level{level}")
def test_unitary_is_preserved_if_in_basis(self, level):
"""Test that a unitary is not synthesized if in the basis."""
qc = QuantumCircuit(2)
qc.unitary(random_unitary(4, seed=42), [0, 1])
qc.measure_all()
result = transpile(qc, basis_gates=["cx", "u", "unitary"], optimization_level=level)
self.assertEqual(result, qc)
@combine(level=[0, 1, 2, 3], name="level{level}")
def test_unitary_is_preserved_if_basis_is_None(self, level):
"""Test that a unitary is not synthesized if basis is None."""
qc = QuantumCircuit(2)
qc.unitary(random_unitary(4, seed=4242), [0, 1])
qc.measure_all()
result = transpile(qc, basis_gates=None, optimization_level=level)
self.assertEqual(result, qc)
@combine(level=[0, 1, 2, 3], name="level{level}")
def test_unitary_is_preserved_if_in_basis_synthesis_translation(self, level):
"""Test that a unitary is not synthesized if in the basis with synthesis translation."""
qc = QuantumCircuit(2)
qc.unitary(random_unitary(4, seed=424242), [0, 1])
qc.measure_all()
result = transpile(
qc,
basis_gates=["cx", "u", "unitary"],
optimization_level=level,
translation_method="synthesis",
)
self.assertEqual(result, qc)
@combine(level=[0, 1, 2, 3], name="level{level}")
def test_unitary_is_preserved_if_basis_is_None_synthesis_transltion(self, level):
"""Test that a unitary is not synthesized if basis is None with synthesis translation."""
qc = QuantumCircuit(2)
qc.unitary(random_unitary(4, seed=42424242), [0, 1])
qc.measure_all()
result = transpile(
qc, basis_gates=None, optimization_level=level, translation_method="synthesis"
)
self.assertEqual(result, qc)
@combine(level=[0, 1, 2, 3], name="level{level}")
def test_respect_basis(self, level):
"""Test that all levels respect basis"""
qc = QuantumCircuit(3)
qc.h(0)
qc.h(1)
qc.cp(np.pi / 8, 0, 1)
qc.cp(np.pi / 4, 0, 2)
basis_gates = ["id", "rz", "sx", "x", "cx"]
result = transpile(
qc, basis_gates=basis_gates, coupling_map=[[0, 1], [2, 1]], optimization_level=level
)
dag = circuit_to_dag(result)
circuit_ops = {node.name for node in dag.topological_op_nodes()}
self.assertEqual(circuit_ops.union(set(basis_gates)), set(basis_gates))
@combine(level=[0, 1, 2, 3], name="level{level}")
def test_alignment_constraints_called_with_by_default(self, level):
"""Test that TimeUnitConversion is not called if there is no delay in the circuit."""
q = QuantumRegister(2, name="q")
circuit = QuantumCircuit(q)
circuit.h(q[0])
circuit.cz(q[0], q[1])
with unittest.mock.patch("qiskit.transpiler.passes.TimeUnitConversion.run") as mock:
transpile(circuit, backend=FakeJohannesburg(), optimization_level=level)
mock.assert_not_called()
@combine(level=[0, 1, 2, 3], name="level{level}")
def test_alignment_constraints_called_with_delay_in_circuit(self, level):
"""Test that TimeUnitConversion is called if there is a delay in the circuit."""
q = QuantumRegister(2, name="q")
circuit = QuantumCircuit(q)
circuit.h(q[0])
circuit.cz(q[0], q[1])
circuit.delay(9.5, unit="ns")
with unittest.mock.patch(
"qiskit.transpiler.passes.TimeUnitConversion.run", return_value=circuit_to_dag(circuit)
) as mock:
transpile(circuit, backend=FakeJohannesburg(), optimization_level=level)
mock.assert_called_once()
def test_unroll_only_if_not_gates_in_basis(self):
"""Test that the list of passes _unroll only runs if a gate is not in the basis."""
qcomp = FakeBelem()
qv_circuit = QuantumVolume(3)
gates_in_basis_true_count = 0
collect_2q_blocks_count = 0
# pylint: disable=unused-argument
def counting_callback_func(pass_, dag, time, property_set, count):
nonlocal gates_in_basis_true_count
nonlocal collect_2q_blocks_count
if isinstance(pass_, GatesInBasis) and property_set["all_gates_in_basis"]:
gates_in_basis_true_count += 1
if isinstance(pass_, Collect2qBlocks):
collect_2q_blocks_count += 1
transpile(
qv_circuit,
backend=qcomp,
optimization_level=3,
callback=counting_callback_func,
translation_method="synthesis",
)
self.assertEqual(gates_in_basis_true_count + 1, collect_2q_blocks_count)
def test_get_vf2_call_limit_deprecated(self):
"""Test that calling test_get_vf2_call_limit emits deprecation warning."""
with self.assertWarns(DeprecationWarning):
qiskit.transpiler.preset_passmanagers.common.get_vf2_call_limit(optimization_level=3)
@ddt
class TestTranspileLevels(QiskitTestCase):
"""Test transpiler on fake backend"""
@combine(
circuit=[emptycircuit, circuit_2532],
level=[0, 1, 2, 3],
backend=[
FakeTenerife(),
FakeMelbourne(),
FakeRueschlikon(),
FakeTokyo(),
FakePoughkeepsie(),
None,
],
dsc="Transpiler {circuit.__name__} on {backend} backend at level {level}",
name="{circuit.__name__}_{backend}_level{level}",
)
def test(self, circuit, level, backend):
"""All the levels with all the backends"""
result = transpile(circuit(), backend=backend, optimization_level=level, seed_transpiler=42)
self.assertIsInstance(result, QuantumCircuit)
@ddt
class TestPassesInspection(QiskitTestCase):
"""Test run passes under different conditions"""
def setUp(self):
"""Sets self.callback to set self.passes with the passes that have been executed"""
super().setUp()
self.passes = []
def callback(**kwargs):
self.passes.append(kwargs["pass_"].__class__.__name__)
self.callback = callback
@data(0, 1, 2, 3)
def test_no_coupling_map(self, level):
"""Without coupling map, no layout selection nor swapper"""
qr = QuantumRegister(3, "q")
qc = QuantumCircuit(qr)
qc.cx(qr[2], qr[1])
qc.cx(qr[2], qr[0])
_ = transpile(qc, optimization_level=level, callback=self.callback)
self.assertNotIn("SetLayout", self.passes)
self.assertNotIn("TrivialLayout", self.passes)
self.assertNotIn("ApplyLayout", self.passes)
self.assertNotIn("StochasticSwap", self.passes)
self.assertNotIn("SabreSwap", self.passes)
self.assertNotIn("CheckGateDirection", self.passes)
@data(0, 1, 2, 3)
def test_backend(self, level):
"""With backend a layout and a swapper is run"""
qr = QuantumRegister(5, "q")
qc = QuantumCircuit(qr)
qc.cx(qr[2], qr[4])
backend = FakeMelbourne()
_ = transpile(qc, backend, optimization_level=level, callback=self.callback)
self.assertIn("SetLayout", self.passes)
self.assertIn("ApplyLayout", self.passes)
self.assertIn("CheckGateDirection", self.passes)
@data(0, 1, 2, 3)
def test_5409(self, level):
"""The parameter layout_method='noise_adaptive' should be honored
See: https://github.com/Qiskit/qiskit-terra/issues/5409
"""
qr = QuantumRegister(5, "q")
qc = QuantumCircuit(qr)
qc.cx(qr[2], qr[4])
backend = FakeMelbourne()
_ = transpile(
qc,
backend,
layout_method="noise_adaptive",
optimization_level=level,
callback=self.callback,
)
self.assertIn("SetLayout", self.passes)
self.assertIn("ApplyLayout", self.passes)
self.assertIn("NoiseAdaptiveLayout", self.passes)
@data(0, 1, 2, 3)
def test_symmetric_coupling_map(self, level):
"""Symmetric coupling map does not run CheckGateDirection"""
qr = QuantumRegister(2, "q")
qc = QuantumCircuit(qr)
qc.cx(qr[0], qr[1])
coupling_map = [[0, 1], [1, 0]]
_ = transpile(
qc,
coupling_map=coupling_map,
initial_layout=[0, 1],
optimization_level=level,
callback=self.callback,
)
self.assertIn("SetLayout", self.passes)
self.assertIn("ApplyLayout", self.passes)
self.assertNotIn("CheckGateDirection", self.passes)
@data(0, 1, 2, 3)
def test_initial_layout_fully_connected_cm(self, level):
"""Honor initial_layout when coupling_map=None
See: https://github.com/Qiskit/qiskit-terra/issues/5345
"""
qr = QuantumRegister(2, "q")
qc = QuantumCircuit(qr)
qc.h(qr[0])
qc.cx(qr[0], qr[1])
transpiled = transpile(
qc, initial_layout=[0, 1], optimization_level=level, callback=self.callback
)
self.assertIn("SetLayout", self.passes)
self.assertIn("ApplyLayout", self.passes)
self.assertEqual(transpiled._layout.initial_layout, Layout.from_qubit_list([qr[0], qr[1]]))
@data(0, 1, 2, 3)
def test_partial_layout_fully_connected_cm(self, level):
"""Honor initial_layout (partially defined) when coupling_map=None
See: https://github.com/Qiskit/qiskit-terra/issues/5345
"""
qr = QuantumRegister(2, "q")
qc = QuantumCircuit(qr)
qc.h(qr[0])
qc.cx(qr[0], qr[1])
transpiled = transpile(
qc, initial_layout=[4, 2], optimization_level=level, callback=self.callback
)
self.assertIn("SetLayout", self.passes)
self.assertIn("ApplyLayout", self.passes)
ancilla = QuantumRegister(3, "ancilla")
self.assertEqual(
transpiled._layout.initial_layout,
Layout.from_qubit_list([ancilla[0], ancilla[1], qr[1], ancilla[2], qr[0]]),
)
@unittest.mock.patch.object(
level0.PassManagerStagePluginManager,
"get_passmanager_stage",
wraps=mock_get_passmanager_stage,
)
def test_backend_with_custom_stages(self, _plugin_manager_mock):
"""Test transpile() executes backend specific custom stage."""
optimization_level = 1
class TargetBackend(FakeLagosV2):
"""Fake lagos subclass with custom transpiler stages."""
def get_scheduling_stage_plugin(self):
"""Custom scheduling stage."""
return "custom_stage_for_test"
def get_translation_stage_plugin(self):
"""Custom post translation stage."""
return "custom_stage_for_test"
target = TargetBackend()
qr = QuantumRegister(2, "q")
qc = QuantumCircuit(qr)
qc.h(qr[0])
qc.cx(qr[0], qr[1])
_ = transpile(qc, target, optimization_level=optimization_level, callback=self.callback)
self.assertIn("ALAPScheduleAnalysis", self.passes)
self.assertIn("PadDynamicalDecoupling", self.passes)
self.assertIn("RemoveResetInZeroState", self.passes)
def test_level1_runs_vf2post_layout_when_routing_required(self):
"""Test that if we run routing as part of sabre layout VF2PostLayout runs."""
target = FakeLagosV2()
qc = QuantumCircuit(5)
qc.h(0)
qc.cy(0, 1)
qc.cy(0, 2)
qc.cy(0, 3)
qc.cy(0, 4)
qc.measure_all()
_ = transpile(qc, target, optimization_level=1, callback=self.callback)
# Expected call path for layout and routing is:
# 1. TrivialLayout (no perfect match)
# 2. VF2Layout (no perfect match)
# 3. SabreLayout (heuristic layout and also runs routing)
# 4. VF2PostLayout (applies a better layout)
self.assertIn("TrivialLayout", self.passes)
self.assertIn("VF2Layout", self.passes)
self.assertIn("SabreLayout", self.passes)
self.assertIn("VF2PostLayout", self.passes)
# Assert we don't run standalone sabre swap
self.assertNotIn("SabreSwap", self.passes)
def test_level1_runs_vf2post_layout_when_routing_method_set_and_required(self):
"""Test that if we run routing as part of sabre layout VF2PostLayout runs."""
target = FakeLagosV2()
qc = QuantumCircuit(5)
qc.h(0)
qc.cy(0, 1)
qc.cy(0, 2)
qc.cy(0, 3)
qc.cy(0, 4)
qc.measure_all()
_ = transpile(
qc, target, optimization_level=1, routing_method="stochastic", callback=self.callback
)
# Expected call path for layout and routing is:
# 1. TrivialLayout (no perfect match)
# 2. VF2Layout (no perfect match)
# 3. SabreLayout (heuristic layout and also runs routing)
# 4. VF2PostLayout (applies a better layout)
self.assertIn("TrivialLayout", self.passes)
self.assertIn("VF2Layout", self.passes)
self.assertIn("SabreLayout", self.passes)
self.assertIn("VF2PostLayout", self.passes)
self.assertIn("StochasticSwap", self.passes)
def test_level1_not_runs_vf2post_layout_when_layout_method_set(self):
"""Test that if we don't run VF2PostLayout with custom layout_method."""
target = FakeLagosV2()
qc = QuantumCircuit(5)
qc.h(0)
qc.cy(0, 1)
qc.cy(0, 2)
qc.cy(0, 3)
qc.cy(0, 4)
qc.measure_all()
_ = transpile(
qc, target, optimization_level=1, layout_method="dense", callback=self.callback
)
self.assertNotIn("TrivialLayout", self.passes)
self.assertNotIn("VF2Layout", self.passes)
self.assertNotIn("SabreLayout", self.passes)
self.assertNotIn("VF2PostLayout", self.passes)
self.assertIn("DenseLayout", self.passes)
self.assertIn("SabreSwap", self.passes)
def test_level1_not_run_vf2post_layout_when_trivial_is_perfect(self):
"""Test that if we find a trivial perfect layout we don't run vf2post."""
target = FakeLagosV2()
qc = QuantumCircuit(2)
qc.h(0)
qc.cx(0, 1)
qc.measure_all()
_ = transpile(qc, target, optimization_level=1, callback=self.callback)
self.assertIn("TrivialLayout", self.passes)
self.assertNotIn("VF2Layout", self.passes)
self.assertNotIn("SabreLayout", self.passes)
self.assertNotIn("VF2PostLayout", self.passes)
# Assert we don't run standalone sabre swap
self.assertNotIn("SabreSwap", self.passes)
def test_level1_not_run_vf2post_layout_when_vf2layout_is_perfect(self):
"""Test that if we find a vf2 perfect layout we don't run vf2post."""
target = FakeLagosV2()
qc = QuantumCircuit(4)
qc.h(0)
qc.cx(0, 1)
qc.cx(0, 2)
qc.cx(0, 3)
qc.measure_all()
_ = transpile(qc, target, optimization_level=1, callback=self.callback)
self.assertIn("TrivialLayout", self.passes)
self.assertIn("VF2Layout", self.passes)
self.assertNotIn("SabreLayout", self.passes)
self.assertNotIn("VF2PostLayout", self.passes)
# Assert we don't run standalone sabre swap
self.assertNotIn("SabreSwap", self.passes)
def test_level1_runs_vf2post_layout_when_routing_required_control_flow(self):
"""Test that if we run routing as part of sabre layout VF2PostLayout runs."""
target = FakeLagosV2()
_target = target.target
target._target.add_instruction(ForLoopOp, name="for_loop")
qc = QuantumCircuit(5)
qc.h(0)
qc.cy(0, 1)
qc.cy(0, 2)
qc.cy(0, 3)
qc.cy(0, 4)
with qc.for_loop((1,)):
qc.cx(0, 1)
qc.measure_all()
_ = transpile(qc, target, optimization_level=1, callback=self.callback)
# Expected call path for layout and routing is:
# 1. TrivialLayout (no perfect match)
# 2. VF2Layout (no perfect match)
# 3. SabreLayout (heuristic layout)
# 4. VF2PostLayout (applies a better layout)
self.assertIn("TrivialLayout", self.passes)
self.assertIn("VF2Layout", self.passes)
self.assertIn("SabreLayout", self.passes)
self.assertIn("VF2PostLayout", self.passes)
def test_level1_not_runs_vf2post_layout_when_layout_method_set_control_flow(self):
"""Test that if we don't run VF2PostLayout with custom layout_method."""
target = FakeLagosV2()
_target = target.target
target._target.add_instruction(ForLoopOp, name="for_loop")
qc = QuantumCircuit(5)
qc.h(0)
qc.cy(0, 1)
qc.cy(0, 2)
qc.cy(0, 3)
qc.cy(0, 4)
with qc.for_loop((1,)):
qc.cx(0, 1)
qc.measure_all()
_ = transpile(
qc, target, optimization_level=1, layout_method="dense", callback=self.callback
)
self.assertNotIn("TrivialLayout", self.passes)
self.assertNotIn("VF2Layout", self.passes)
self.assertNotIn("SabreLayout", self.passes)
self.assertNotIn("VF2PostLayout", self.passes)
self.assertIn("DenseLayout", self.passes)
self.assertIn("SabreSwap", self.passes)
def test_level1_not_run_vf2post_layout_when_trivial_is_perfect_control_flow(self):
"""Test that if we find a trivial perfect layout we don't run vf2post."""
target = FakeLagosV2()
_target = target.target
target._target.add_instruction(ForLoopOp, name="for_loop")
qc = QuantumCircuit(2)
qc.h(0)
qc.cx(0, 1)
with qc.for_loop((1,)):
qc.cx(0, 1)
qc.measure_all()
_ = transpile(qc, target, optimization_level=1, callback=self.callback)
self.assertIn("TrivialLayout", self.passes)
self.assertNotIn("VF2Layout", self.passes)
self.assertNotIn("SabreLayout", self.passes)
self.assertNotIn("SabreSwap", self.passes)
self.assertNotIn("VF2PostLayout", self.passes)
def test_level1_not_run_vf2post_layout_when_vf2layout_is_perfect_control_flow(self):
"""Test that if we find a vf2 perfect layout we don't run vf2post."""
target = FakeLagosV2()
_target = target.target
target._target.add_instruction(ForLoopOp, name="for_loop")
qc = QuantumCircuit(4)
qc.h(0)
qc.cx(0, 1)
qc.cx(0, 2)
qc.cx(0, 3)
with qc.for_loop((1,)):
qc.cx(0, 1)
qc.measure_all()
_ = transpile(qc, target, optimization_level=1, callback=self.callback)
self.assertIn("TrivialLayout", self.passes)
self.assertIn("VF2Layout", self.passes)
self.assertNotIn("SabreLayout", self.passes)
self.assertNotIn("VF2PostLayout", self.passes)
self.assertNotIn("SabreSwap", self.passes)
@ddt
class TestInitialLayouts(QiskitTestCase):
"""Test transpiling with different layouts"""
@data(0, 1, 2, 3)
def test_layout_1711(self, level):
"""Test that a user-given initial layout is respected,
in the qobj.
See: https://github.com/Qiskit/qiskit-terra/issues/1711
"""
# build a circuit which works as-is on the coupling map, using the initial layout
qr = QuantumRegister(3, "q")
cr = ClassicalRegister(3)
ancilla = QuantumRegister(13, "ancilla")
qc = QuantumCircuit(qr, cr)
qc.cx(qr[2], qr[1])
qc.cx(qr[2], qr[0])
initial_layout = {0: qr[1], 2: qr[0], 15: qr[2]}
final_layout = {
0: qr[1],
1: ancilla[0],
2: qr[0],
3: ancilla[1],
4: ancilla[2],
5: ancilla[3],
6: ancilla[4],
7: ancilla[5],
8: ancilla[6],
9: ancilla[7],
10: ancilla[8],
11: ancilla[9],
12: ancilla[10],
13: ancilla[11],
14: ancilla[12],
15: qr[2],
}
backend = FakeRueschlikon()
qc_b = transpile(qc, backend, initial_layout=initial_layout, optimization_level=level)
qobj = assemble(qc_b)
self.assertEqual(qc_b._layout.initial_layout._p2v, final_layout)
compiled_ops = qobj.experiments[0].instructions
for operation in compiled_ops:
if operation.name == "cx":
self.assertIn(operation.qubits, backend.configuration().coupling_map)
self.assertIn(operation.qubits, [[15, 0], [15, 2]])
@data(0, 1, 2, 3)
def test_layout_2532(self, level):
"""Test that a user-given initial layout is respected,
in the transpiled circuit.
See: https://github.com/Qiskit/qiskit-terra/issues/2532
"""
# build a circuit which works as-is on the coupling map, using the initial layout
qr = QuantumRegister(5, "q")
cr = ClassicalRegister(2)
ancilla = QuantumRegister(9, "ancilla")
qc = QuantumCircuit(qr, cr)
qc.cx(qr[2], qr[4])
initial_layout = {
qr[2]: 11,
qr[4]: 3, # map to [11, 3] connection
qr[0]: 1,
qr[1]: 5,
qr[3]: 9,
}
final_layout = {
0: ancilla[0],
1: qr[0],
2: ancilla[1],
3: qr[4],
4: ancilla[2],
5: qr[1],
6: ancilla[3],
7: ancilla[4],
8: ancilla[5],
9: qr[3],
10: ancilla[6],
11: qr[2],
12: ancilla[7],
13: ancilla[8],
}
backend = FakeMelbourne()
qc_b = transpile(qc, backend, initial_layout=initial_layout, optimization_level=level)
self.assertEqual(qc_b._layout.initial_layout._p2v, final_layout)
output_qr = qc_b.qregs[0]
for instruction in qc_b:
if instruction.operation.name == "cx":
for qubit in instruction.qubits:
self.assertIn(qubit, [output_qr[11], output_qr[3]])
@data(0, 1, 2, 3)
def test_layout_2503(self, level):
"""Test that a user-given initial layout is respected,
even if cnots are not in the coupling map.
See: https://github.com/Qiskit/qiskit-terra/issues/2503
"""
# build a circuit which works as-is on the coupling map, using the initial layout
qr = QuantumRegister(3, "q")
cr = ClassicalRegister(2)
ancilla = QuantumRegister(17, "ancilla")
qc = QuantumCircuit(qr, cr)
qc.append(U3Gate(0.1, 0.2, 0.3), [qr[0]])
qc.append(U2Gate(0.4, 0.5), [qr[2]])
qc.barrier()
qc.cx(qr[0], qr[2])
initial_layout = [6, 7, 12]
final_layout = {
0: ancilla[0],
1: ancilla[1],
2: ancilla[2],
3: ancilla[3],
4: ancilla[4],
5: ancilla[5],
6: qr[0],
7: qr[1],
8: ancilla[6],
9: ancilla[7],
10: ancilla[8],
11: ancilla[9],
12: qr[2],
13: ancilla[10],
14: ancilla[11],
15: ancilla[12],
16: ancilla[13],
17: ancilla[14],
18: ancilla[15],
19: ancilla[16],
}
backend = FakePoughkeepsie()
qc_b = transpile(qc, backend, initial_layout=initial_layout, optimization_level=level)
self.assertEqual(qc_b._layout.initial_layout._p2v, final_layout)
output_qr = qc_b.qregs[0]
self.assertIsInstance(qc_b[0].operation, U3Gate)
self.assertEqual(qc_b[0].qubits[0], output_qr[6])
self.assertIsInstance(qc_b[1].operation, U2Gate)
self.assertEqual(qc_b[1].qubits[0], output_qr[12])
@ddt
class TestFinalLayouts(QiskitTestCase):
"""Test final layouts after preset transpilation"""
@data(0, 1, 2, 3)
def test_layout_tokyo_2845(self, level):
"""Test that final layout in tokyo #2845
See: https://github.com/Qiskit/qiskit-terra/issues/2845
"""
qr1 = QuantumRegister(3, "qr1")
qr2 = QuantumRegister(2, "qr2")
qc = QuantumCircuit(qr1, qr2)
qc.cx(qr1[0], qr1[1])
qc.cx(qr1[1], qr1[2])
qc.cx(qr1[2], qr2[0])
qc.cx(qr2[0], qr2[1])
trivial_layout = {
0: Qubit(QuantumRegister(3, "qr1"), 0),
1: Qubit(QuantumRegister(3, "qr1"), 1),
2: Qubit(QuantumRegister(3, "qr1"), 2),
3: Qubit(QuantumRegister(2, "qr2"), 0),
4: Qubit(QuantumRegister(2, "qr2"), 1),
5: Qubit(QuantumRegister(15, "ancilla"), 0),
6: Qubit(QuantumRegister(15, "ancilla"), 1),
7: Qubit(QuantumRegister(15, "ancilla"), 2),
8: Qubit(QuantumRegister(15, "ancilla"), 3),
9: Qubit(QuantumRegister(15, "ancilla"), 4),
10: Qubit(QuantumRegister(15, "ancilla"), 5),
11: Qubit(QuantumRegister(15, "ancilla"), 6),
12: Qubit(QuantumRegister(15, "ancilla"), 7),
13: Qubit(QuantumRegister(15, "ancilla"), 8),
14: Qubit(QuantumRegister(15, "ancilla"), 9),
15: Qubit(QuantumRegister(15, "ancilla"), 10),
16: Qubit(QuantumRegister(15, "ancilla"), 11),
17: Qubit(QuantumRegister(15, "ancilla"), 12),
18: Qubit(QuantumRegister(15, "ancilla"), 13),
19: Qubit(QuantumRegister(15, "ancilla"), 14),
}
vf2_layout = {
0: Qubit(QuantumRegister(15, "ancilla"), 0),
1: Qubit(QuantumRegister(15, "ancilla"), 1),
2: Qubit(QuantumRegister(15, "ancilla"), 2),
3: Qubit(QuantumRegister(15, "ancilla"), 3),
4: Qubit(QuantumRegister(15, "ancilla"), 4),
5: Qubit(QuantumRegister(15, "ancilla"), 5),
6: Qubit(QuantumRegister(3, "qr1"), 1),
7: Qubit(QuantumRegister(15, "ancilla"), 6),
8: Qubit(QuantumRegister(15, "ancilla"), 7),
9: Qubit(QuantumRegister(15, "ancilla"), 8),
10: Qubit(QuantumRegister(3, "qr1"), 0),
11: Qubit(QuantumRegister(3, "qr1"), 2),
12: Qubit(QuantumRegister(15, "ancilla"), 9),
13: Qubit(QuantumRegister(15, "ancilla"), 10),
14: Qubit(QuantumRegister(15, "ancilla"), 11),
15: Qubit(QuantumRegister(15, "ancilla"), 12),
16: Qubit(QuantumRegister(2, "qr2"), 0),
17: Qubit(QuantumRegister(2, "qr2"), 1),
18: Qubit(QuantumRegister(15, "ancilla"), 13),
19: Qubit(QuantumRegister(15, "ancilla"), 14),
}
# Trivial layout
expected_layout_level0 = trivial_layout
# Dense layout
expected_layout_level1 = vf2_layout
# CSP layout
expected_layout_level2 = vf2_layout
expected_layout_level3 = vf2_layout
expected_layouts = [
expected_layout_level0,
expected_layout_level1,
expected_layout_level2,
expected_layout_level3,
]
backend = FakeTokyo()
result = transpile(qc, backend, optimization_level=level, seed_transpiler=42)
self.assertEqual(result._layout.initial_layout._p2v, expected_layouts[level])
@data(0, 1, 2, 3)
def test_layout_tokyo_fully_connected_cx(self, level):
"""Test that final layout in tokyo in a fully connected circuit"""
qr = QuantumRegister(5, "qr")
qc = QuantumCircuit(qr)
for qubit_target in qr:
for qubit_control in qr:
if qubit_control != qubit_target:
qc.cx(qubit_control, qubit_target)
ancilla = QuantumRegister(15, "ancilla")
trivial_layout = {
0: qr[0],
1: qr[1],
2: qr[2],
3: qr[3],
4: qr[4],
5: ancilla[0],
6: ancilla[1],
7: ancilla[2],
8: ancilla[3],
9: ancilla[4],
10: ancilla[5],
11: ancilla[6],
12: ancilla[7],
13: ancilla[8],
14: ancilla[9],
15: ancilla[10],
16: ancilla[11],
17: ancilla[12],
18: ancilla[13],
19: ancilla[14],
}
sabre_layout = {
0: ancilla[0],
1: qr[4],
2: ancilla[1],
3: ancilla[2],
4: ancilla[3],
5: qr[1],
6: qr[0],
7: ancilla[4],
8: ancilla[5],
9: ancilla[6],
10: qr[2],
11: qr[3],
12: ancilla[7],
13: ancilla[8],
14: ancilla[9],
15: ancilla[10],
16: ancilla[11],
17: ancilla[12],
18: ancilla[13],
19: ancilla[14],
}
sabre_layout_lvl_2 = {
0: ancilla[0],
1: qr[4],
2: ancilla[1],
3: ancilla[2],
4: ancilla[3],
5: qr[1],
6: qr[0],
7: ancilla[4],
8: ancilla[5],
9: ancilla[6],
10: qr[2],
11: qr[3],
12: ancilla[7],
13: ancilla[8],
14: ancilla[9],
15: ancilla[10],
16: ancilla[11],
17: ancilla[12],
18: ancilla[13],
19: ancilla[14],
}
sabre_layout_lvl_3 = {
0: ancilla[0],
1: qr[4],
2: ancilla[1],
3: ancilla[2],
4: ancilla[3],
5: qr[1],
6: qr[0],
7: ancilla[4],
8: ancilla[5],
9: ancilla[6],
10: qr[2],
11: qr[3],
12: ancilla[7],
13: ancilla[8],
14: ancilla[9],
15: ancilla[10],
16: ancilla[11],
17: ancilla[12],
18: ancilla[13],
19: ancilla[14],
}
expected_layout_level0 = trivial_layout
expected_layout_level1 = sabre_layout
expected_layout_level2 = sabre_layout_lvl_2
expected_layout_level3 = sabre_layout_lvl_3
expected_layouts = [
expected_layout_level0,
expected_layout_level1,
expected_layout_level2,
expected_layout_level3,
]
backend = FakeTokyo()
result = transpile(qc, backend, optimization_level=level, seed_transpiler=42)
self.assertEqual(result._layout.initial_layout._p2v, expected_layouts[level])
@data(0, 1, 2, 3)
def test_all_levels_use_trivial_if_perfect(self, level):
"""Test that we always use trivial if it's a perfect match.
See: https://github.com/Qiskit/qiskit-terra/issues/5694 for more
details
"""
backend = FakeTokyo()
config = backend.configuration()
rows = [x[0] for x in config.coupling_map]
cols = [x[1] for x in config.coupling_map]
adjacency_matrix = np.zeros((20, 20))
adjacency_matrix[rows, cols] = 1
qc = GraphState(adjacency_matrix)
qc.measure_all()
expected = {
0: Qubit(QuantumRegister(20, "q"), 0),
1: Qubit(QuantumRegister(20, "q"), 1),
2: Qubit(QuantumRegister(20, "q"), 2),
3: Qubit(QuantumRegister(20, "q"), 3),
4: Qubit(QuantumRegister(20, "q"), 4),
5: Qubit(QuantumRegister(20, "q"), 5),
6: Qubit(QuantumRegister(20, "q"), 6),
7: Qubit(QuantumRegister(20, "q"), 7),
8: Qubit(QuantumRegister(20, "q"), 8),
9: Qubit(QuantumRegister(20, "q"), 9),
10: Qubit(QuantumRegister(20, "q"), 10),
11: Qubit(QuantumRegister(20, "q"), 11),
12: Qubit(QuantumRegister(20, "q"), 12),
13: Qubit(QuantumRegister(20, "q"), 13),
14: Qubit(QuantumRegister(20, "q"), 14),
15: Qubit(QuantumRegister(20, "q"), 15),
16: Qubit(QuantumRegister(20, "q"), 16),
17: Qubit(QuantumRegister(20, "q"), 17),
18: Qubit(QuantumRegister(20, "q"), 18),
19: Qubit(QuantumRegister(20, "q"), 19),
}
trans_qc = transpile(qc, backend, optimization_level=level, seed_transpiler=42)
self.assertEqual(trans_qc._layout.initial_layout._p2v, expected)
@data(0)
def test_trivial_layout(self, level):
"""Test that trivial layout is preferred in level 0
See: https://github.com/Qiskit/qiskit-terra/pull/3657#pullrequestreview-342012465
"""
qr = QuantumRegister(10, "qr")
qc = QuantumCircuit(qr)
qc.cx(qr[0], qr[1])
qc.cx(qr[1], qr[2])
qc.cx(qr[2], qr[6])
qc.cx(qr[3], qr[8])
qc.cx(qr[4], qr[9])
qc.cx(qr[9], qr[8])
qc.cx(qr[8], qr[7])
qc.cx(qr[7], qr[6])
qc.cx(qr[6], qr[5])
qc.cx(qr[5], qr[0])
ancilla = QuantumRegister(10, "ancilla")
trivial_layout = {
0: qr[0],
1: qr[1],
2: qr[2],
3: qr[3],
4: qr[4],
5: qr[5],
6: qr[6],
7: qr[7],
8: qr[8],
9: qr[9],
10: ancilla[0],
11: ancilla[1],
12: ancilla[2],
13: ancilla[3],
14: ancilla[4],
15: ancilla[5],
16: ancilla[6],
17: ancilla[7],
18: ancilla[8],
19: ancilla[9],
}
expected_layouts = [trivial_layout, trivial_layout]
backend = FakeTokyo()
result = transpile(qc, backend, optimization_level=level, seed_transpiler=42)
self.assertEqual(result._layout.initial_layout._p2v, expected_layouts[level])
@data(0, 1, 2, 3)
def test_initial_layout(self, level):
"""When a user provides a layout (initial_layout), it should be used."""
qr = QuantumRegister(10, "qr")
qc = QuantumCircuit(qr)
qc.cx(qr[0], qr[1])
qc.cx(qr[1], qr[2])
qc.cx(qr[2], qr[3])
qc.cx(qr[3], qr[9])
qc.cx(qr[4], qr[9])
qc.cx(qr[9], qr[8])
qc.cx(qr[8], qr[7])
qc.cx(qr[7], qr[6])
qc.cx(qr[6], qr[5])
qc.cx(qr[5], qr[0])
initial_layout = {
0: qr[0],
2: qr[1],
4: qr[2],
6: qr[3],
8: qr[4],
10: qr[5],
12: qr[6],
14: qr[7],
16: qr[8],
18: qr[9],
}
backend = FakeTokyo()
result = transpile(
qc, backend, optimization_level=level, initial_layout=initial_layout, seed_transpiler=42
)
for physical, virtual in initial_layout.items():
self.assertEqual(result._layout.initial_layout._p2v[physical], virtual)
@ddt
class TestTranspileLevelsSwap(QiskitTestCase):
"""Test if swap is in the basis, do not unroll
See https://github.com/Qiskit/qiskit-terra/pull/3963
The circuit in combine should require a swap and that swap should exit at the end
for the transpilation"""
@combine(
circuit=[circuit_2532],
level=[0, 1, 2, 3],
dsc="circuit: {circuit.__name__}, level: {level}",
name="{circuit.__name__}_level{level}",
)
def test_1(self, circuit, level):
"""Simple coupling map (linear 5 qubits)."""
basis = ["u1", "u2", "cx", "swap"]
coupling_map = CouplingMap([(0, 1), (1, 2), (2, 3), (3, 4)])
result = transpile(
circuit(),
optimization_level=level,
basis_gates=basis,
coupling_map=coupling_map,
seed_transpiler=42,
initial_layout=[0, 1, 2, 3, 4],
)
self.assertIsInstance(result, QuantumCircuit)
resulting_basis = {node.name for node in circuit_to_dag(result).op_nodes()}
self.assertIn("swap", resulting_basis)
# Skipping optimization level 3 because the swap gates get absorbed into
# a unitary block as part of the KAK decompostion optimization passes and
# optimized away.
@combine(
level=[0, 1, 2],
dsc="If swap in basis, do not decompose it. level: {level}",
name="level{level}",
)
def test_2(self, level):
"""Simple coupling map (linear 5 qubits).
The circuit requires a swap and that swap should exit at the end
for the transpilation"""
basis = ["u1", "u2", "cx", "swap"]
circuit = QuantumCircuit(5)
circuit.cx(0, 4)
circuit.cx(1, 4)
circuit.cx(2, 4)
circuit.cx(3, 4)
coupling_map = CouplingMap([(0, 1), (1, 2), (2, 3), (3, 4)])
result = transpile(
circuit,
optimization_level=level,
basis_gates=basis,
coupling_map=coupling_map,
seed_transpiler=421234242,
)
self.assertIsInstance(result, QuantumCircuit)
resulting_basis = {node.name for node in circuit_to_dag(result).op_nodes()}
self.assertIn("swap", resulting_basis)
@ddt
class TestOptimizationWithCondition(QiskitTestCase):
"""Test optimization levels with condition in the circuit"""
@data(0, 1, 2, 3)
def test_optimization_condition(self, level):
"""Test optimization levels with condition in the circuit"""
qr = QuantumRegister(2)
cr = ClassicalRegister(1)
qc = QuantumCircuit(qr, cr)
qc.cx(0, 1).c_if(cr, 1)
backend = FakeJohannesburg()
circ = transpile(qc, backend, optimization_level=level)
self.assertIsInstance(circ, QuantumCircuit)
def test_input_dag_copy(self):
"""Test substitute_node_with_dag input_dag copy on condition"""
qc = QuantumCircuit(2, 1)
qc.cx(0, 1).c_if(qc.cregs[0], 1)
qc.cx(1, 0)
circ = transpile(qc, basis_gates=["u3", "cz"])
self.assertIsInstance(circ, QuantumCircuit)
@ddt
class TestOptimizationOnSize(QiskitTestCase):
"""Test the optimization levels for optimization based on
both size and depth of the circuit.
See https://github.com/Qiskit/qiskit-terra/pull/7542
"""
@data(2, 3)
def test_size_optimization(self, level):
"""Test the levels for optimization based on size of circuit"""
qc = QuantumCircuit(8)
qc.cx(1, 2)
qc.cx(2, 3)
qc.cx(5, 4)
qc.cx(6, 5)
qc.cx(4, 5)
qc.cx(3, 4)
qc.cx(5, 6)
qc.cx(5, 4)
qc.cx(3, 4)
qc.cx(2, 3)
qc.cx(1, 2)
qc.cx(6, 7)
qc.cx(6, 5)
qc.cx(5, 4)
qc.cx(7, 6)
qc.cx(6, 7)
circ = transpile(qc, optimization_level=level).decompose()
circ_data = circ.data
free_qubits = {0, 1, 2, 3}
# ensure no gates are using qubits - [0,1,2,3]
for gate in circ_data:
indices = {circ.find_bit(qubit).index for qubit in gate.qubits}
common = indices.intersection(free_qubits)
for common_qubit in common:
self.assertTrue(common_qubit not in free_qubits)
self.assertLess(circ.size(), qc.size())
self.assertLessEqual(circ.depth(), qc.depth())
@ddt
class TestGeenratePresetPassManagers(QiskitTestCase):
"""Test generate_preset_pass_manager function."""
@data(0, 1, 2, 3)
def test_with_backend(self, optimization_level):
"""Test a passmanager is constructed when only a backend and optimization level."""
target = FakeTokyo()
pm = generate_preset_pass_manager(optimization_level, target)
self.assertIsInstance(pm, PassManager)
@data(0, 1, 2, 3)
def test_with_no_backend(self, optimization_level):
"""Test a passmanager is constructed with no backend and optimization level."""
target = FakeLagosV2()
pm = generate_preset_pass_manager(
optimization_level,
coupling_map=target.coupling_map,
basis_gates=target.operation_names,
inst_map=target.instruction_schedule_map,
instruction_durations=target.instruction_durations,
timing_constraints=target.target.timing_constraints(),
target=target.target,
)
self.assertIsInstance(pm, PassManager)
@data(0, 1, 2, 3)
def test_with_no_backend_only_target(self, optimization_level):
"""Test a passmanager is constructed with a manual target and optimization level."""
target = FakeLagosV2()
pm = generate_preset_pass_manager(optimization_level, target=target.target)
self.assertIsInstance(pm, PassManager)
def test_invalid_optimization_level(self):
"""Assert we fail with an invalid optimization_level."""
with self.assertRaises(ValueError):
generate_preset_pass_manager(42)
@unittest.mock.patch.object(
level2.PassManagerStagePluginManager,
"get_passmanager_stage",
wraps=mock_get_passmanager_stage,
)
def test_backend_with_custom_stages_level2(self, _plugin_manager_mock):
"""Test generated preset pass manager includes backend specific custom stages."""
optimization_level = 2
class TargetBackend(FakeLagosV2):
"""Fake lagos subclass with custom transpiler stages."""
def get_scheduling_stage_plugin(self):
"""Custom scheduling stage."""
return "custom_stage_for_test"
def get_translation_stage_plugin(self):
"""Custom post translation stage."""
return "custom_stage_for_test"
target = TargetBackend()
pm = generate_preset_pass_manager(optimization_level, backend=target)
self.assertIsInstance(pm, PassManager)
pass_list = [y.__class__.__name__ for x in pm.passes() for y in x["passes"]]
self.assertIn("PadDynamicalDecoupling", pass_list)
self.assertIn("ALAPScheduleAnalysis", pass_list)
post_translation_pass_list = [
y.__class__.__name__
for x in pm.translation.passes() # pylint: disable=no-member
for y in x["passes"]
]
self.assertIn("RemoveResetInZeroState", post_translation_pass_list)
@unittest.mock.patch.object(
level1.PassManagerStagePluginManager,
"get_passmanager_stage",
wraps=mock_get_passmanager_stage,
)
def test_backend_with_custom_stages_level1(self, _plugin_manager_mock):
"""Test generated preset pass manager includes backend specific custom stages."""
optimization_level = 1
class TargetBackend(FakeLagosV2):
"""Fake lagos subclass with custom transpiler stages."""
def get_scheduling_stage_plugin(self):
"""Custom scheduling stage."""
return "custom_stage_for_test"
def get_translation_stage_plugin(self):
"""Custom post translation stage."""
return "custom_stage_for_test"
target = TargetBackend()
pm = generate_preset_pass_manager(optimization_level, backend=target)
self.assertIsInstance(pm, PassManager)
pass_list = [y.__class__.__name__ for x in pm.passes() for y in x["passes"]]
self.assertIn("PadDynamicalDecoupling", pass_list)
self.assertIn("ALAPScheduleAnalysis", pass_list)
post_translation_pass_list = [
y.__class__.__name__
for x in pm.translation.passes() # pylint: disable=no-member
for y in x["passes"]
]
self.assertIn("RemoveResetInZeroState", post_translation_pass_list)
@unittest.mock.patch.object(
level3.PassManagerStagePluginManager,
"get_passmanager_stage",
wraps=mock_get_passmanager_stage,
)
def test_backend_with_custom_stages_level3(self, _plugin_manager_mock):
"""Test generated preset pass manager includes backend specific custom stages."""
optimization_level = 3
class TargetBackend(FakeLagosV2):
"""Fake lagos subclass with custom transpiler stages."""
def get_scheduling_stage_plugin(self):
"""Custom scheduling stage."""
return "custom_stage_for_test"
def get_translation_stage_plugin(self):
"""Custom post translation stage."""
return "custom_stage_for_test"
target = TargetBackend()
pm = generate_preset_pass_manager(optimization_level, backend=target)
self.assertIsInstance(pm, PassManager)
pass_list = [y.__class__.__name__ for x in pm.passes() for y in x["passes"]]
self.assertIn("PadDynamicalDecoupling", pass_list)
self.assertIn("ALAPScheduleAnalysis", pass_list)
post_translation_pass_list = [
y.__class__.__name__
for x in pm.translation.passes() # pylint: disable=no-member
for y in x["passes"]
]
self.assertIn("RemoveResetInZeroState", post_translation_pass_list)
@unittest.mock.patch.object(
level0.PassManagerStagePluginManager,
"get_passmanager_stage",
wraps=mock_get_passmanager_stage,
)
def test_backend_with_custom_stages_level0(self, _plugin_manager_mock):
"""Test generated preset pass manager includes backend specific custom stages."""
optimization_level = 0
class TargetBackend(FakeLagosV2):
"""Fake lagos subclass with custom transpiler stages."""
def get_scheduling_stage_plugin(self):
"""Custom scheduling stage."""
return "custom_stage_for_test"
def get_translation_stage_plugin(self):
"""Custom post translation stage."""
return "custom_stage_for_test"
target = TargetBackend()
pm = generate_preset_pass_manager(optimization_level, backend=target)
self.assertIsInstance(pm, PassManager)
pass_list = [y.__class__.__name__ for x in pm.passes() for y in x["passes"]]
self.assertIn("PadDynamicalDecoupling", pass_list)
self.assertIn("ALAPScheduleAnalysis", pass_list)
post_translation_pass_list = [
y.__class__.__name__
for x in pm.translation.passes() # pylint: disable=no-member
for y in x["passes"]
]
self.assertIn("RemoveResetInZeroState", post_translation_pass_list)
@ddt
class TestIntegrationControlFlow(QiskitTestCase):
"""Integration tests for control-flow circuits through the preset pass managers."""
@data(0, 1, 2, 3)
def test_default_compilation(self, optimization_level):
"""Test that a simple circuit with each type of control-flow passes a full transpilation
pipeline with the defaults."""
class CustomCX(Gate):
"""Custom CX"""
def __init__(self):
super().__init__("custom_cx", 2, [])
def _define(self):
self._definition = QuantumCircuit(2)
self._definition.cx(0, 1)
circuit = QuantumCircuit(6, 1)
circuit.h(0)
circuit.measure(0, 0)
circuit.cx(0, 1)
circuit.cz(0, 2)
circuit.append(CustomCX(), [1, 2], [])
with circuit.for_loop((1,)):
circuit.cx(0, 1)
circuit.cz(0, 2)
circuit.append(CustomCX(), [1, 2], [])
with circuit.if_test((circuit.clbits[0], True)) as else_:
circuit.cx(0, 1)
circuit.cz(0, 2)
circuit.append(CustomCX(), [1, 2], [])
with else_:
circuit.cx(3, 4)
circuit.cz(3, 5)
circuit.append(CustomCX(), [4, 5], [])
with circuit.while_loop((circuit.clbits[0], True)):
circuit.cx(3, 4)
circuit.cz(3, 5)
circuit.append(CustomCX(), [4, 5], [])
coupling_map = CouplingMap.from_line(6)
transpiled = transpile(
circuit,
basis_gates=["sx", "rz", "cx", "if_else", "for_loop", "while_loop"],
coupling_map=coupling_map,
optimization_level=optimization_level,
seed_transpiler=2022_10_04,
)
# Tests of the complete validity of a circuit are mostly done at the indiviual pass level;
# here we're just checking that various passes do appear to have run.
self.assertIsInstance(transpiled, QuantumCircuit)
# Assert layout ran.
self.assertIsNot(getattr(transpiled, "_layout", None), None)
def _visit_block(circuit, stack=None):
"""Assert that every block contains at least one swap to imply that routing has run."""
if stack is None:
# List of (instruction_index, block_index).
stack = ()
seen_cx = 0
for i, instruction in enumerate(circuit):
if isinstance(instruction.operation, ControlFlowOp):
for j, block in enumerate(instruction.operation.blocks):
_visit_block(block, stack + ((i, j),))
elif isinstance(instruction.operation, CXGate):
seen_cx += 1
# Assert unrolling ran.
self.assertNotIsInstance(instruction.operation, CustomCX)
# Assert translation ran.
self.assertNotIsInstance(instruction.operation, CZGate)
# There are three "natural" swaps in each block (one for each 2q operation), so if
# routing ran, we should see more than that.
self.assertGreater(seen_cx, 3, msg=f"no swaps in block at indices: {stack}")
# Assert routing ran.
_visit_block(transpiled)
@data(0, 1, 2, 3)
def test_allow_overriding_defaults(self, optimization_level):
"""Test that the method options can be overridden."""
circuit = QuantumCircuit(3, 1)
circuit.h(0)
circuit.measure(0, 0)
with circuit.for_loop((1,)):
circuit.h(0)
circuit.cx(0, 1)
circuit.cz(0, 2)
circuit.cx(1, 2)
coupling_map = CouplingMap.from_line(3)
calls = set()
def callback(pass_, **_):
calls.add(pass_.name())
transpiled = transpile(
circuit,
basis_gates=["u3", "cx", "if_else", "for_loop", "while_loop"],
layout_method="trivial",
translation_method="unroller",
coupling_map=coupling_map,
optimization_level=optimization_level,
seed_transpiler=2022_10_04,
callback=callback,
)
self.assertIsInstance(transpiled, QuantumCircuit)
self.assertIsNot(getattr(transpiled, "_layout", None), None)
self.assertIn("TrivialLayout", calls)
self.assertIn("Unroller", calls)
self.assertNotIn("DenseLayout", calls)
self.assertNotIn("SabreLayout", calls)
self.assertNotIn("BasisTranslator", calls)
@data(0, 1, 2, 3)
def test_invalid_methods_raise_on_control_flow(self, optimization_level):
"""Test that trying to use an invalid method with control flow fails."""
qc = QuantumCircuit(1)
with qc.for_loop((1,)):
qc.x(0)
with self.assertRaisesRegex(TranspilerError, "Got routing_method="):
transpile(qc, routing_method="lookahead", optimization_level=optimization_level)
with self.assertRaisesRegex(TranspilerError, "Got scheduling_method="):
transpile(qc, scheduling_method="alap", optimization_level=optimization_level)
@data(0, 1, 2, 3)
def test_unsupported_basis_gates_raise(self, optimization_level):
"""Test that trying to transpile a control-flow circuit for a backend that doesn't support
the necessary operations in its `basis_gates` will raise a sensible error."""
backend = FakeTokyo()
qc = QuantumCircuit(1, 1)
with qc.for_loop((0,)):
pass
with self.assertRaisesRegex(TranspilerError, "The control-flow construct.*not supported"):
transpile(qc, backend, optimization_level=optimization_level)
qc = QuantumCircuit(1, 1)
with qc.if_test((qc.clbits[0], False)):
pass
with self.assertRaisesRegex(TranspilerError, "The control-flow construct.*not supported"):
transpile(qc, backend, optimization_level=optimization_level)
qc = QuantumCircuit(1, 1)
with qc.while_loop((qc.clbits[0], False)):
pass
with qc.for_loop((0, 1, 2)):
pass
with self.assertRaisesRegex(TranspilerError, "The control-flow construct.*not supported"):
transpile(qc, backend, optimization_level=optimization_level)
@data(0, 1, 2, 3)
def test_unsupported_targets_raise(self, optimization_level):
"""Test that trying to transpile a control-flow circuit for a backend that doesn't support
the necessary operations in its `Target` will raise a more sensible error."""
target = Target(num_qubits=2)
target.add_instruction(CXGate(), {(0, 1): None})
qc = QuantumCircuit(1, 1)
with qc.for_loop((0,)):
pass
with self.assertRaisesRegex(TranspilerError, "The control-flow construct.*not supported"):
transpile(qc, target=target, optimization_level=optimization_level)
qc = QuantumCircuit(1, 1)
with qc.if_test((qc.clbits[0], False)):
pass
with self.assertRaisesRegex(TranspilerError, "The control-flow construct.*not supported"):
transpile(qc, target=target, optimization_level=optimization_level)
qc = QuantumCircuit(1, 1)
with qc.while_loop((qc.clbits[0], False)):
pass
with qc.for_loop((0, 1, 2)):
pass
with self.assertRaisesRegex(TranspilerError, "The control-flow construct.*not supported"):
transpile(qc, target=target, optimization_level=optimization_level)
|
https://github.com/qiskit-community/qiskit-translations-staging
|
qiskit-community
|
from qiskit import pulse
dc = pulse.DriveChannel
d0, d1, d2, d3, d4 = dc(0), dc(1), dc(2), dc(3), dc(4)
with pulse.build(name='pulse_programming_in') as pulse_prog:
pulse.play([1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1], d0)
pulse.play([1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0], d1)
pulse.play([1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0], d2)
pulse.play([1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0], d3)
pulse.play([1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0], d4)
pulse_prog.draw()
|
https://github.com/Qubico-Hack/tutorials
|
Qubico-Hack
|
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import clear_output
!pip install qiskit[all]
from qiskit import QuantumCircuit
from qiskit.circuit import Parameter
from qiskit.circuit.library import RealAmplitudes, ZZFeatureMap
!pip install qiskit_algorithms
from qiskit_algorithms.optimizers import COBYLA,L_BFGS_B
from qiskit_algorithms.utils import algorithm_globals
!pip install qiskit_machine_learning
from qiskit_machine_learning.algorithms.classifiers import NeuralNetworkClassifier, VQC
from qiskit_machine_learning.algorithms.regressors import NeuralNetworkRegressor, VQR
from qiskit_machine_learning.neural_networks import SamplerQNN, EstimatorQNN
from qiskit_machine_learning.circuit.library import QNNCircuit
algorithm_globals.random_seed = 42
num_inputs = 2
num_samples = 20
X = 2 * algorithm_globals.random.random([num_samples, num_inputs]) - 1
y01 = 1 * (np.sum(X, axis=1) >= 0) # in { 0, 1}
y = 2 * y01 - 1 # in {-1, +1}
y_one_hot = np.zeros((num_samples, 2))
for i in range(num_samples):
y_one_hot[i, y01[i]] = 1
for x, y_target in zip(X, y):
if y_target == 1:
plt.plot(x[0], x[1], "bo")
else:
plt.plot(x[0], x[1], "go")
plt.plot([-1, 1], [1, -1], "--", color="black")
plt.show()
# construct QNN with the QNNCircuit's default ZZFeatureMap feature map and RealAmplitudes ansatz.
qc = QNNCircuit(num_qubits=2)
qc.draw(output="mpl")
estimator_qnn = EstimatorQNN(circuit=qc)
# QNN maps inputs to [-1, +1]
estimator_qnn.forward(X[0, :], algorithm_globals.random.random(estimator_qnn.num_weights))
# callback function that draws a live plot when the .fit() method is called
def callback_graph(weights, obj_func_eval):
clear_output(wait=True)
objective_func_vals.append(obj_func_eval)
plt.title("Objective function value against iteration")
plt.xlabel("Iteration")
plt.ylabel("Objective function value")
plt.plot(range(len(objective_func_vals)), objective_func_vals)
plt.show()
# construct neural network classifier
estimator_classifier = NeuralNetworkClassifier(
estimator_qnn, optimizer=COBYLA(maxiter=60), callback=callback_graph
)
# create empty array for callback to store evaluations of the objective function
objective_func_vals = []
plt.rcParams["figure.figsize"] = (12, 6)
# fit classifier to data
estimator_classifier.fit(X, y)
# return to default figsize
plt.rcParams["figure.figsize"] = (6, 4)
# score classifier
estimator_classifier.score(X, y)
# evaluate data points
y_predict = estimator_classifier.predict(X)
# plot results
# red == wrongly classified
for x, y_target, y_p in zip(X, y, y_predict):
if y_target == 1:
plt.plot(x[0], x[1], "bo")
else:
plt.plot(x[0], x[1], "go")
if y_target != y_p:
plt.scatter(x[0], x[1], s=200, facecolors="none", edgecolors="r", linewidths=2)
plt.plot([-1, 1], [1, -1], "--", color="black")
plt.show()
estimator_classifier.weights
# construct a quantum circuit from the default ZZFeatureMap feature map and a customized RealAmplitudes ansatz
qc = QNNCircuit(ansatz=RealAmplitudes(num_inputs, reps=1))
qc.draw(output="mpl")
# parity maps bitstrings to 0 or 1
def parity(x):
return "{:b}".format(x).count("1") % 2
output_shape = 2 # corresponds to the number of classes, possible outcomes of the (parity) mapping.
# construct QNN
sampler_qnn = SamplerQNN(
circuit=qc,
interpret=parity,
output_shape=output_shape,
)
# construct classifier
sampler_classifier = NeuralNetworkClassifier(
neural_network=sampler_qnn, optimizer=COBYLA(maxiter=30), callback=callback_graph
)
# create empty array for callback to store evaluations of the objective function
objective_func_vals = []
plt.rcParams["figure.figsize"] = (12, 6)
# fit classifier to data
sampler_classifier.fit(X, y01)
# return to default figsize
plt.rcParams["figure.figsize"] = (6, 4)
# score classifier
sampler_classifier.score(X, y01)
# evaluate data points
y_predict = sampler_classifier.predict(X)
# plot results
# red == wrongly classified
for x, y_target, y_p in zip(X, y01, y_predict):
if y_target == 1:
plt.plot(x[0], x[1], "bo")
else:
plt.plot(x[0], x[1], "go")
if y_target != y_p:
plt.scatter(x[0], x[1], s=200, facecolors="none", edgecolors="r", linewidths=2)
plt.plot([-1, 1], [1, -1], "--", color="black")
plt.show()
sampler_classifier.weights
# construct feature map, ansatz, and optimizer
feature_map = ZZFeatureMap(num_inputs)
ansatz = RealAmplitudes(num_inputs, reps=1)
# construct variational quantum classifier
vqc = VQC(
feature_map=feature_map,
ansatz=ansatz,
loss="cross_entropy",
optimizer=COBYLA(maxiter=30),
callback=callback_graph,
)
# create empty array for callback to store evaluations of the objective function
objective_func_vals = []
plt.rcParams["figure.figsize"] = (12, 6)
# fit classifier to data
vqc.fit(X, y_one_hot)
# return to default figsize
plt.rcParams["figure.figsize"] = (6, 4)
# score classifier
vqc.score(X, y_one_hot)
# evaluate data points
y_predict = vqc.predict(X)
# plot results
# red == wrongly classified
for x, y_target, y_p in zip(X, y_one_hot, y_predict):
if y_target[0] == 1:
plt.plot(x[0], x[1], "bo")
else:
plt.plot(x[0], x[1], "go")
if not np.all(y_target == y_p):
plt.scatter(x[0], x[1], s=200, facecolors="none", edgecolors="r", linewidths=2)
plt.plot([-1, 1], [1, -1], "--", color="black")
plt.show()
from sklearn.datasets import make_classification
from sklearn.preprocessing import MinMaxScaler
X, y = make_classification(
n_samples=10,
n_features=2,
n_classes=3,
n_redundant=0,
n_clusters_per_class=1,
class_sep=2.0,
random_state=algorithm_globals.random_seed,
)
X = MinMaxScaler().fit_transform(X)
plt.scatter(X[:, 0], X[:, 1], c=y)
y_cat = np.empty(y.shape, dtype=str)
y_cat[y == 0] = "A"
y_cat[y == 1] = "B"
y_cat[y == 2] = "C"
print(y_cat)
vqc = VQC(
num_qubits=2,
optimizer=COBYLA(maxiter=30),
callback=callback_graph,
)
# create empty array for callback to store evaluations of the objective function
objective_func_vals = []
plt.rcParams["figure.figsize"] = (12, 6)
# fit classifier to data
vqc.fit(X, y_cat)
# return to default figsize
plt.rcParams["figure.figsize"] = (6, 4)
# score classifier
vqc.score(X, y_cat)
predict = vqc.predict(X)
print(f"Predicted labels: {predict}")
print(f"Ground truth: {y_cat}")
num_samples = 20
eps = 0.2
lb, ub = -np.pi, np.pi
X_ = np.linspace(lb, ub, num=50).reshape(50, 1)
f = lambda x: np.sin(x)
X = (ub - lb) * algorithm_globals.random.random([num_samples, 1]) + lb
y = f(X[:, 0]) + eps * (2 * algorithm_globals.random.random(num_samples) - 1)
plt.plot(X_, f(X_), "r--")
plt.plot(X, y, "bo")
plt.show()
# construct simple feature map
param_x = Parameter("x")
feature_map = QuantumCircuit(1, name="fm")
feature_map.ry(param_x, 0)
# construct simple ansatz
param_y = Parameter("y")
ansatz = QuantumCircuit(1, name="vf")
ansatz.ry(param_y, 0)
# construct a circuit
qc = QNNCircuit(feature_map=feature_map, ansatz=ansatz)
# construct QNN
regression_estimator_qnn = EstimatorQNN(circuit=qc)
# construct the regressor from the neural network
regressor = NeuralNetworkRegressor(
neural_network=regression_estimator_qnn,
loss="squared_error",
optimizer=L_BFGS_B(maxiter=5),
callback=callback_graph,
)
# create empty array for callback to store evaluations of the objective function
objective_func_vals = []
plt.rcParams["figure.figsize"] = (12, 6)
# fit to data
regressor.fit(X, y)
# return to default figsize
plt.rcParams["figure.figsize"] = (6, 4)
# score the result
regressor.score(X, y)
# plot target function
plt.plot(X_, f(X_), "r--")
# plot data
plt.plot(X, y, "bo")
# plot fitted line
y_ = regressor.predict(X_)
plt.plot(X_, y_, "g-")
plt.show()
regressor.weights
vqr = VQR(
feature_map=feature_map,
ansatz=ansatz,
optimizer=L_BFGS_B(maxiter=5),
callback=callback_graph,
)
# create empty array for callback to store evaluations of the objective function
objective_func_vals = []
plt.rcParams["figure.figsize"] = (12, 6)
# fit regressor
vqr.fit(X, y)
# return to default figsize
plt.rcParams["figure.figsize"] = (6, 4)
# score result
vqr.score(X, y)
# plot target function
plt.plot(X_, f(X_), "r--")
# plot data
plt.plot(X, y, "bo")
# plot fitted line
y_ = vqr.predict(X_)
plt.plot(X_, y_, "g-")
plt.show()
import qiskit.tools.jupyter
%qiskit_version_table
%qiskit_copyright
|
https://github.com/renatawong/classical-shadow-vqe
|
renatawong
|
# Installation of the requirements
#!python -m pip install -r requirements.txt
'''
(C) Renata Wong 2023
Qiskit code for testing fidelity of derandomised classical shadow on the ground state energy of molecules.
Procedure:
1. Derandomize the molecule-in-question's Hamiltonian.
2. Choose a variational ansatz with initial parameters selected at random.
3. Apply the derandomized Hamiltonian as basis change operators to the ansatz.
4. Measure the ansatz in the Pauli Z basis and store the results as a shadow.
5. Obtain the expectation value of the molecular Hamiltonian from the shadow.
6. Optimize for minimum Hamiltonian expectation value.
7. Feed the calculated angles/parameters back to the ansatz.
8. Repeat steps 3-7 till the optimization is completed.
9. Output the minimized expectation value of the molecular Hamiltonian and the mean-square-root-error.
Note: Below we perform calculations on the molecular Hamiltonian of H_2.
To perform calculations on other molecules, you will need to specify their geometry, charge and spin
to replace the values in the driver.
Note: predicting_quantum_properties module comes from https://github.com/hsinyuan-huang/predicting-quantum-properties
'''
from qiskit import QuantumCircuit, execute
from qiskit_aer import QasmSimulator
from qiskit_nature.units import DistanceUnit
from qiskit_nature.second_q.drivers import PySCFDriver
from qiskit_nature.second_q.mappers import BravyiKitaevMapper
from qiskit.algorithms.minimum_eigensolvers import NumPyMinimumEigensolver
from qiskit_nature.second_q.algorithms import GroundStateEigensolver
from qiskit.circuit.library import EfficientSU2
from qiskit.algorithms.optimizers import SLSQP, COBYLA, SPSA
import time
import numpy as np
from functools import partial
import matplotlib.pyplot as plt
from collections import Counter
from predicting_quantum_properties.prediction_shadow import estimate_exp
from modified_derandomization import modified_derandomized_classical_shadow
# handle deprecation issues
import qiskit_nature
qiskit_nature.settings.use_pauli_sum_op = False
import h5py
H5PY_DEFAULT_READONLY=1
# classically obtained ground state energy
EXPECTED_EIGENVALUE = -1.86
# specifying the geometry of the molecule in question
driver = PySCFDriver(
atom="H 0 0 0; H 0 0 0.735",
basis="sto3g",
charge=0,
spin=0,
unit=DistanceUnit.ANGSTROM,
)
problem = driver.run()
hamiltonian = problem.hamiltonian
# electronic Hamiltonian of the system
second_q_op = hamiltonian.second_q_op()
# The Bravyi-Kitaev repserentation of the Fermionic Hamiltonian
mapper = BravyiKitaevMapper()
bkencoded_hamiltonian = mapper.map(second_q_op)
print(bkencoded_hamiltonian)
def process_hamiltonian(hamiltonian, derandomize = False):
hamiltonian_observables = []
hamiltonian_coefficients = []
for observable in hamiltonian.paulis:
op_list = []
for op_index, pauli_op in enumerate(observable):
pauli_op = str(pauli_op)
if pauli_op == 'I' or pauli_op == 'X' or pauli_op == 'Y' or pauli_op == 'Z':
op_list.append((pauli_op, op_index))
hamiltonian_observables.append(op_list)
hamiltonian_coefficients = hamiltonian.coeffs.real
system_size = len(hamiltonian_observables[0])
# removing all occurrences of Pauli-I, for all-Pauli-I there is an empty list left
# these observables are needed for estimate_exp()
observables_xyze = []
for observable in hamiltonian_observables:
XYZE = []
for pauli in observable:
if pauli[0] != 'I':
XYZE.append(pauli)
observables_xyze.append(XYZE)
# derandomisation procedure requires that coefficients are non-negative
if derandomize == True:
absolute_coefficients = [abs(coeffcient) for coeffcient in hamiltonian_coefficients]
# removing the empty list as well
# these observables are needed for derandomisation procedure
observables_xyz = []
for idx, observable in enumerate(observables_xyze):
if observable:
observables_xyz.append(observable)
else:
absolute_coefficients.pop(idx)
return system_size, observables_xyze, observables_xyz, hamiltonian_coefficients, absolute_coefficients
return system_size, observables_xyze, hamiltonian_coefficients
# process the Hamiltonian to obtain properly formatted data
hamiltonian_data = process_hamiltonian(bkencoded_hamiltonian, derandomize = True)
system_size, observables_xyze, observables_xyz, hamiltonian_coefficients, absolute_coefficients = hamiltonian_data
#print('HAMILTONIAN\n', observables_xyze)
'''
VARIATIONAL ANSATZ
Note that for molecules other than H_2 you may need to specify a different number of reps.
'''
reps = 1
ansatz = EfficientSU2(system_size, su2_gates=['rx', 'ry'], entanglement='circular', reps=reps, skip_final_rotation_layer=False)
ansatz.decompose().draw('mpl')
'''
COST FUNCTION
'''
def basis_change_circuit(pauli_op):
# Generating circuit with just the basis change operators
#
# pauli_op: n-qubit Pauli operator
basis_change = QuantumCircuit(ansatz.num_qubits, ansatz.num_qubits)
for idx, op in enumerate(pauli_op):
if op == 'X':
basis_change.h(idx)
elif op == 'Y':
basis_change.h(idx)
basis_change.p(-np.pi/2, idx)
return basis_change
def min_cost():
return min(cost_history)
def log_cost(cost):
global cost_history
cost_history.append(cost)
def objective_function(operators, params):
backend = QasmSimulator(method='statevector', shots=1)
pauli_op_dict = Counter(tuple(x) for x in operators)
shadow = []
for pauli_op in pauli_op_dict:
qc = ansatz.bind_parameters(params)
qc = qc.compose(basis_change_circuit(pauli_op))
qc.measure(reversed(range(system_size)), range(system_size))
result = execute(qc, backend, shots=pauli_op_dict[pauli_op]).result()
counts = result.get_counts()
# generate a separate circuit for each basis change Pauli operator
output_str = list(list(counts.keys())[list(counts.values()).index(1)])
output = [int(i) for i in output_str]
eigenvals = [x+1 if x == 0 else x-2 for x in output]
snapshot = [(op, eigenval) for op, eigenval in zip(pauli_op, eigenvals)]
shadow.append(snapshot)
cost = 0.0
for term, weight in zip(observables_xyze, hamiltonian_coefficients):
sum_product, match_count = estimate_exp(shadow, term)
if match_count != 0:
exp_val = sum_product / match_count
cost += weight * exp_val
log_cost(cost)
return cost
'''
RUNNING EXPERIMENTS
'''
start_time = time.time()
rmse_errors = []
print('NUMBER OF OPERATORS | DERANDOMISED OPERATORS | AVERAGE RMSE ERROR\n')
measurement_range = [50, 100, 200, 600, 1000, 1600]
for num_operators in measurement_range:
derandomized_hamiltonian = modified_derandomized_classical_shadow(observables_xyz, num_operators,
system_size, weight=absolute_coefficients)
tuples = (tuple(pauli) for pauli in derandomized_hamiltonian)
counts = Counter(tuples)
optimizer = SPSA(maxiter=2000)
cost_function = partial(objective_function, derandomized_hamiltonian)
expectation_values = []
num_experiments = 5
for iteration in range(num_experiments):
cost_history = []
params = np.random.rand(ansatz.num_parameters)
result = optimizer.minimize(fun=cost_function, x0=params)
minimal_cost = min_cost()
expectation_values.append(minimal_cost)
print("EXPERIMENT {}: GROUND STATE ENERGY FOUND = {}".format(iteration, minimal_cost))
rmse_derandomised_cs = np.sqrt(np.sum([(expectation_values[i] - EXPECTED_EIGENVALUE)**2
for i in range(num_experiments)])/num_experiments)
rmse_errors.append(rmse_derandomised_cs)
print('{} | {} | {}'.format(num_operators, counts, rmse_derandomised_cs))
elapsed_time = time.time() - start_time
print("Execution time = ", time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))
points = measurement_range
num_points = len(measurement_range)
plt.plot([i for i in points], [rmse_errors[i] for i in range(num_points)], 'r', label='SPSA(maxiter=2000)')
plt.xlabel('Number of measurements')
plt.ylabel('Average RMSE error')
plt.legend(loc=1)
'''
Above we have assumed a particular ground state energy for the molecule of interest.
Below we corroborate this assumption using a classical minimum eigensolver on our Hamiltonian.
'''
converter = BravyiKitaevMapper()
numpy_solver = NumPyMinimumEigensolver()
calc = GroundStateEigensolver(converter, numpy_solver)
res = calc.solve(problem)
print('Electronic ground state energy:\n', res)
|
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 RealAmplitudes
from qiskit.quantum_info import Statevector
from qiskit.utils import algorithm_globals
from qiskit_machine_learning.circuit.library import RawFeatureVector
from qiskit_machine_learning.neural_networks import SamplerQNN
algorithm_globals.random_seed = 42
def ansatz(num_qubits):
return RealAmplitudes(num_qubits, reps=5)
num_qubits = 5
circ = ansatz(num_qubits)
circ.decompose().draw("mpl")
def auto_encoder_circuit(num_latent, num_trash):
qr = QuantumRegister(num_latent + 2 * num_trash + 1, "q")
cr = ClassicalRegister(1, "c")
circuit = QuantumCircuit(qr, cr)
circuit.compose(ansatz(num_latent + num_trash), range(0, num_latent + num_trash), inplace=True)
circuit.barrier()
auxiliary_qubit = num_latent + 2 * num_trash
# swap test
circuit.h(auxiliary_qubit)
for i in range(num_trash):
circuit.cswap(auxiliary_qubit, num_latent + i, num_latent + num_trash + i)
circuit.h(auxiliary_qubit)
circuit.measure(auxiliary_qubit, cr[0])
return circuit
num_latent = 3
num_trash = 2
circuit = auto_encoder_circuit(num_latent, num_trash)
circuit.draw("mpl")
def domain_wall(circuit, a, b):
# Here we place the Domain Wall to qubits a - b in our circuit
for i in np.arange(int(b / 2), int(b)):
circuit.x(i)
return circuit
domain_wall_circuit = domain_wall(QuantumCircuit(5), 0, 5)
domain_wall_circuit.draw("mpl")
ae = auto_encoder_circuit(num_latent, num_trash)
qc = QuantumCircuit(num_latent + 2 * num_trash + 1, 1)
qc = qc.compose(domain_wall_circuit, range(num_latent + num_trash))
qc = qc.compose(ae)
qc.draw("mpl")
# Here we define our interpret for our SamplerQNN
def identity_interpret(x):
return x
qnn = SamplerQNN(
circuit=qc,
input_params=[],
weight_params=ae.parameters,
interpret=identity_interpret,
output_shape=2,
)
def cost_func_domain(params_values):
probabilities = qnn.forward([], params_values)
# we pick a probability of getting 1 as the output of the network
cost = np.sum(probabilities[:, 1])
# plotting part
clear_output(wait=True)
objective_func_vals.append(cost)
plt.title("Objective function value against iteration")
plt.xlabel("Iteration")
plt.ylabel("Objective function value")
plt.plot(range(len(objective_func_vals)), objective_func_vals)
plt.show()
return cost
opt = COBYLA(maxiter=150)
initial_point = algorithm_globals.random.random(ae.num_parameters)
objective_func_vals = []
# make the plot nicer
plt.rcParams["figure.figsize"] = (12, 6)
start = time.time()
opt_result = opt.minimize(cost_func_domain, initial_point)
elapsed = time.time() - start
print(f"Fit in {elapsed:0.2f} seconds")
test_qc = QuantumCircuit(num_latent + num_trash)
test_qc = test_qc.compose(domain_wall_circuit)
ansatz_qc = ansatz(num_latent + num_trash)
test_qc = test_qc.compose(ansatz_qc)
test_qc.barrier()
test_qc.reset(4)
test_qc.reset(3)
test_qc.barrier()
test_qc = test_qc.compose(ansatz_qc.inverse())
test_qc.draw("mpl")
test_qc = test_qc.assign_parameters(opt_result.x)
domain_wall_state = Statevector(domain_wall_circuit).data
output_state = Statevector(test_qc).data
fidelity = np.sqrt(np.dot(domain_wall_state.conj(), output_state) ** 2)
print("Fidelity of our Output State with our Input State: ", fidelity.real)
def zero_idx(j, i):
# Index for zero pixels
return [
[i, j],
[i - 1, j - 1],
[i - 1, j + 1],
[i - 2, j - 1],
[i - 2, j + 1],
[i - 3, j - 1],
[i - 3, j + 1],
[i - 4, j - 1],
[i - 4, j + 1],
[i - 5, j],
]
def one_idx(i, j):
# Index for one pixels
return [[i, j - 1], [i, j - 2], [i, j - 3], [i, j - 4], [i, j - 5], [i - 1, j - 4], [i, j]]
def get_dataset_digits(num, draw=True):
# Create Dataset containing zero and one
train_images = []
train_labels = []
for i in range(int(num / 2)):
# First we introduce background noise
empty = np.array([algorithm_globals.random.uniform(0, 0.1) for i in range(32)]).reshape(
8, 4
)
# Now we insert the pixels for the one
for i, j in one_idx(2, 6):
empty[j][i] = algorithm_globals.random.uniform(0.9, 1)
train_images.append(empty)
train_labels.append(1)
if draw:
plt.title("This is a One")
plt.imshow(train_images[-1])
plt.show()
for i in range(int(num / 2)):
empty = np.array([algorithm_globals.random.uniform(0, 0.1) for i in range(32)]).reshape(
8, 4
)
# Now we insert the pixels for the zero
for k, j in zero_idx(2, 6):
empty[k][j] = algorithm_globals.random.uniform(0.9, 1)
train_images.append(empty)
train_labels.append(0)
if draw:
plt.imshow(train_images[-1])
plt.title("This is a Zero")
plt.show()
train_images = np.array(train_images)
train_images = train_images.reshape(len(train_images), 32)
for i in range(len(train_images)):
sum_sq = np.sum(train_images[i] ** 2)
train_images[i] = train_images[i] / np.sqrt(sum_sq)
return train_images, train_labels
train_images, __ = get_dataset_digits(2)
num_latent = 3
num_trash = 2
fm = RawFeatureVector(2 ** (num_latent + num_trash))
ae = auto_encoder_circuit(num_latent, num_trash)
qc = QuantumCircuit(num_latent + 2 * num_trash + 1, 1)
qc = qc.compose(fm, range(num_latent + num_trash))
qc = qc.compose(ae)
qc.draw("mpl")
def identity_interpret(x):
return x
qnn = SamplerQNN(
circuit=qc,
input_params=fm.parameters,
weight_params=ae.parameters,
interpret=identity_interpret,
output_shape=2,
)
def cost_func_digits(params_values):
probabilities = qnn.forward(train_images, params_values)
cost = np.sum(probabilities[:, 1]) / train_images.shape[0]
# plotting part
clear_output(wait=True)
objective_func_vals.append(cost)
plt.title("Objective function value against iteration")
plt.xlabel("Iteration")
plt.ylabel("Objective function value")
plt.plot(range(len(objective_func_vals)), objective_func_vals)
plt.show()
return cost
with open("12_qae_initial_point.json", "r") as f:
initial_point = json.load(f)
opt = COBYLA(maxiter=150)
objective_func_vals = []
# make the plot nicer
plt.rcParams["figure.figsize"] = (12, 6)
start = time.time()
opt_result = opt.minimize(fun=cost_func_digits, x0=initial_point)
elapsed = time.time() - start
print(f"Fit in {elapsed:0.2f} seconds")
# Test
test_qc = QuantumCircuit(num_latent + num_trash)
test_qc = test_qc.compose(fm)
ansatz_qc = ansatz(num_latent + num_trash)
test_qc = test_qc.compose(ansatz_qc)
test_qc.barrier()
test_qc.reset(4)
test_qc.reset(3)
test_qc.barrier()
test_qc = test_qc.compose(ansatz_qc.inverse())
# sample new images
test_images, test_labels = get_dataset_digits(2, draw=False)
for image, label in zip(test_images, test_labels):
original_qc = fm.assign_parameters(image)
original_sv = Statevector(original_qc).data
original_sv = np.reshape(np.abs(original_sv) ** 2, (8, 4))
param_values = np.concatenate((image, opt_result.x))
output_qc = test_qc.assign_parameters(param_values)
output_sv = Statevector(output_qc).data
output_sv = np.reshape(np.abs(output_sv) ** 2, (8, 4))
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.imshow(original_sv)
ax1.set_title("Input Data")
ax2.imshow(output_sv)
ax2.set_title("Output Data")
plt.show()
import qiskit.tools.jupyter
%qiskit_version_table
%qiskit_copyright
|
https://github.com/matteoacrossi/oqs-jupyterbook
|
matteoacrossi
|
# imports
import numpy as np
from qiskit import QuantumRegister, QuantumCircuit
####################################
# Depolarizing channel on IBMQX2 #
####################################
# Quantum register
q = QuantumRegister(4, name="q")
# Quantum circuit
depolarizing = QuantumCircuit(q)
# Depolarizing channel acting on q_2
## Qubit identification
system = 0
a_0 = 1
a_1 = 2
a_2 = 3
## Define rotation angle
theta = 0.0
## Construct circuit
depolarizing.ry(theta, q[a_0])
depolarizing.ry(theta, q[a_1])
depolarizing.ry(theta, q[a_2])
depolarizing.cx(q[a_0], q[system])
depolarizing.cy(q[a_1], q[system])
depolarizing.cz(q[a_2], q[system])
# Draw circuit
depolarizing.draw(output='mpl')
def depolarizing_channel(q, p, system, ancillae):
"""Returns a QuantumCircuit implementing depolarizing channel on q[system]
Args:
q (QuantumRegister): the register to use for the circuit
p (float): the probability for the channel between 0 and 1
system (int): index of the system qubit
ancillae (list): list of indices for the ancillary qubits
Returns:
A QuantumCircuit object
"""
# Write the code here...
# Let's fix the quantum register and the qubit assignments
# We create the quantum circuit
q = QuantumRegister(5, name='q')
# Index of the system qubit
system = 2
# Indices of the ancillary qubits
ancillae = [1, 3, 4]
# For example, let's consider 10 equally spaced values of p
p_values = np.linspace(0, 1, 10)
|
https://github.com/qiskit-community/qiskit-translations-staging
|
qiskit-community
|
from qiskit import pulse
dc = pulse.DriveChannel
d0, d1, d2, d3, d4 = dc(0), dc(1), dc(2), dc(3), dc(4)
with pulse.build(name='pulse_programming_in') as pulse_prog:
pulse.play([1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1], d0)
pulse.play([1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0], d1)
pulse.play([1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0], d2)
pulse.play([1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0], d3)
pulse.play([1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0], d4)
pulse_prog.draw()
|
https://github.com/swe-train/qiskit__qiskit
|
swe-train
|
# This code is part of Qiskit.
#
# (C) Copyright IBM 2022.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""Test functionality to collect, split and consolidate blocks from DAGCircuits."""
import unittest
from qiskit import QuantumRegister, ClassicalRegister
from qiskit.converters import (
circuit_to_dag,
circuit_to_dagdependency,
circuit_to_instruction,
dag_to_circuit,
dagdependency_to_circuit,
)
from qiskit.test import QiskitTestCase
from qiskit.circuit import QuantumCircuit, Measure, Clbit
from qiskit.dagcircuit.collect_blocks import BlockCollector, BlockSplitter, BlockCollapser
class TestCollectBlocks(QiskitTestCase):
"""Tests to verify correctness of collecting, splitting, and consolidating blocks
from DAGCircuit and DAGDependency. Additional tests appear as a part of
CollectLinearFunctions and CollectCliffords passes.
"""
def test_collect_gates_from_dagcircuit_1(self):
"""Test collecting CX gates from DAGCircuits."""
qc = QuantumCircuit(5)
qc.cx(0, 1)
qc.cx(0, 2)
qc.z(0)
qc.cx(0, 3)
qc.cx(0, 4)
block_collector = BlockCollector(circuit_to_dag(qc))
blocks = block_collector.collect_all_matching_blocks(
lambda node: node.op.name == "cx",
split_blocks=False,
min_block_size=2,
)
# The middle z-gate leads to two blocks of size 2 each
self.assertEqual(len(blocks), 2)
self.assertEqual(len(blocks[0]), 2)
self.assertEqual(len(blocks[1]), 2)
def test_collect_gates_from_dagcircuit_2(self):
"""Test collecting both CX and Z gates from DAGCircuits."""
qc = QuantumCircuit(5)
qc.cx(0, 1)
qc.cx(0, 2)
qc.z(0)
qc.cx(0, 3)
qc.cx(0, 4)
block_collector = BlockCollector(circuit_to_dag(qc))
blocks = block_collector.collect_all_matching_blocks(
lambda node: node.op.name in ["cx", "z"],
split_blocks=False,
min_block_size=1,
)
# All of the gates are part of a single block
self.assertEqual(len(blocks), 1)
self.assertEqual(len(blocks[0]), 5)
def test_collect_gates_from_dagcircuit_3(self):
"""Test collecting CX gates from DAGCircuits."""
qc = QuantumCircuit(5)
qc.cx(0, 1)
qc.cx(0, 2)
qc.z(0)
qc.cx(1, 3)
qc.cx(0, 3)
qc.cx(0, 4)
block_collector = BlockCollector(circuit_to_dag(qc))
blocks = block_collector.collect_all_matching_blocks(
lambda node: node.op.name in ["cx"],
split_blocks=False,
min_block_size=1,
)
# We should end up with two CX blocks: even though there is "a path
# around z(0)", without commutativity analysis z(0) prevents from
# including all CX-gates into the same block
self.assertEqual(len(blocks), 2)
def test_collect_gates_from_dagdependency_1(self):
"""Test collecting CX gates from DAGDependency."""
qc = QuantumCircuit(5)
qc.cx(0, 1)
qc.cx(0, 2)
qc.z(0)
qc.cx(0, 3)
qc.cx(0, 4)
block_collector = BlockCollector(circuit_to_dagdependency(qc))
blocks = block_collector.collect_all_matching_blocks(
lambda node: node.op.name == "cx",
split_blocks=False,
min_block_size=1,
)
# The middle z-gate commutes with CX-gates, which leads to a single block of length 4
self.assertEqual(len(blocks), 1)
self.assertEqual(len(blocks[0]), 4)
def test_collect_gates_from_dagdependency_2(self):
"""Test collecting both CX and Z gates from DAGDependency."""
qc = QuantumCircuit(5)
qc.cx(0, 1)
qc.cx(0, 2)
qc.z(0)
qc.cx(0, 3)
qc.cx(0, 4)
block_collector = BlockCollector(circuit_to_dagdependency(qc))
blocks = block_collector.collect_all_matching_blocks(
lambda node: node.op.name in ["cx", "z"],
split_blocks=False,
min_block_size=1,
)
# All the gates are part of a single block
self.assertEqual(len(blocks), 1)
self.assertEqual(len(blocks[0]), 5)
def test_collect_and_split_gates_from_dagcircuit(self):
"""Test collecting and splitting blocks from DAGCircuit."""
qc = QuantumCircuit(6)
qc.cx(0, 1)
qc.cx(3, 5)
qc.cx(2, 4)
qc.swap(1, 0)
qc.cz(5, 3)
block_collector = BlockCollector(circuit_to_dag(qc))
blocks = block_collector.collect_all_matching_blocks(
lambda node: True,
split_blocks=False,
min_block_size=1,
)
# All the gates are part of a single block
self.assertEqual(len(blocks), 1)
self.assertEqual(len(blocks[0]), 5)
# Split the first block into sub-blocks over disjoint qubit sets
# We should get 3 sub-blocks
split_blocks = BlockSplitter().run(blocks[0])
self.assertEqual(len(split_blocks), 3)
def test_collect_and_split_gates_from_dagdependency(self):
"""Test collecting and splitting blocks from DAGDependecy."""
qc = QuantumCircuit(6)
qc.cx(0, 1)
qc.cx(3, 5)
qc.cx(2, 4)
qc.swap(1, 0)
qc.cz(5, 3)
block_collector = BlockCollector(circuit_to_dagdependency(qc))
blocks = block_collector.collect_all_matching_blocks(
lambda node: True,
split_blocks=False,
min_block_size=1,
)
# All the gates are part of a single block
self.assertEqual(len(blocks), 1)
self.assertEqual(len(blocks[0]), 5)
# Split the first block into sub-blocks over disjoint qubit sets
# We should get 3 sub-blocks
split_blocks = BlockSplitter().run(blocks[0])
self.assertEqual(len(split_blocks), 3)
def test_circuit_has_measure(self):
"""Test that block collection works properly when there is a measure in the
middle of the circuit."""
qc = QuantumCircuit(2, 1)
qc.cx(1, 0)
qc.x(0)
qc.x(1)
qc.measure(0, 0)
qc.x(0)
qc.cx(1, 0)
block_collector = BlockCollector(circuit_to_dag(qc))
blocks = block_collector.collect_all_matching_blocks(
lambda node: node.op.name in ["x", "cx"],
split_blocks=False,
min_block_size=1,
)
# measure prevents combining the two blocks
self.assertEqual(len(blocks), 2)
self.assertEqual(len(blocks[0]), 3)
self.assertEqual(len(blocks[1]), 2)
def test_circuit_has_measure_dagdependency(self):
"""Test that block collection works properly when there is a measure in the
middle of the circuit."""
qc = QuantumCircuit(2, 1)
qc.cx(1, 0)
qc.x(0)
qc.x(1)
qc.measure(0, 0)
qc.x(0)
qc.cx(1, 0)
block_collector = BlockCollector(circuit_to_dagdependency(qc))
blocks = block_collector.collect_all_matching_blocks(
lambda node: node.op.name in ["x", "cx"],
split_blocks=False,
min_block_size=1,
)
# measure prevents combining the two blocks
self.assertEqual(len(blocks), 2)
self.assertEqual(len(blocks[0]), 3)
self.assertEqual(len(blocks[1]), 2)
def test_circuit_has_conditional_gates(self):
"""Test that block collection works properly when there the circuit
contains conditional gates."""
qc = QuantumCircuit(2, 1)
qc.x(0)
qc.x(1)
qc.cx(1, 0)
qc.x(1).c_if(0, 1)
qc.x(0)
qc.x(1)
qc.cx(0, 1)
# If the filter_function does not look at conditions, we should collect all
# gates into the block.
block_collector = BlockCollector(circuit_to_dag(qc))
blocks = block_collector.collect_all_matching_blocks(
lambda node: node.op.name in ["x", "cx"],
split_blocks=False,
min_block_size=1,
)
self.assertEqual(len(blocks), 1)
self.assertEqual(len(blocks[0]), 7)
# If the filter_function does look at conditions, we should not collect the middle
# conditional gate (note that x(1) following the measure is collected into the first
# block).
block_collector = BlockCollector(circuit_to_dag(qc))
blocks = block_collector.collect_all_matching_blocks(
lambda node: node.op.name in ["x", "cx"] and not getattr(node.op, "condition", None),
split_blocks=False,
min_block_size=1,
)
self.assertEqual(len(blocks), 2)
self.assertEqual(len(blocks[0]), 4)
self.assertEqual(len(blocks[1]), 2)
def test_circuit_has_conditional_gates_dagdependency(self):
"""Test that block collection works properly when there the circuit
contains conditional gates."""
qc = QuantumCircuit(2, 1)
qc.x(0)
qc.x(1)
qc.cx(1, 0)
qc.x(1).c_if(0, 1)
qc.x(0)
qc.x(1)
qc.cx(0, 1)
# If the filter_function does not look at conditions, we should collect all
# gates into the block.
block_collector = BlockCollector(circuit_to_dagdependency(qc))
blocks = block_collector.collect_all_matching_blocks(
lambda node: node.op.name in ["x", "cx"],
split_blocks=False,
min_block_size=1,
)
self.assertEqual(len(blocks), 1)
self.assertEqual(len(blocks[0]), 7)
# If the filter_function does look at conditions, we should not collect the middle
# conditional gate (note that x(1) following the measure is collected into the first
# block).
block_collector = BlockCollector(circuit_to_dag(qc))
blocks = block_collector.collect_all_matching_blocks(
lambda node: node.op.name in ["x", "cx"] and not getattr(node.op, "condition", None),
split_blocks=False,
min_block_size=1,
)
self.assertEqual(len(blocks), 2)
self.assertEqual(len(blocks[0]), 4)
self.assertEqual(len(blocks[1]), 2)
def test_multiple_collection_methods(self):
"""Test that block collection allows to collect blocks using several different
filter functions."""
qc = QuantumCircuit(5)
qc.cx(0, 1)
qc.cx(0, 2)
qc.swap(1, 4)
qc.swap(4, 3)
qc.z(0)
qc.z(1)
qc.z(2)
qc.z(3)
qc.z(4)
qc.swap(3, 4)
qc.cx(0, 3)
qc.cx(0, 4)
block_collector = BlockCollector(circuit_to_dag(qc))
linear_blocks = block_collector.collect_all_matching_blocks(
lambda node: node.op.name in ["cx", "swap"],
split_blocks=False,
min_block_size=1,
)
cx_blocks = block_collector.collect_all_matching_blocks(
lambda node: node.op.name in ["cx"],
split_blocks=False,
min_block_size=1,
)
swapz_blocks = block_collector.collect_all_matching_blocks(
lambda node: node.op.name in ["swap", "z"],
split_blocks=False,
min_block_size=1,
)
# We should end up with two linear blocks
self.assertEqual(len(linear_blocks), 2)
self.assertEqual(len(linear_blocks[0]), 4)
self.assertEqual(len(linear_blocks[1]), 3)
# We should end up with two cx blocks
self.assertEqual(len(cx_blocks), 2)
self.assertEqual(len(cx_blocks[0]), 2)
self.assertEqual(len(cx_blocks[1]), 2)
# We should end up with one swap,z blocks
self.assertEqual(len(swapz_blocks), 1)
self.assertEqual(len(swapz_blocks[0]), 8)
def test_min_block_size(self):
"""Test that the option min_block_size for collecting blocks works correctly."""
# original circuit
circuit = QuantumCircuit(2)
circuit.cx(0, 1)
circuit.h(0)
circuit.cx(0, 1)
circuit.cx(1, 0)
circuit.h(0)
circuit.cx(0, 1)
circuit.cx(1, 0)
circuit.cx(0, 1)
block_collector = BlockCollector(circuit_to_dag(circuit))
# When min_block_size = 1, we should obtain 3 linear blocks
blocks = block_collector.collect_all_matching_blocks(
lambda node: node.op.name in ["cx", "swap"],
split_blocks=False,
min_block_size=1,
)
self.assertEqual(len(blocks), 3)
# When min_block_size = 2, we should obtain 2 linear blocks
blocks = block_collector.collect_all_matching_blocks(
lambda node: node.op.name in ["cx", "swap"],
split_blocks=False,
min_block_size=2,
)
self.assertEqual(len(blocks), 2)
# When min_block_size = 3, we should obtain 1 linear block
blocks = block_collector.collect_all_matching_blocks(
lambda node: node.op.name in ["cx", "swap"],
split_blocks=False,
min_block_size=3,
)
self.assertEqual(len(blocks), 1)
# When min_block_size = 4, we should obtain no linear blocks
blocks = block_collector.collect_all_matching_blocks(
lambda node: node.op.name in ["cx", "swap"],
split_blocks=False,
min_block_size=4,
)
self.assertEqual(len(blocks), 0)
def test_split_blocks(self):
"""Test that splitting blocks of nodes into sub-blocks works correctly."""
# original circuit is linear
circuit = QuantumCircuit(5)
circuit.cx(0, 2)
circuit.cx(1, 4)
circuit.cx(2, 0)
circuit.cx(0, 3)
circuit.swap(3, 2)
circuit.swap(4, 1)
block_collector = BlockCollector(circuit_to_dag(circuit))
# If we do not split block into sub-blocks, we expect a single linear block.
blocks = block_collector.collect_all_matching_blocks(
lambda node: node.op.name in ["cx", "swap"],
split_blocks=False,
min_block_size=2,
)
self.assertEqual(len(blocks), 1)
# If we do split block into sub-blocks, we expect two linear blocks:
# one over qubits {0, 2, 3}, and another over qubits {1, 4}.
blocks = block_collector.collect_all_matching_blocks(
lambda node: node.op.name in ["cx", "swap"],
split_blocks=True,
min_block_size=2,
)
self.assertEqual(len(blocks), 2)
def test_do_not_split_blocks(self):
"""Test that splitting blocks of nodes into sub-blocks works correctly."""
# original circuit is linear
circuit = QuantumCircuit(5)
circuit.cx(0, 3)
circuit.cx(0, 2)
circuit.cx(1, 4)
circuit.swap(4, 2)
block_collector = BlockCollector(circuit_to_dagdependency(circuit))
# Check that we have a single linear block
blocks = block_collector.collect_all_matching_blocks(
lambda node: node.op.name in ["cx", "swap"],
split_blocks=True,
min_block_size=1,
)
self.assertEqual(len(blocks), 1)
def test_collect_blocks_with_cargs(self):
"""Test collecting and collapsing blocks with classical bits appearing as cargs."""
qc = QuantumCircuit(3)
qc.h(0)
qc.h(1)
qc.h(2)
qc.measure_all()
dag = circuit_to_dag(qc)
# Collect all measure instructions
blocks = BlockCollector(dag).collect_all_matching_blocks(
lambda node: isinstance(node.op, Measure), split_blocks=False, min_block_size=1
)
# We should have a single block consisting of 3 measures
self.assertEqual(len(blocks), 1)
self.assertEqual(len(blocks[0]), 3)
self.assertEqual(blocks[0][0].op, Measure())
self.assertEqual(blocks[0][1].op, Measure())
self.assertEqual(blocks[0][2].op, Measure())
def _collapse_fn(circuit):
op = circuit_to_instruction(circuit)
op.name = "COLLAPSED"
return op
# Collapse block with measures into a single "COLLAPSED" block
dag = BlockCollapser(dag).collapse_to_operation(blocks, _collapse_fn)
collapsed_qc = dag_to_circuit(dag)
self.assertEqual(len(collapsed_qc.data), 5)
self.assertEqual(collapsed_qc.data[0].operation.name, "h")
self.assertEqual(collapsed_qc.data[1].operation.name, "h")
self.assertEqual(collapsed_qc.data[2].operation.name, "h")
self.assertEqual(collapsed_qc.data[3].operation.name, "barrier")
self.assertEqual(collapsed_qc.data[4].operation.name, "COLLAPSED")
self.assertEqual(collapsed_qc.data[4].operation.definition.num_qubits, 3)
self.assertEqual(collapsed_qc.data[4].operation.definition.num_clbits, 3)
def test_collect_blocks_with_cargs_dagdependency(self):
"""Test collecting and collapsing blocks with classical bits appearing as cargs,
using DAGDependency."""
qc = QuantumCircuit(3)
qc.h(0)
qc.h(1)
qc.h(2)
qc.measure_all()
dag = circuit_to_dagdependency(qc)
# Collect all measure instructions
blocks = BlockCollector(dag).collect_all_matching_blocks(
lambda node: isinstance(node.op, Measure), split_blocks=False, min_block_size=1
)
# We should have a single block consisting of 3 measures
self.assertEqual(len(blocks), 1)
self.assertEqual(len(blocks[0]), 3)
self.assertEqual(blocks[0][0].op, Measure())
self.assertEqual(blocks[0][1].op, Measure())
self.assertEqual(blocks[0][2].op, Measure())
def _collapse_fn(circuit):
op = circuit_to_instruction(circuit)
op.name = "COLLAPSED"
return op
# Collapse block with measures into a single "COLLAPSED" block
dag = BlockCollapser(dag).collapse_to_operation(blocks, _collapse_fn)
collapsed_qc = dagdependency_to_circuit(dag)
self.assertEqual(len(collapsed_qc.data), 5)
self.assertEqual(collapsed_qc.data[0].operation.name, "h")
self.assertEqual(collapsed_qc.data[1].operation.name, "h")
self.assertEqual(collapsed_qc.data[2].operation.name, "h")
self.assertEqual(collapsed_qc.data[3].operation.name, "barrier")
self.assertEqual(collapsed_qc.data[4].operation.name, "COLLAPSED")
self.assertEqual(collapsed_qc.data[4].operation.definition.num_qubits, 3)
self.assertEqual(collapsed_qc.data[4].operation.definition.num_clbits, 3)
def test_collect_blocks_with_clbits(self):
"""Test collecting and collapsing blocks with classical bits appearing under
condition."""
qc = QuantumCircuit(4, 3)
qc.cx(0, 1).c_if(0, 1)
qc.cx(2, 3)
qc.cx(1, 2)
qc.cx(0, 1)
qc.cx(2, 3).c_if(1, 0)
dag = circuit_to_dag(qc)
# Collect all cx gates (including the conditional ones)
blocks = BlockCollector(dag).collect_all_matching_blocks(
lambda node: node.op.name == "cx", split_blocks=False, min_block_size=1
)
# We should have a single block consisting of all CX nodes
self.assertEqual(len(blocks), 1)
self.assertEqual(len(blocks[0]), 5)
def _collapse_fn(circuit):
op = circuit_to_instruction(circuit)
op.name = "COLLAPSED"
return op
# Collapse block with measures into a single "COLLAPSED" block
dag = BlockCollapser(dag).collapse_to_operation(blocks, _collapse_fn)
collapsed_qc = dag_to_circuit(dag)
self.assertEqual(len(collapsed_qc.data), 1)
self.assertEqual(collapsed_qc.data[0].operation.name, "COLLAPSED")
self.assertEqual(collapsed_qc.data[0].operation.definition.num_qubits, 4)
self.assertEqual(collapsed_qc.data[0].operation.definition.num_clbits, 2)
def test_collect_blocks_with_clbits_dagdependency(self):
"""Test collecting and collapsing blocks with classical bits appearing
under conditions, using DAGDependency."""
qc = QuantumCircuit(4, 3)
qc.cx(0, 1).c_if(0, 1)
qc.cx(2, 3)
qc.cx(1, 2)
qc.cx(0, 1)
qc.cx(2, 3).c_if(1, 0)
dag = circuit_to_dagdependency(qc)
# Collect all cx gates (including the conditional ones)
blocks = BlockCollector(dag).collect_all_matching_blocks(
lambda node: node.op.name == "cx", split_blocks=False, min_block_size=1
)
# We should have a single block consisting of all CX nodes
self.assertEqual(len(blocks), 1)
self.assertEqual(len(blocks[0]), 5)
def _collapse_fn(circuit):
op = circuit_to_instruction(circuit)
op.name = "COLLAPSED"
return op
# Collapse block with measures into a single "COLLAPSED" block
dag = BlockCollapser(dag).collapse_to_operation(blocks, _collapse_fn)
collapsed_qc = dagdependency_to_circuit(dag)
self.assertEqual(len(collapsed_qc.data), 1)
self.assertEqual(collapsed_qc.data[0].operation.name, "COLLAPSED")
self.assertEqual(collapsed_qc.data[0].operation.definition.num_qubits, 4)
self.assertEqual(collapsed_qc.data[0].operation.definition.num_clbits, 2)
def test_collect_blocks_with_clbits2(self):
"""Test collecting and collapsing blocks with classical bits appearing under
condition."""
qreg = QuantumRegister(4, "qr")
creg = ClassicalRegister(3, "cr")
cbit = Clbit()
qc = QuantumCircuit(qreg, creg, [cbit])
qc.cx(0, 1).c_if(creg[1], 1)
qc.cx(2, 3).c_if(cbit, 0)
qc.cx(1, 2)
qc.cx(0, 1).c_if(creg[2], 1)
dag = circuit_to_dag(qc)
# Collect all cx gates (including the conditional ones)
blocks = BlockCollector(dag).collect_all_matching_blocks(
lambda node: node.op.name == "cx", split_blocks=False, min_block_size=1
)
# We should have a single block consisting of all CX nodes
self.assertEqual(len(blocks), 1)
self.assertEqual(len(blocks[0]), 4)
def _collapse_fn(circuit):
op = circuit_to_instruction(circuit)
op.name = "COLLAPSED"
return op
# Collapse block with measures into a single "COLLAPSED" block
dag = BlockCollapser(dag).collapse_to_operation(blocks, _collapse_fn)
collapsed_qc = dag_to_circuit(dag)
self.assertEqual(len(collapsed_qc.data), 1)
self.assertEqual(collapsed_qc.data[0].operation.name, "COLLAPSED")
self.assertEqual(collapsed_qc.data[0].operation.definition.num_qubits, 4)
self.assertEqual(collapsed_qc.data[0].operation.definition.num_clbits, 3)
def test_collect_blocks_with_clbits2_dagdependency(self):
"""Test collecting and collapsing blocks with classical bits appearing under
condition, using DAGDependency."""
qreg = QuantumRegister(4, "qr")
creg = ClassicalRegister(3, "cr")
cbit = Clbit()
qc = QuantumCircuit(qreg, creg, [cbit])
qc.cx(0, 1).c_if(creg[1], 1)
qc.cx(2, 3).c_if(cbit, 0)
qc.cx(1, 2)
qc.cx(0, 1).c_if(creg[2], 1)
dag = circuit_to_dag(qc)
# Collect all cx gates (including the conditional ones)
blocks = BlockCollector(dag).collect_all_matching_blocks(
lambda node: node.op.name == "cx", split_blocks=False, min_block_size=1
)
# We should have a single block consisting of all CX nodes
self.assertEqual(len(blocks), 1)
self.assertEqual(len(blocks[0]), 4)
def _collapse_fn(circuit):
op = circuit_to_instruction(circuit)
op.name = "COLLAPSED"
return op
# Collapse block with measures into a single "COLLAPSED" block
dag = BlockCollapser(dag).collapse_to_operation(blocks, _collapse_fn)
collapsed_qc = dag_to_circuit(dag)
self.assertEqual(len(collapsed_qc.data), 1)
self.assertEqual(collapsed_qc.data[0].operation.name, "COLLAPSED")
self.assertEqual(collapsed_qc.data[0].operation.definition.num_qubits, 4)
self.assertEqual(collapsed_qc.data[0].operation.definition.num_clbits, 3)
def test_collect_blocks_with_cregs(self):
"""Test collecting and collapsing blocks with classical registers appearing under
condition."""
qreg = QuantumRegister(4, "qr")
creg = ClassicalRegister(3, "cr")
creg2 = ClassicalRegister(2, "cr2")
qc = QuantumCircuit(qreg, creg, creg2)
qc.cx(0, 1).c_if(creg, 3)
qc.cx(1, 2)
qc.cx(0, 1).c_if(creg[2], 1)
dag = circuit_to_dag(qc)
# Collect all cx gates (including the conditional ones)
blocks = BlockCollector(dag).collect_all_matching_blocks(
lambda node: node.op.name == "cx", split_blocks=False, min_block_size=1
)
# We should have a single block consisting of all CX nodes
self.assertEqual(len(blocks), 1)
self.assertEqual(len(blocks[0]), 3)
def _collapse_fn(circuit):
op = circuit_to_instruction(circuit)
op.name = "COLLAPSED"
return op
# Collapse block with measures into a single "COLLAPSED" block
dag = BlockCollapser(dag).collapse_to_operation(blocks, _collapse_fn)
collapsed_qc = dag_to_circuit(dag)
self.assertEqual(len(collapsed_qc.data), 1)
self.assertEqual(collapsed_qc.data[0].operation.name, "COLLAPSED")
self.assertEqual(len(collapsed_qc.data[0].operation.definition.cregs), 1)
self.assertEqual(collapsed_qc.data[0].operation.definition.num_qubits, 3)
self.assertEqual(collapsed_qc.data[0].operation.definition.num_clbits, 3)
def test_collect_blocks_with_cregs_dagdependency(self):
"""Test collecting and collapsing blocks with classical registers appearing under
condition, using DAGDependency."""
qreg = QuantumRegister(4, "qr")
creg = ClassicalRegister(3, "cr")
creg2 = ClassicalRegister(2, "cr2")
qc = QuantumCircuit(qreg, creg, creg2)
qc.cx(0, 1).c_if(creg, 3)
qc.cx(1, 2)
qc.cx(0, 1).c_if(creg[2], 1)
dag = circuit_to_dagdependency(qc)
# Collect all cx gates (including the conditional ones)
blocks = BlockCollector(dag).collect_all_matching_blocks(
lambda node: node.op.name == "cx", split_blocks=False, min_block_size=1
)
# We should have a single block consisting of all CX nodes
self.assertEqual(len(blocks), 1)
self.assertEqual(len(blocks[0]), 3)
def _collapse_fn(circuit):
op = circuit_to_instruction(circuit)
op.name = "COLLAPSED"
return op
# Collapse block with measures into a single "COLLAPSED" block
dag = BlockCollapser(dag).collapse_to_operation(blocks, _collapse_fn)
collapsed_qc = dagdependency_to_circuit(dag)
self.assertEqual(len(collapsed_qc.data), 1)
self.assertEqual(collapsed_qc.data[0].operation.name, "COLLAPSED")
self.assertEqual(len(collapsed_qc.data[0].operation.definition.cregs), 1)
self.assertEqual(collapsed_qc.data[0].operation.definition.num_qubits, 3)
self.assertEqual(collapsed_qc.data[0].operation.definition.num_clbits, 3)
def test_collect_blocks_backwards_dagcircuit(self):
"""Test collecting H gates from DAGCircuit in the forward vs. the reverse
directions."""
qc = QuantumCircuit(4)
qc.h(0)
qc.h(1)
qc.h(2)
qc.h(3)
qc.cx(1, 2)
qc.z(0)
qc.z(1)
qc.z(2)
qc.z(3)
block_collector = BlockCollector(circuit_to_dag(qc))
# When collecting in the forward direction, there are two blocks of
# single-qubit gates: the first of size 6, and the second of size 2.
blocks = block_collector.collect_all_matching_blocks(
lambda node: node.op.name in ["h", "z"],
split_blocks=False,
min_block_size=1,
collect_from_back=False,
)
self.assertEqual(len(blocks), 2)
self.assertEqual(len(blocks[0]), 6)
self.assertEqual(len(blocks[1]), 2)
# When collecting in the backward direction, there are also two blocks of
# single-qubit ates: but now the first is of size 2, and the second is of size 6.
blocks = block_collector.collect_all_matching_blocks(
lambda node: node.op.name in ["h", "z"],
split_blocks=False,
min_block_size=1,
collect_from_back=True,
)
self.assertEqual(len(blocks), 2)
self.assertEqual(len(blocks[0]), 2)
self.assertEqual(len(blocks[1]), 6)
def test_collect_blocks_backwards_dagdependency(self):
"""Test collecting H gates from DAGDependency in the forward vs. the reverse
directions."""
qc = QuantumCircuit(4)
qc.z(0)
qc.z(1)
qc.z(2)
qc.z(3)
qc.cx(1, 2)
qc.h(0)
qc.h(1)
qc.h(2)
qc.h(3)
block_collector = BlockCollector(circuit_to_dagdependency(qc))
# When collecting in the forward direction, there are two blocks of
# single-qubit gates: the first of size 6, and the second of size 2.
blocks = block_collector.collect_all_matching_blocks(
lambda node: node.op.name in ["h", "z"],
split_blocks=False,
min_block_size=1,
collect_from_back=False,
)
self.assertEqual(len(blocks), 2)
self.assertEqual(len(blocks[0]), 6)
self.assertEqual(len(blocks[1]), 2)
# When collecting in the backward direction, there are also two blocks of
# single-qubit ates: but now the first is of size 1, and the second is of size 7
# (note that z(1) and CX(1, 2) commute).
blocks = block_collector.collect_all_matching_blocks(
lambda node: node.op.name in ["h", "z"],
split_blocks=False,
min_block_size=1,
collect_from_back=True,
)
self.assertEqual(len(blocks), 2)
self.assertEqual(len(blocks[0]), 1)
self.assertEqual(len(blocks[1]), 7)
def test_split_layers_dagcircuit(self):
"""Test that splitting blocks of nodes into layers works correctly."""
# original circuit is linear
circuit = QuantumCircuit(5)
circuit.cx(0, 2)
circuit.cx(1, 4)
circuit.cx(2, 0)
circuit.cx(0, 3)
circuit.swap(3, 2)
circuit.swap(4, 1)
block_collector = BlockCollector(circuit_to_dag(circuit))
# If we split the gates into depth-1 layers, we expect four linear blocks:
# CX(0, 2), CX(1, 4)
# CX(2, 0), CX(4, 1)
# CX(0, 3)
# CX(3, 2)
blocks = block_collector.collect_all_matching_blocks(
lambda node: node.op.name in ["cx", "swap"],
split_blocks=False,
min_block_size=1,
split_layers=True,
)
self.assertEqual(len(blocks), 4)
self.assertEqual(len(blocks[0]), 2)
self.assertEqual(len(blocks[1]), 2)
self.assertEqual(len(blocks[2]), 1)
self.assertEqual(len(blocks[3]), 1)
def test_split_layers_dagdependency(self):
"""Test that splitting blocks of nodes into layers works correctly."""
# original circuit is linear
circuit = QuantumCircuit(5)
circuit.cx(0, 2)
circuit.cx(1, 4)
circuit.cx(2, 0)
circuit.cx(0, 3)
circuit.swap(3, 2)
circuit.swap(4, 1)
block_collector = BlockCollector(circuit_to_dagdependency(circuit))
# If we split the gates into depth-1 layers, we expect four linear blocks:
# CX(0, 2), CX(1, 4)
# CX(2, 0), CX(4, 1)
# CX(0, 3)
# CX(3, 2)
blocks = block_collector.collect_all_matching_blocks(
lambda node: node.op.name in ["cx", "swap"],
split_blocks=False,
min_block_size=1,
split_layers=True,
)
self.assertEqual(len(blocks), 4)
self.assertEqual(len(blocks[0]), 2)
self.assertEqual(len(blocks[1]), 2)
self.assertEqual(len(blocks[2]), 1)
self.assertEqual(len(blocks[3]), 1)
if __name__ == "__main__":
unittest.main()
|
https://github.com/GabrielPontolillo/Quantum_Algorithm_Implementations
|
GabrielPontolillo
|
import cirq
import random
import matplotlib.pyplot as plt
import numpy as np
import math
def qft_dagger_cirq(qc, qubits, n):
for qubit in range(n//2):
qc.append(cirq.SWAP(qubits[qubit], qubits[n-qubit-1]))
for j in range(n):
for m in range(j):
qc.append((cirq.CZ**(-1/2**(j-m)))(qubits[m],qubits[j]))
qc.append(cirq.H(qubits[j]))
def generalised_qpe_cirq(amt_estimation_qubits, angle, shots = 10000):
# Create and set up circuit
qubits = cirq.LineQubit.range(amt_estimation_qubits+1)
circuit = cirq.Circuit()
# Apply H-Gates to counting qubits:
for qubit in range(amt_estimation_qubits):
circuit.append(cirq.H(qubits[qubit]))
#print(circuit)
# Prepare our eigenstate |psi>:
circuit.append(cirq.X(qubits[amt_estimation_qubits]))
#print(circuit)
repetitions = 1
for counting_qubit in range(amt_estimation_qubits):
for i in range(repetitions):
circuit.append((cirq.CZ**((angle/math.pi)))(qubits[counting_qubit],qubits[amt_estimation_qubits]));
repetitions *= 2
#print(circuit)
# Do the inverse QFT:
qft_dagger_cirq(circuit, qubits, amt_estimation_qubits)
# Measure of course!
circuit.append(cirq.measure(*qubits[:-1], key='m'))
simulator = cirq.Simulator()
results = simulator.run(circuit , repetitions = shots)
theta_estimates = np.sum(2 ** np.arange(amt_estimation_qubits) * results.measurements['m'], axis=1) / 2**amt_estimation_qubits
print(theta_estimates)
counts = dict()
for i in theta_estimates:
counts[i] = counts.get(i, 0) + 1
print(counts)
unique,pos = np.unique(theta_estimates,return_inverse=True)
counts = np.bincount(pos)
maxpos = counts.argmax()
print(unique[maxpos],counts[maxpos])
#generalised_qpe_cirq(7,(13*math.pi/9))
generalised_qpe_cirq(7,(13*math.pi/9))
|
https://github.com/indian-institute-of-science-qc/qiskit-aakash
|
indian-institute-of-science-qc
|
# This code is part of Qiskit.
#
# (C) Copyright IBM 2017, 2019.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""Tests for quantum channel representation transformations."""
import unittest
import numpy as np
from qiskit import QiskitError
from qiskit.quantum_info.states import DensityMatrix
from qiskit.quantum_info.operators.predicates import matrix_equal
from qiskit.quantum_info.operators.operator import Operator
from qiskit.quantum_info.operators.channel.choi import Choi
from qiskit.quantum_info.operators.channel.superop import SuperOp
from qiskit.quantum_info.operators.channel.kraus import Kraus
from qiskit.quantum_info.operators.channel.stinespring import Stinespring
from qiskit.quantum_info.operators.channel.ptm import PTM
from qiskit.quantum_info.operators.channel.chi import Chi
from .channel_test_case import ChannelTestCase
class TestTransformations(ChannelTestCase):
"""Tests for Operator channel representation."""
unitary_mat = [
ChannelTestCase.UI,
ChannelTestCase.UX,
ChannelTestCase.UY,
ChannelTestCase.UZ,
ChannelTestCase.UH,
]
unitary_choi = [
ChannelTestCase.choiI,
ChannelTestCase.choiX,
ChannelTestCase.choiY,
ChannelTestCase.choiZ,
ChannelTestCase.choiH,
]
unitary_chi = [
ChannelTestCase.chiI,
ChannelTestCase.chiX,
ChannelTestCase.chiY,
ChannelTestCase.chiZ,
ChannelTestCase.chiH,
]
unitary_sop = [
ChannelTestCase.sopI,
ChannelTestCase.sopX,
ChannelTestCase.sopY,
ChannelTestCase.sopZ,
ChannelTestCase.sopH,
]
unitary_ptm = [
ChannelTestCase.ptmI,
ChannelTestCase.ptmX,
ChannelTestCase.ptmY,
ChannelTestCase.ptmZ,
ChannelTestCase.ptmH,
]
def test_operator_to_operator(self):
"""Test Operator to Operator transformation."""
# Test unitary channels
for mat in self.unitary_mat:
chan1 = Operator(mat)
chan2 = Operator(chan1)
self.assertEqual(chan1, chan2)
def test_operator_to_choi(self):
"""Test Operator to Choi transformation."""
# Test unitary channels
for mat, choi in zip(self.unitary_mat, self.unitary_choi):
chan1 = Choi(choi)
chan2 = Choi(Operator(mat))
self.assertEqual(chan1, chan2)
def test_operator_to_superop(self):
"""Test Operator to SuperOp transformation."""
# Test unitary channels
for mat, sop in zip(self.unitary_mat, self.unitary_sop):
chan1 = SuperOp(sop)
chan2 = SuperOp(Operator(mat))
self.assertEqual(chan1, chan2)
def test_operator_to_kraus(self):
"""Test Operator to Kraus transformation."""
# Test unitary channels
for mat in self.unitary_mat:
chan1 = Kraus(mat)
chan2 = Kraus(Operator(mat))
self.assertEqual(chan1, chan2)
def test_operator_to_stinespring(self):
"""Test Operator to Stinespring transformation."""
# Test unitary channels
for mat in self.unitary_mat:
chan1 = Stinespring(mat)
chan2 = Stinespring(Operator(chan1))
self.assertEqual(chan1, chan2)
def test_operator_to_chi(self):
"""Test Operator to Chi transformation."""
# Test unitary channels
for mat, chi in zip(self.unitary_mat, self.unitary_chi):
chan1 = Chi(chi)
chan2 = Chi(Operator(mat))
self.assertEqual(chan1, chan2)
def test_operator_to_ptm(self):
"""Test Operator to PTM transformation."""
# Test unitary channels
for mat, ptm in zip(self.unitary_mat, self.unitary_ptm):
chan1 = PTM(ptm)
chan2 = PTM(Operator(mat))
self.assertEqual(chan1, chan2)
def test_choi_to_operator(self):
"""Test Choi to Operator transformation."""
# Test unitary channels
for mat, choi in zip(self.unitary_mat, self.unitary_choi):
chan1 = Operator(mat)
chan2 = Operator(Choi(choi))
self.assertTrue(matrix_equal(chan2.data, chan1.data, ignore_phase=True))
def test_choi_to_choi(self):
"""Test Choi to Choi transformation."""
# Test unitary channels
for choi in self.unitary_choi:
chan1 = Choi(choi)
chan2 = Choi(chan1)
self.assertEqual(chan1, chan2)
# Test depolarizing channels
for p in [0.25, 0.5, 0.75, 1]:
chan1 = Choi(self.depol_choi(p))
chan2 = Choi(chan1)
self.assertEqual(chan1, chan2)
def test_choi_to_superop(self):
"""Test Choi to SuperOp transformation."""
# Test unitary channels
for choi, sop in zip(self.unitary_choi, self.unitary_sop):
chan1 = SuperOp(sop)
chan2 = SuperOp(Choi(choi))
self.assertEqual(chan1, chan2)
# Test depolarizing channels
for p in [0.25, 0.5, 0.75, 1]:
chan1 = SuperOp(self.depol_sop(p))
chan2 = SuperOp(Choi(self.depol_choi(p)))
self.assertEqual(chan1, chan2)
def test_choi_to_kraus(self):
"""Test Choi to Kraus transformation."""
# Test unitary channels
for mat, choi in zip(self.unitary_mat, self.unitary_choi):
chan1 = Kraus(mat)
chan2 = Kraus(Choi(choi))
self.assertTrue(matrix_equal(chan2.data[0], chan1.data[0], ignore_phase=True))
# Test depolarizing channels
rho = DensityMatrix(np.diag([1, 0]))
for p in [0.25, 0.5, 0.75, 1]:
target = rho.evolve(Kraus(self.depol_kraus(p)))
output = rho.evolve(Kraus(Choi(self.depol_choi(p))))
self.assertEqual(output, target)
def test_choi_to_stinespring(self):
"""Test Choi to Stinespring transformation."""
# Test unitary channels
for mat, choi in zip(self.unitary_mat, self.unitary_choi):
chan1 = Kraus(mat)
chan2 = Kraus(Choi(choi))
self.assertTrue(matrix_equal(chan2.data[0], chan1.data[0], ignore_phase=True))
# Test depolarizing channels
rho = DensityMatrix(np.diag([1, 0]))
for p in [0.25, 0.5, 0.75, 1]:
target = rho.evolve(Stinespring(self.depol_stine(p)))
output = rho.evolve(Stinespring(Choi(self.depol_choi(p))))
self.assertEqual(output, target)
def test_choi_to_chi(self):
"""Test Choi to Chi transformation."""
# Test unitary channels
for choi, chi in zip(self.unitary_choi, self.unitary_chi):
chan1 = Chi(chi)
chan2 = Chi(Choi(choi))
self.assertEqual(chan1, chan2)
# Test depolarizing channels
for p in [0.25, 0.5, 0.75, 1]:
chan1 = Chi(self.depol_chi(p))
chan2 = Chi(Choi(self.depol_choi(p)))
self.assertEqual(chan1, chan2)
def test_choi_to_ptm(self):
"""Test Choi to PTM transformation."""
# Test unitary channels
for choi, ptm in zip(self.unitary_choi, self.unitary_ptm):
chan1 = PTM(ptm)
chan2 = PTM(Choi(choi))
self.assertEqual(chan1, chan2)
# Test depolarizing channels
for p in [0.25, 0.5, 0.75, 1]:
chan1 = PTM(self.depol_ptm(p))
chan2 = PTM(Choi(self.depol_choi(p)))
self.assertEqual(chan1, chan2)
def test_superop_to_operator(self):
"""Test SuperOp to Operator transformation."""
for mat, sop in zip(self.unitary_mat, self.unitary_sop):
chan1 = Operator(mat)
chan2 = Operator(SuperOp(sop))
self.assertTrue(matrix_equal(chan2.data, chan1.data, ignore_phase=True))
self.assertRaises(QiskitError, Operator, SuperOp(self.depol_sop(0.5)))
def test_superop_to_choi(self):
"""Test SuperOp to Choi transformation."""
# Test unitary channels
for choi, sop in zip(self.unitary_choi, self.unitary_sop):
chan1 = Choi(choi)
chan2 = Choi(SuperOp(sop))
self.assertEqual(chan1, chan2)
# Test depolarizing channels
for p in [0, 0.25, 0.5, 0.75, 1]:
chan1 = Choi(self.depol_choi(p))
chan2 = Choi(SuperOp(self.depol_sop(p)))
self.assertEqual(chan1, chan2)
def test_superop_to_superop(self):
"""Test SuperOp to SuperOp transformation."""
# Test unitary channels
for sop in self.unitary_sop:
chan1 = SuperOp(sop)
chan2 = SuperOp(chan1)
self.assertEqual(chan1, chan2)
# Test depolarizing channels
for p in [0, 0.25, 0.5, 0.75, 1]:
chan1 = SuperOp(self.depol_sop(p))
chan2 = SuperOp(chan1)
self.assertEqual(chan1, chan2)
def test_superop_to_kraus(self):
"""Test SuperOp to Kraus transformation."""
# Test unitary channels
for mat, sop in zip(self.unitary_mat, self.unitary_sop):
chan1 = Kraus(mat)
chan2 = Kraus(SuperOp(sop))
self.assertTrue(matrix_equal(chan2.data[0], chan1.data[0], ignore_phase=True))
# Test depolarizing channels
rho = DensityMatrix(np.diag([1, 0]))
for p in [0.25, 0.5, 0.75, 1]:
target = rho.evolve(Kraus(self.depol_kraus(p)))
output = rho.evolve(Kraus(SuperOp(self.depol_sop(p))))
self.assertEqual(output, target)
def test_superop_to_stinespring(self):
"""Test SuperOp to Stinespring transformation."""
# Test unitary channels
for mat, sop in zip(self.unitary_mat, self.unitary_sop):
chan1 = Stinespring(mat)
chan2 = Stinespring(SuperOp(sop))
self.assertTrue(matrix_equal(chan2.data[0], chan1.data[0], ignore_phase=True))
# Test depolarizing channels
rho = DensityMatrix(np.diag([1, 0]))
for p in [0.25, 0.5, 0.75, 1]:
target = rho.evolve(Stinespring(self.depol_stine(p)))
output = rho.evolve(Stinespring(SuperOp(self.depol_sop(p))))
self.assertEqual(output, target)
def test_superop_to_chi(self):
"""Test SuperOp to Chi transformation."""
# Test unitary channels
for sop, ptm in zip(self.unitary_sop, self.unitary_ptm):
chan1 = PTM(ptm)
chan2 = PTM(SuperOp(sop))
self.assertEqual(chan1, chan2)
# Test depolarizing channels
for p in [0.25, 0.5, 0.75, 1]:
chan1 = Chi(self.depol_chi(p))
chan2 = Chi(SuperOp(self.depol_sop(p)))
self.assertEqual(chan1, chan2)
def test_superop_to_ptm(self):
"""Test SuperOp to PTM transformation."""
# Test unitary channels
for sop, ptm in zip(self.unitary_sop, self.unitary_ptm):
chan1 = PTM(ptm)
chan2 = PTM(SuperOp(sop))
self.assertEqual(chan1, chan2)
# Test depolarizing channels
for p in [0.25, 0.5, 0.75, 1]:
chan1 = PTM(self.depol_ptm(p))
chan2 = PTM(SuperOp(self.depol_sop(p)))
self.assertEqual(chan1, chan2)
def test_kraus_to_operator(self):
"""Test Kraus to Operator transformation."""
for mat in self.unitary_mat:
chan1 = Operator(mat)
chan2 = Operator(Kraus(mat))
self.assertTrue(matrix_equal(chan2.data, chan1.data, ignore_phase=True))
self.assertRaises(QiskitError, Operator, Kraus(self.depol_kraus(0.5)))
def test_kraus_to_choi(self):
"""Test Kraus to Choi transformation."""
# Test unitary channels
for mat, choi in zip(self.unitary_mat, self.unitary_choi):
chan1 = Choi(choi)
chan2 = Choi(Kraus(mat))
self.assertEqual(chan1, chan2)
# Test depolarizing channels
for p in [0.25, 0.5, 0.75, 1]:
chan1 = Choi(self.depol_choi(p))
chan2 = Choi(Kraus(self.depol_kraus(p)))
self.assertEqual(chan1, chan2)
def test_kraus_to_superop(self):
"""Test Kraus to SuperOp transformation."""
# Test unitary channels
for mat, sop in zip(self.unitary_mat, self.unitary_sop):
chan1 = SuperOp(sop)
chan2 = SuperOp(Kraus(mat))
self.assertEqual(chan1, chan2)
# Test depolarizing channels
for p in [0.25, 0.5, 0.75, 1]:
chan1 = SuperOp(self.depol_sop(p))
chan2 = SuperOp(Kraus(self.depol_kraus(p)))
self.assertEqual(chan1, chan2)
def test_kraus_to_kraus(self):
"""Test Kraus to Kraus transformation."""
# Test unitary channels
for mat in self.unitary_mat:
chan1 = Kraus(mat)
chan2 = Kraus(chan1)
self.assertEqual(chan1, chan2)
# Test depolarizing channels
for p in [0.25, 0.5, 0.75, 1]:
chan1 = Kraus(self.depol_kraus(p))
chan2 = Kraus(chan1)
self.assertEqual(chan1, chan2)
def test_kraus_to_stinespring(self):
"""Test Kraus to Stinespring transformation."""
# Test unitary channels
for mat in self.unitary_mat:
chan1 = Stinespring(mat)
chan2 = Stinespring(Kraus(mat))
self.assertTrue(matrix_equal(chan2.data[0], chan1.data[0], ignore_phase=True))
# Test depolarizing channels
rho = DensityMatrix(np.diag([1, 0]))
for p in [0.25, 0.5, 0.75, 1]:
target = rho.evolve(Stinespring(self.depol_stine(p)))
output = rho.evolve(Stinespring(Kraus(self.depol_kraus(p))))
self.assertEqual(output, target)
def test_kraus_to_chi(self):
"""Test Kraus to Chi transformation."""
# Test unitary channels
for mat, chi in zip(self.unitary_mat, self.unitary_chi):
chan1 = Chi(chi)
chan2 = Chi(Kraus(mat))
self.assertEqual(chan1, chan2)
# Test depolarizing channels
for p in [0.25, 0.5, 0.75, 1]:
chan1 = Chi(self.depol_chi(p))
chan2 = Chi(Kraus(self.depol_kraus(p)))
self.assertEqual(chan1, chan2)
def test_kraus_to_ptm(self):
"""Test Kraus to PTM transformation."""
# Test unitary channels
for mat, ptm in zip(self.unitary_mat, self.unitary_ptm):
chan1 = PTM(ptm)
chan2 = PTM(Kraus(mat))
self.assertEqual(chan1, chan2)
# Test depolarizing channels
for p in [0.25, 0.5, 0.75, 1]:
chan1 = PTM(self.depol_ptm(p))
chan2 = PTM(Kraus(self.depol_kraus(p)))
self.assertEqual(chan1, chan2)
def test_stinespring_to_operator(self):
"""Test Stinespring to Operator transformation."""
for mat in self.unitary_mat:
chan1 = Operator(mat)
chan2 = Operator(Stinespring(mat))
self.assertTrue(matrix_equal(chan2.data, chan1.data, ignore_phase=True))
self.assertRaises(QiskitError, Operator, Stinespring(self.depol_stine(0.5)))
def test_stinespring_to_choi(self):
"""Test Stinespring to Choi transformation."""
# Test unitary channels
for mat, choi in zip(self.unitary_mat, self.unitary_choi):
chan1 = Choi(choi)
chan2 = Choi(Stinespring(mat))
self.assertEqual(chan1, chan2)
# Test depolarizing channels
for p in [0.25, 0.5, 0.75, 1]:
chan1 = Choi(self.depol_choi(p))
chan2 = Choi(Stinespring(self.depol_stine(p)))
self.assertEqual(chan1, chan2)
def test_stinespring_to_superop(self):
"""Test Stinespring to SuperOp transformation."""
# Test unitary channels
for mat, sop in zip(self.unitary_mat, self.unitary_sop):
chan1 = SuperOp(sop)
chan2 = SuperOp(Kraus(mat))
self.assertEqual(chan1, chan2)
# Test depolarizing channels
for p in [0.25, 0.5, 0.75, 1]:
chan1 = SuperOp(self.depol_sop(p))
chan2 = SuperOp(Stinespring(self.depol_stine(p)))
self.assertEqual(chan1, chan2)
def test_stinespring_to_kraus(self):
"""Test Stinespring to Kraus transformation."""
# Test unitary channels
for mat in self.unitary_mat:
chan1 = Kraus(mat)
chan2 = Kraus(Stinespring(mat))
self.assertEqual(chan1, chan2)
# Test depolarizing channels
for p in [0.25, 0.5, 0.75, 1]:
chan1 = Kraus(self.depol_kraus(p))
chan2 = Kraus(Stinespring(self.depol_stine(p)))
self.assertEqual(chan1, chan2)
def test_stinespring_to_stinespring(self):
"""Test Stinespring to Stinespring transformation."""
# Test unitary channels
for mat in self.unitary_mat:
chan1 = Stinespring(mat)
chan2 = Stinespring(chan1)
self.assertEqual(chan1, chan2)
# Test depolarizing channels
for p in [0.25, 0.5, 0.75, 1]:
chan1 = Stinespring(self.depol_stine(p))
chan2 = Stinespring(chan1)
self.assertEqual(chan1, chan2)
def test_stinespring_to_chi(self):
"""Test Stinespring to Chi transformation."""
# Test unitary channels
for mat, chi in zip(self.unitary_mat, self.unitary_chi):
chan1 = Chi(chi)
chan2 = Chi(Stinespring(mat))
self.assertEqual(chan1, chan2)
# Test depolarizing channels
for p in [0.25, 0.5, 0.75, 1]:
chan1 = Chi(self.depol_chi(p))
chan2 = Chi(Stinespring(self.depol_stine(p)))
self.assertEqual(chan1, chan2)
def test_stinespring_to_ptm(self):
"""Test Stinespring to PTM transformation."""
# Test unitary channels
for mat, ptm in zip(self.unitary_mat, self.unitary_ptm):
chan1 = PTM(ptm)
chan2 = PTM(Stinespring(mat))
self.assertEqual(chan1, chan2)
# Test depolarizing channels
for p in [0.25, 0.5, 0.75, 1]:
chan1 = PTM(self.depol_ptm(p))
chan2 = PTM(Stinespring(self.depol_stine(p)))
self.assertEqual(chan1, chan2)
def test_chi_to_operator(self):
"""Test Chi to Operator transformation."""
for mat, chi in zip(self.unitary_mat, self.unitary_chi):
chan1 = Operator(mat)
chan2 = Operator(Chi(chi))
self.assertTrue(matrix_equal(chan2.data, chan1.data, ignore_phase=True))
self.assertRaises(QiskitError, Operator, Chi(self.depol_chi(0.5)))
def test_chi_to_choi(self):
"""Test Chi to Choi transformation."""
# Test unitary channels
for chi, choi in zip(self.unitary_chi, self.unitary_choi):
chan1 = Choi(choi)
chan2 = Choi(Chi(chi))
self.assertEqual(chan1, chan2)
# Test depolarizing channels
for p in [0.25, 0.5, 0.75, 1]:
chan1 = Choi(self.depol_choi(p))
chan2 = Choi(Chi(self.depol_chi(p)))
self.assertEqual(chan1, chan2)
def test_chi_to_superop(self):
"""Test Chi to SuperOp transformation."""
# Test unitary channels
for chi, sop in zip(self.unitary_chi, self.unitary_sop):
chan1 = SuperOp(sop)
chan2 = SuperOp(Chi(chi))
self.assertEqual(chan1, chan2)
# Test depolarizing channels
for p in [0.25, 0.5, 0.75, 1]:
chan1 = SuperOp(self.depol_sop(p))
chan2 = SuperOp(Chi(self.depol_chi(p)))
self.assertEqual(chan1, chan2)
def test_chi_to_kraus(self):
"""Test Chi to Kraus transformation."""
# Test unitary channels
for mat, chi in zip(self.unitary_mat, self.unitary_chi):
chan1 = Kraus(mat)
chan2 = Kraus(Chi(chi))
self.assertTrue(matrix_equal(chan2.data[0], chan1.data[0], ignore_phase=True))
# Test depolarizing channels
rho = DensityMatrix(np.diag([1, 0]))
for p in [0.25, 0.5, 0.75, 1]:
target = rho.evolve(Kraus(self.depol_kraus(p)))
output = rho.evolve(Kraus(Chi(self.depol_chi(p))))
self.assertEqual(output, target)
def test_chi_to_stinespring(self):
"""Test Chi to Stinespring transformation."""
# Test unitary channels
for mat, chi in zip(self.unitary_mat, self.unitary_chi):
chan1 = Kraus(mat)
chan2 = Kraus(Chi(chi))
self.assertTrue(matrix_equal(chan2.data[0], chan1.data[0], ignore_phase=True))
# Test depolarizing channels
rho = DensityMatrix(np.diag([1, 0]))
for p in [0.25, 0.5, 0.75, 1]:
target = rho.evolve(Stinespring(self.depol_stine(p)))
output = rho.evolve(Stinespring(Chi(self.depol_chi(p))))
self.assertEqual(output, target)
def test_chi_to_chi(self):
"""Test Chi to Chi transformation."""
# Test unitary channels
for chi in self.unitary_chi:
chan1 = Chi(chi)
chan2 = Chi(chan1)
self.assertEqual(chan1, chan2)
# Test depolarizing channels
for p in [0.25, 0.5, 0.75, 1]:
chan1 = Chi(self.depol_chi(p))
chan2 = Chi(chan1)
self.assertEqual(chan1, chan2)
def test_chi_to_ptm(self):
"""Test Chi to PTM transformation."""
# Test unitary channels
for chi, ptm in zip(self.unitary_chi, self.unitary_ptm):
chan1 = PTM(ptm)
chan2 = PTM(Chi(chi))
self.assertEqual(chan1, chan2)
# Test depolarizing channels
for p in [0.25, 0.5, 0.75, 1]:
chan1 = PTM(self.depol_ptm(p))
chan2 = PTM(Chi(self.depol_chi(p)))
self.assertEqual(chan1, chan2)
def test_ptm_to_operator(self):
"""Test PTM to Operator transformation."""
for mat, ptm in zip(self.unitary_mat, self.unitary_ptm):
chan1 = Operator(mat)
chan2 = Operator(PTM(ptm))
self.assertTrue(matrix_equal(chan2.data, chan1.data, ignore_phase=True))
self.assertRaises(QiskitError, Operator, PTM(self.depol_ptm(0.5)))
def test_ptm_to_choi(self):
"""Test PTM to Choi transformation."""
# Test unitary channels
for ptm, choi in zip(self.unitary_ptm, self.unitary_choi):
chan1 = Choi(choi)
chan2 = Choi(PTM(ptm))
self.assertEqual(chan1, chan2)
# Test depolarizing channels
for p in [0.25, 0.5, 0.75, 1]:
chan1 = Choi(self.depol_choi(p))
chan2 = Choi(PTM(self.depol_ptm(p)))
self.assertEqual(chan1, chan2)
def test_ptm_to_superop(self):
"""Test PTM to SuperOp transformation."""
# Test unitary channels
for ptm, sop in zip(self.unitary_ptm, self.unitary_sop):
chan1 = SuperOp(sop)
chan2 = SuperOp(PTM(ptm))
self.assertEqual(chan1, chan2)
# Test depolarizing channels
for p in [0.25, 0.5, 0.75, 1]:
chan1 = SuperOp(self.depol_sop(p))
chan2 = SuperOp(PTM(self.depol_ptm(p)))
self.assertEqual(chan1, chan2)
def test_ptm_to_kraus(self):
"""Test PTM to Kraus transformation."""
# Test unitary channels
for mat, ptm in zip(self.unitary_mat, self.unitary_ptm):
chan1 = Kraus(mat)
chan2 = Kraus(PTM(ptm))
self.assertTrue(matrix_equal(chan2.data[0], chan1.data[0], ignore_phase=True))
# Test depolarizing channels
rho = DensityMatrix(np.diag([1, 0]))
for p in [0.25, 0.5, 0.75, 1]:
target = rho.evolve(Kraus(self.depol_kraus(p)))
output = rho.evolve(Kraus(PTM(self.depol_ptm(p))))
self.assertEqual(output, target)
def test_ptm_to_stinespring(self):
"""Test PTM to Stinespring transformation."""
# Test unitary channels
for mat, ptm in zip(self.unitary_mat, self.unitary_ptm):
chan1 = Kraus(mat)
chan2 = Kraus(PTM(ptm))
self.assertTrue(matrix_equal(chan2.data[0], chan1.data[0], ignore_phase=True))
# Test depolarizing channels
rho = DensityMatrix(np.diag([1, 0]))
for p in [0.25, 0.5, 0.75, 1]:
target = rho.evolve(Stinespring(self.depol_stine(p)))
output = rho.evolve(Stinespring(PTM(self.depol_ptm(p))))
self.assertEqual(output, target)
def test_ptm_to_chi(self):
"""Test PTM to Chi transformation."""
# Test unitary channels
for chi, ptm in zip(self.unitary_chi, self.unitary_ptm):
chan1 = Chi(chi)
chan2 = Chi(PTM(ptm))
self.assertEqual(chan1, chan2)
# Test depolarizing channels
for p in [0.25, 0.5, 0.75, 1]:
chan1 = Chi(self.depol_chi(p))
chan2 = Chi(PTM(self.depol_ptm(p)))
self.assertEqual(chan1, chan2)
def test_ptm_to_ptm(self):
"""Test PTM to PTM transformation."""
# Test unitary channels
for ptm in self.unitary_ptm:
chan1 = PTM(ptm)
chan2 = PTM(chan1)
self.assertEqual(chan1, chan2)
# Test depolarizing channels
for p in [0.25, 0.5, 0.75, 1]:
chan1 = PTM(self.depol_ptm(p))
chan2 = PTM(chan1)
self.assertEqual(chan1, chan2)
if __name__ == "__main__":
unittest.main()
|
https://github.com/anpaschool/quantum-computing
|
anpaschool
|
from qiskit import *
from math import pi
from qiskit.visualization import plot_bloch_multivector
# Let's do an X-gate on a |0> qubit
qc = QuantumCircuit(1)
qc.x(0)
qc.draw('mpl')
# Let's see the result
backend = Aer.get_backend('statevector_simulator')
out = execute(qc,backend).result().get_statevector()
print(out)
plot_bloch_multivector(out)
# Run the quantum circuit on a unitary simulator backend
backend = Aer.get_backend('unitary_simulator')
job = execute(qc, backend)
result = job.result()
# Show the results
print(result.get_unitary(qc, decimals=3))
# Let's do an X-gate on a |0> qubit
qc = QuantumCircuit(1)
qc.y(0)
qc.draw('mpl')
# Let's see the result
backend = Aer.get_backend('statevector_simulator')
out = execute(qc,backend).result().get_statevector()
print(out)
plot_bloch_multivector(out)
# Run the quantum circuit on a unitary simulator backend
backend = Aer.get_backend('unitary_simulator')
job = execute(qc, backend)
print(out)
result = job.result()
# Show the results
print(result.get_unitary(qc, decimals=3))
qc = QuantumCircuit(1)
qc.z(0)
qc.draw(output='mpl')
# Let's see the result
backend = Aer.get_backend('statevector_simulator')
out = execute(qc,backend).result().get_statevector()
print(out)
plot_bloch_multivector(out)
# Run the quantum circuit on a unitary simulator backend
backend = Aer.get_backend('unitary_simulator')
job = execute(qc, backend)
print(out)
result = job.result()
# Show the results
print(result.get_unitary(qc, decimals=3))
# Let's do an X-gate on a |0> qubit
qc = QuantumCircuit(1)
qc.x(0)
qc.x(0)
qc.y(0)
qc.y(0)
qc.z(0)
qc.z(0)
qc.draw('mpl')
# Let's see the result
backend = Aer.get_backend('statevector_simulator')
out = execute(qc,backend).result().get_statevector()
print(out)
plot_bloch_multivector(out)
# Run the quantum circuit on a unitary simulator backend
backend = Aer.get_backend('unitary_simulator')
job = execute(qc, backend)
result = job.result()
# Show the results
print(result.get_unitary(qc, decimals=3))
# Let's do an X-gate on a |0> qubit
qc = QuantumCircuit(2)
qc.x(0)
qc.y(1)
qc.draw('mpl')
# Let's see the result
backend = Aer.get_backend('statevector_simulator')
out = execute(qc,backend).result().get_statevector()
print(out)
plot_bloch_multivector(out)
# Run the quantum circuit on a unitary simulator backend
backend = Aer.get_backend('unitary_simulator')
job = execute(qc, backend)
result = job.result()
# Show the results
print(result.get_unitary(qc, decimals=3))
# Let's do an X-gate on a |0> qubit
qc = QuantumCircuit(2)
qc.x(0)
qc.y(1)
qc.z(0)
qc.x(1)
qc.draw('mpl')
# Let's see the result
backend = Aer.get_backend('statevector_simulator')
out = execute(qc,backend).result().get_statevector()
print(out)
plot_bloch_multivector(out)
# Run the quantum circuit on a unitary simulator backend
backend = Aer.get_backend('unitary_simulator')
job = execute(qc, backend)
result = job.result()
# Show the results
print(result.get_unitary(qc, decimals=3))
# Let's do an X-gate on a |0> qubit
qc = QuantumCircuit(3)
qc.x(0)
qc.y(1)
qc.z(2)
qc.draw('mpl')
# Let's see the result
backend = Aer.get_backend('statevector_simulator')
out = execute(qc,backend).result().get_statevector()
print(out)
plot_bloch_multivector(out)
# Run the quantum circuit on a unitary simulator backend
backend = Aer.get_backend('unitary_simulator')
job = execute(qc, backend)
result = job.result()
# Show the results
print(result.get_unitary(qc, decimals=3))
|
https://github.com/qiskit-community/qiskit-translations-staging
|
qiskit-community
|
import matplotlib.pyplot as plt
from qiskit import QuantumCircuit, transpile
from qiskit.providers.fake_provider import FakeAuckland
backend = FakeAuckland()
ghz = QuantumCircuit(15)
ghz.h(0)
ghz.cx(0, range(1, 15))
depths = []
gate_counts = []
non_local_gate_counts = []
levels = [str(x) for x in range(4)]
for level in range(4):
circ = transpile(ghz, backend, optimization_level=level)
depths.append(circ.depth())
gate_counts.append(sum(circ.count_ops().values()))
non_local_gate_counts.append(circ.num_nonlocal_gates())
fig, (ax1, ax2) = plt.subplots(2, 1)
ax1.bar(levels, depths, label='Depth')
ax1.set_xlabel("Optimization Level")
ax1.set_ylabel("Depth")
ax1.set_title("Output Circuit Depth")
ax2.bar(levels, gate_counts, label='Number of Circuit Operations')
ax2.bar(levels, non_local_gate_counts, label='Number of non-local gates')
ax2.set_xlabel("Optimization Level")
ax2.set_ylabel("Number of gates")
ax2.legend()
ax2.set_title("Number of output circuit gates")
fig.tight_layout()
plt.show()
|
https://github.com/ctuning/ck-qiskit
|
ctuning
|
#!/usr/bin/env python3
"""
This script runs Variational-Quantum-Eigensolver using Qiskit library
Example running it partially using CK infrastructure:
ck virtual env --tag_groups='compiler,python qiskit,lib vqe,utils vqe,hamiltonian deployed,ansatz deployed,optimizer' \
--shell_cmd="$HOME/CK/ck-qiskit/program/qiskit-vqe/qiskit_vqe_common.py --start_param_value=0.5"
"""
import os
import json
import time
import inspect
import numpy as np
from scipy import linalg as la
from qiskit import QuantumProgram, register, QISKitError
from qiskit.tools.apps.optimization import make_Hamiltonian, group_paulis
from qiskit.tools.qi.pauli import Pauli, label_to_pauli
from eval_hamiltonian import eval_hamiltonian
from vqe_utils import cmdline_parse_and_report, get_first_callable, NumpyEncoder
from vqe_hamiltonian import label_to_hamiltonian_coeff # the file contents will be different depending on the plugin choice
import custom_ansatz # the file contents will be different depending on the plugin choice
fun_evaluation_counter = 0 # global
def vqe_for_qiskit(sample_number, pauli_list, timeout_seconds, json_stream_file):
def expectation_estimation(current_params, report):
timestamp_before_ee = time.time()
timestamp_before_q_run = timestamp_before_ee # no point in taking consecutive timestamps
ansatz_circuit = ansatz_function(current_params)
global fun_evaluation_counter
# Trying to recover from a timed-out run assuming it to be a temporary glitch:
#
for attempt in range(1,8):
try:
complex_energy, q_run_seconds = eval_hamiltonian(Q_program, pauli_list_grouped, ansatz_circuit, sample_number, q_device_name, timeout=timeout_seconds)
energy = complex_energy.real
break
except QISKitError as e:
print("{}, trying again -- attempt number {}, timeout: {} seconds".format(e, attempt, timeout_seconds))
if len(q_run_seconds)>0: # got the real measured q time
total_q_run_seconds = sum( q_run_seconds )
else: # have to assume
total_q_run_seconds = time.time() - timestamp_before_q_run
q_run_seconds = [ total_q_run_seconds ]
q_runs = len(q_run_seconds)
total_q_run_shots = sample_number * q_runs
q_run_shots = [sample_number] * q_runs
report_this_iteration = {
'total_q_seconds_per_c_iteration' : total_q_run_seconds,
'seconds_per_individual_q_run' : q_run_seconds,
'total_q_shots_per_c_iteration' : total_q_run_shots,
'shots_per_individual_q_run' : q_run_shots,
'energy' : energy,
}
if report != 'TestMode':
report['iterations'].append( report_this_iteration )
report['total_q_seconds'] += report_this_iteration['total_q_seconds_per_c_iteration'] # total_q_time += total
report['total_q_shots'] += report_this_iteration['total_q_shots_per_c_iteration']
fun_evaluation_counter += 1
report_this_iteration['total_seconds_per_c_iteration'] = time.time() - timestamp_before_ee
print(report_this_iteration, "\n")
json_stream_file.write( json.dumps(report_this_iteration, cls=NumpyEncoder)+"\n" )
json_stream_file.flush()
return energy
# Initialise quantum program
Q_program = QuantumProgram()
# Groups a list of (coeff,Pauli) tuples into tensor product basis (tpb) sets
pauli_list_grouped = group_paulis(pauli_list)
report = { 'total_q_seconds': 0, 'total_q_shots':0, 'iterations' : [] }
# Initial objective function value
fun_initial = expectation_estimation(start_params, 'TestMode')
print('Initial guess at start_params is: {:.4f}'.format(fun_initial))
timestamp_before_optimizer = time.time()
optimizer_output = minimizer_function(expectation_estimation, start_params, my_args=(report), my_options = minimizer_options)
report['total_seconds'] = time.time() - timestamp_before_optimizer
# Also generate and provide a validated function value at the optimal point
fun_validated = expectation_estimation(optimizer_output['x'], 'TestMode')
print('Validated value at solution is: {:.4f}'.format(fun_validated))
# Exact (noiseless) calculation of the energy at the given point:
complex_energy, _ = eval_hamiltonian(Q_program, pauli_list, ansatz_function(optimizer_output['x']), 1, 'local_statevector_simulator')
optimizer_output['fun_exact'] = complex_energy.real
optimizer_output['fun_validated'] = fun_validated
print('Total Q seconds = %f' % report['total_q_seconds'])
print('Total Q shots = %d' % report['total_q_shots'])
print('Total seconds = %f' % report['total_seconds'])
return (optimizer_output, report)
if __name__ == '__main__':
start_params, sample_number, q_device_name, minimizer_method, minimizer_options, minimizer_function = cmdline_parse_and_report(
num_params = custom_ansatz.num_params,
q_device_name_default = 'local_qasm_simulator',
q_device_name_help = "Real devices: 'ibmqx4' or 'ibmqx5'. Use 'ibmq_qasm_simulator' for remote simulator or 'local_qasm_simulator' for local",
minimizer_options_default = '{"maxfev":200, "xatol": 0.001, "fatol": 0.001}',
start_param_value_default = 0.0
)
# q_device_name = os.environ.get('VQE_QUANTUM_BACKEND', 'local_qasm_simulator') # try 'local_qasm_simulator', 'ibmq_qasm_simulator', 'ibmqx4', 'ibmqx5'
try:
import Qconfig
register(Qconfig.APItoken, Qconfig.config["url"], verify=False,
hub=Qconfig.config["hub"],
group=Qconfig.config["group"],
project=Qconfig.config["project"])
except:
print("""
WARNING: There's no connection with IBMQuantumExperience servers.
cannot test I/O intesive tasks, will only test CPU intensive tasks
running the jobs in the local simulator
""")
# Ignore warnings due to chopping of small imaginary part of the energy
#import warnings
#warnings.filterwarnings('ignore')
# Load the Hamiltonian into Qiskit-friendly format:
pauli_list = [ [label_to_hamiltonian_coeff[label], label_to_pauli(label)] for label in label_to_hamiltonian_coeff ]
# Calculate Exact Energy classically, to compare with quantum solution:
#
H = make_Hamiltonian(pauli_list)
classical_energy = np.amin(la.eigh(H)[0])
print('The exact ground state energy (the smallest eigenvalue of the Hamiltonian) is: {:.4f}'.format(classical_energy))
# Load the ansatz function from the plug-in
ansatz_method = get_first_callable( custom_ansatz )
ansatz_function = getattr(custom_ansatz, ansatz_method) # ansatz_method is a string/name, ansatz_function is an imported callable
timeout_seconds = int( os.environ.get('VQE_QUANTUM_TIMEOUT', '120') )
json_stream_file = open('vqe_stream.json', 'a')
# ---------------------------------------- run VQE: --------------------------------------------------
(vqe_output, report) = vqe_for_qiskit(sample_number, pauli_list, timeout_seconds, json_stream_file)
# ---------------------------------------- store the results: ----------------------------------------
json_stream_file.write( '# Experiment finished\n' )
json_stream_file.close()
minimizer_src = inspect.getsource( minimizer_function )
ansatz_src = inspect.getsource( ansatz_function )
vqe_input = {
"q_device_name" : q_device_name,
"minimizer_method" : minimizer_method,
"minimizer_src" : minimizer_src,
"minimizer_options" : minimizer_options,
"ansatz_method" : ansatz_method,
"ansatz_src" : ansatz_src,
"sample_number" : sample_number,
"classical_energy" : classical_energy
}
output_dict = { "vqe_input" : vqe_input, "vqe_output" : vqe_output, "report" : report }
formatted_json = json.dumps(output_dict, cls=NumpyEncoder, sort_keys = True, indent = 4)
# print(formatted_json)
with open('ibm_vqe_report.json', 'w') as json_file:
json_file.write( formatted_json )
|
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
from qiskit.visualization import *
def to_numbers(tensor_list):
num_list = []
for tensor in tensor_list:
num_list += [tensor.item()]
return num_list
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, execute
from qiskit.circuit import Parameter
from qiskit import Aer
from tqdm import tqdm
from matplotlib import pyplot as plt
%matplotlib inline
class QiskitCircuit():
# Specify initial parameters and the quantum circuit
def __init__(self,shots):
self.theta = Parameter('Theta')
self.shots = shots
def create_circuit():
qr = QuantumRegister(1,'q')
cr = ClassicalRegister(1,'c')
ckt = QuantumCircuit(qr,cr)
ckt.h(qr[0])
ckt.barrier()
ckt.ry(self.theta,qr[0])
ckt.barrier()
ckt.measure(qr,cr)
return ckt
self.circuit = create_circuit()
def N_qubit_expectation_Z(self,counts, shots, nr_qubits):
expects = np.zeros(nr_qubits)
for key in counts.keys():
perc = counts[key]/shots
check = np.array([(float(key[i])-1/2)*2*perc for i in range(nr_qubits)])
expects += check
return expects
def bind(self, parameters):
[self.theta] = to_numbers(parameters)
self.circuit.data[2][0]._params = to_numbers(parameters)
def run(self, i):
self.bind(i)
backend = Aer.get_backend('qasm_simulator')
job_sim = execute(self.circuit,backend,shots=self.shots)
result_sim = job_sim.result()
counts = result_sim.get_counts(self.circuit)
return self.N_qubit_expectation_Z(counts,self.shots,1)
class TorchCircuit(Function):
@staticmethod
def forward(ctx, i):
if not hasattr(ctx, 'QiskitCirc'):
ctx.QiskitCirc = QiskitCircuit(shots=100)
exp_value = ctx.QiskitCirc.run(i[0])
result = torch.tensor([exp_value]) # store the result as a torch tensor
ctx.save_for_backward(result, i)
return result
@staticmethod
def backward(ctx, grad_output):
s = np.pi/2
forward_tensor, i = ctx.saved_tensors
# Obtain paramaters
input_numbers = to_numbers(i[0])
gradient = []
for k in range(len(input_numbers)):
input_plus_s = input_numbers
input_plus_s[k] = input_numbers[k] + s # Shift up by s
exp_value_plus = ctx.QiskitCirc.run(torch.tensor(input_plus_s))[0]
result_plus_s = torch.tensor([exp_value_plus])
input_minus_s = input_numbers
input_minus_s[k] = input_numbers[k] - s # Shift down by s
exp_value_minus = ctx.QiskitCirc.run(torch.tensor(input_minus_s))[0]
result_minus_s = torch.tensor([exp_value_minus])
gradient_result = (result_plus_s - result_minus_s)
gradient.append(gradient_result)
result = torch.tensor([gradient])
return result.float() * grad_output.float()
#import torchvision
#transform = torchvision.transforms.Compose([torchvision.transforms.ToTensor()]) # transform images to tensors/vectors
#cifar_trainset = datasets.CIFAR10(root='./data1', train=True, download=True, transform=transform)
#labels = cifar_trainset.targets # get the labels for the data
#labels = labels.numpy()
#idx1 = np.where(labels == 0) # filter on aeroplanes
#idx2 = np.where(labels == 1) # filter on automobiles
# Specify number of datapoints per class (i.e. there will be n pictures of automobiles and n pictures of aeroplanes in the training set)
#n=100
# concatenate the data indices
#idx = np.concatenate((idx1[0][0:n],idx2[0][0:n]))
# create the filtered dataset for our training set
#cifar_trainset.targets = labels[idx]
#cifar_trainset.data = cifar_trainset.data[idx]
#train_loader = torch.utils.data.DataLoader(cifar_trainset, batch_size=1, shuffle=True)
import tensorflow
import torchvision
import torchvision
transform = torchvision.transforms.Compose([torchvision.transforms.ToTensor()]) # transform images to tensors/vectors
cifar_trainset = datasets.CIFAR10(root='./data1', train=True, download=True, transform=transform)
labels = cifar_trainset.targets # get the labels for the data
labels = np.array(labels)
idx1 = np.where(labels == 0) # filter on aeroplanes
idx2 = np.where(labels == 1) # filter on automobiles
# Specify number of datapoints per class (i.e. there will be n pictures of automobiles and n pictures of aeroplanes in the training set)
n=100
# concatenate the data indices
idx = np.concatenate((idx1[0][0:n],idx2[0][0:n]))
# create the filtered dataset for our training set
cifar_trainset.targets = labels[idx]
cifar_trainset.data = cifar_trainset.data[idx]
train_loader = torch.utils.data.DataLoader(cifar_trainset, batch_size=1, shuffle=True)
qc = TorchCircuit.apply
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.h1 = nn.Linear(500, 500)
self.h2 = nn.Linear(500, 1)
def forward(self,x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
x = x.view(-1, 500)
x = F.relu(self.h1(x))
x = F.dropout(x, training=self.training)
x = self.h2(x)
x = qc(x)
x = (x+1)/2 # Normalise the inputs to 1 or 0
x = torch.cat((x, 1-x), -1)
return x
network = Net()
#input = input.permute(1,0,2,3)
optimizer = optim.Adam(network.parameters(), lr=0.001)
epochs = 15
loss_list = []
for epoch in range(epochs):
total_loss = []
target_list = []
for batch_idx, (data, target) in enumerate(train_loader):
target_list.append(target.item())
optimizer.zero_grad()
output = network(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
total_loss.append(loss.item())
loss_list.append(sum(total_loss)/len(total_loss))
print(loss_list[-1])
# Normalise the loss between 0 and 1
for i in range(len(loss_list)):
loss_list[i] += 1
# Plot the loss per epoch
plt.plot(loss_list)
network = Net()
#input = input.permute(1,0,2,3)
optimizer = optim.Adam(network.parameters(), lr=0.001)
epochs = 5
loss_list = []
for epoch in range(epochs):
total_loss = []
target_list = []
for batch_idx, (data, target) in enumerate(train_loader):
target_list.append(target.item())
optimizer.zero_grad()
output = network(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
total_loss.append(loss.item())
loss_list.append(sum(total_loss)/len(total_loss))
print(loss_list[-1])
# Normalise the loss between 0 and 1
for i in range(len(loss_list)):
loss_list[i] += 1
# Plot the loss per epoch
plt.plot(loss_list)
network = Net()
#input = input.permute(1,0,2,3)
optimizer = optim.Adam(network.parameters(), lr=0.001)
epochs = 15
loss_list = []
for epoch in range(epochs):
total_loss = []
target_list = []
for batch_idx, (data, target) in enumerate(train_loader):
target_list.append(target.item())
optimizer.zero_grad()
output = network(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
total_loss.append(loss.item())
loss_list.append(sum(total_loss)/len(total_loss))
print(loss_list[-1])
# Normalise the loss between 0 and 1
for i in range(len(loss_list)):
loss_list[i] += 1
# Plot the loss per epoch
plt.plot(loss_list)
network = Net()
#input = input.permute(1,0,2,3)
optimizer = optim.Adam(network.parameters(), lr=0.001)
epochs = 20
loss_list = []
for epoch in range(epochs):
total_loss = []
target_list = []
for batch_idx, (data, target) in enumerate(train_loader):
target_list.append(target.item())
optimizer.zero_grad()
output = network(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
total_loss.append(loss.item())
loss_list.append(sum(total_loss)/len(total_loss))
print(loss_list[-1])
# Normalise the loss between 0 and 1
for i in range(len(loss_list)):
loss_list[i] += 1
# Plot the loss per epoch
plt.plot(loss_list)
model = Net()
optimizer = optim.Adam(model.parameters(), lr=0.001)
loss_func = nn.NLLLoss()
epochs = 20
loss_list = []
model.train()
for epoch in range(epochs):
total_loss = []
for batch_idx, (data, target) in enumerate(train_loader):
optimizer.zero_grad()
# Forward pass
output = model(data)
# Calculating loss
loss = loss_func(output, target)
# Backward pass
loss.backward()
# Optimize the weights
optimizer.step()
total_loss.append(loss.item())
loss_list.append(sum(total_loss)/len(total_loss))
print('Training [{:.0f}%]\tLoss: {:.4f}'.format(
100. * (epoch + 1) / epochs, loss_list[-1]))
plt.plot(loss_list)
plt.title('Hybrid NN Training Convergence')
plt.xlabel('Training Iterations')
plt.ylabel('Neg Log Likelihood Loss')
model.eval()
with torch.no_grad():
correct = 0
for batch_idx, (data, target) in enumerate(test_loader):
output = model(data)
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
loss = loss_func(output, target)
total_loss.append(loss.item())
print('Performance on test data:\n\tLoss: {:.4f}\n\tAccuracy: {:.1f}%'.format(
sum(total_loss) / len(total_loss),
correct / len(test_loader) * 100)
)
import torchvision
transform = torchvision.transforms.Compose([torchvision.transforms.ToTensor()]) # transform images to tensors/vectors
cifar_testset = datasets.CIFAR10(root='./data1', train=False, download=True, transform=transform)
labels1 = cifar_testset.targets # get the labels for the data
labels1 = np.array(labels1)
idx1_ae = np.where(labels1 == 0) # filter on aeroplanes
idx2_au = np.where(labels1 == 1) # filter on automobiles
# Specify number of datapoints per class (i.e. there will be n pictures of automobiles and n pictures of aeroplanes in the training set)
n=50
# concatenate the data indices
idxa = np.concatenate((idx1_ae[0][0:n],idx2_au[0][0:n]))
# create the filtered dataset for our training set
cifar_testset.targets = labels[idxa]
cifar_testset.data = cifar_testset.data[idxa]
test_loader = torch.utils.data.DataLoader(cifar_testset, batch_size=1, shuffle=True)
model.eval()
with torch.no_grad():
correct = 0
for batch_idx, (data, target) in enumerate(test_loader):
output = model(data)
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
loss = loss_func(output, target)
total_loss.append(loss.item())
print('Performance on test data:\n\tLoss: {:.4f}\n\tAccuracy: {:.1f}%'.format(
sum(total_loss) / len(total_loss),
correct / len(test_loader) * 100)
)
qc = TorchCircuit.apply
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.h1 = nn.Linear(500, 500)
self.h2 = nn.Linear(500, 11)
def forward(self,x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
x = x.view(-1, 500)
x = F.relu(self.h1(x))
x = F.dropout(x, training=self.training)
x = self.h2(x)
x = qc(x)
x = (x+1)/2 # Normalise the inputs to 1 or 0
x = torch.cat((x, 1-x), -1)
return x
|
https://github.com/UST-QuAntiL/nisq-analyzer-content
|
UST-QuAntiL
|
from qiskit import QuantumRegister, ClassicalRegister
from qiskit import QuantumCircuit
# https://quantum-circuit.com/app_details/about/bw5r9HTiTHvQHtCB5
qc = QuantumCircuit()
q = QuantumRegister(5, 'q')
c = ClassicalRegister(3, 'c')
qc.add_register(q)
qc.add_register(c)
qc.h(q[0])
qc.h(q[1])
qc.h(q[2])
qc.h(q[1])
qc.cx(q[2], q[3])
qc.cu1(0, q[1], q[0])
qc.cx(q[2], q[4])
qc.h(q[0])
qc.cu1(0, q[1], q[2])
qc.cu1(0, q[0], q[2])
qc.h(q[2])
qc.measure(q[0], c[0])
qc.measure(q[1], c[1])
qc.measure(q[2], c[2])
def get_circuit(**kwargs):
"""Get circuit of Shor with input 15."""
return qc
|
https://github.com/qiskit-community/qiskit-dell-runtime
|
qiskit-community
|
from dell_runtime import DellRuntimeProvider
from dell_runtime import BackendProvider
from qiskit import QuantumCircuit
provider = DellRuntimeProvider()
RUNTIME_PROGRAM = """
# 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 or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
# This is a simplified version of the circuit-runner program.
from qiskit.compiler import transpile, schedule
def main(
backend,
user_messenger,
circuits,
initial_layout=None,
seed_transpiler=None,
optimization_level=None,
transpiler_options=None,
scheduling_method=None,
schedule_circuit=False,
inst_map=None,
meas_map=None,
measurement_error_mitigation=False,
**kwargs,
):
# transpiling the circuits using given transpile options
transpiler_options = transpiler_options or {}
circuits = transpile(
circuits,
initial_layout=initial_layout,
seed_transpiler=seed_transpiler,
optimization_level=optimization_level,
backend=backend,
**transpiler_options,
)
if schedule_circuit:
circuits = schedule(
circuits=circuits,
backend=backend,
inst_map=inst_map,
meas_map=meas_map,
method=scheduling_method,
)
if not isinstance(circuits, list):
circuits = [circuits]
# Compute raw results
result = backend.run(circuits, **kwargs).result()
if measurement_error_mitigation:
# Performs measurement error mitigation.
pass
user_messenger.publish(result.to_dict(), final=True)
"""
RUNTIME_PROGRAM_METADATA = {
"max_execution_time": 600,
"description": "Qiskit test program"
}
program_id = provider.runtime.upload_program(RUNTIME_PROGRAM, metadata=RUNTIME_PROGRAM_METADATA)
N = 6
qc = QuantumCircuit(N)
qc.x(range(0, N))
qc.h(range(0, N))
for kk in range(N//2,0,-1):
qc.ch(kk, kk-1)
for kk in range(N//2, N-1):
qc.ch(kk, kk+1)
qc.measure_all()
program_inputs = {
'circuits': qc,
'shots': 2048,
'optimization_level': 0,
'initial_layout': [0,1,4,7,10,12],
'measurement_error_mitigation': False
}
runtime_program = provider.runtime.program(program_id)
job = provider.runtime.run(program_id, options=None, inputs=program_inputs)
job.status()
|
https://github.com/xtophe388/QISKIT
|
xtophe388
|
# execute this cell twice to see the output due to an issue with matplotlib
import sys
if sys.version_info < (3,5):
raise Exception('Please use Python version 3.5 or greater.')
from pprint import pprint
import math
# importing the QISKit
from qiskit import QuantumProgram
# To use API
#import Qconfig
def gate_mu3(qcirc,theta,phi,lam,a,b):
qcirc.cx(a,b)
qcirc.cu3(theta,phi,lam,b,a)
qcirc.cx(a,b)
n_nodes = 5
n_step = 3
# Creating Programs
qp = QuantumProgram()
# Creating Registers
qr = qp.create_quantum_register('qr', n_nodes)
cr = qp.create_classical_register('cr', n_nodes)
# Creating Circuits
qc = qp.create_circuit('QWalk', [qr], [cr])
# Creating of two partitions with M1' and M2
for i in range(0,n_nodes-1,2):
gate_mu3(qc,math.pi, math.pi, 0, qr[i], qr[i+1])
for i in range(1,n_nodes,2):
gate_mu3(qc,math.pi/2, 0, 0, qr[i], qr[i+1])
# import state tomography functions
from qiskit.tools.visualization import plot_histogram, plot_state
import numpy as np
# execute the quantum circuit
backend = 'local_unitary_simulator' # the device to run on
qobj = qp.compile(['QWalk'], backend=backend)
result = qp.run(qobj)
initial_state = np.zeros(2**n_nodes)
initial_state[1]=1.0 # state 0 = ....0000, state 1 = ...000001
QWalk = result.get_data('QWalk')['unitary']
#Applying QWalk n_step times
for i in range(0,n_step):
if i > 0: initial_state = np.copy(state_QWalk) # Copy previous state
state_QWalk = np.dot(QWalk,initial_state) # Multiply on QWalk matrix
rho_QWalk=np.outer(state_QWalk, state_QWalk.conj()) # Calculate density matrix
print('step = ',i+1) # print number
plot_state(rho_QWalk,'qsphere') # draw Quantum Sphere
|
https://github.com/Andres8bit/IBMQ-Quantum-Qiskit
|
Andres8bit
|
%matplotlib inline
# initialization:
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
# Importing standard Qiskit libraries and configuring account
from qiskit import QuantumCircuit, execute, BasicAer, IBMQ
from qiskit.providers.ibmq import least_busy
from qiskit.compiler import transpile, assemble
from qiskit.tools.jupyter import *
from qiskit.tools.monitor import job_monitor
from qiskit.visualization import *
# Loading your IBM Q account(s)
provider = IBMQ.load_account()
# Simon's Algorithm:
# Given an unknown blackbox funnction f,
# which is quaranteed to be either 1:1 or 2:1.
# That is to say
# either: f is a 1 to 1 maping,
# where for every unique x value maps to a single unique f(x).
# or: f is a 2 to 1 maping,
# where exactly 2 unique x vals map to the same f(x).
# Given this blackbox function how quickly can we determine which type of function f is?
# Then is f is 2:1 how quickly can we determine s, where if f is 2:1.
# given x1,x2: f(x1) = f(x)
# it is guaranteed: x1 CNOT x2 = s
# ========== Classical Solution: ============
# The traditional approach requires us to check up to 2^[N-1] + 1 inputs.
# Where N is the number of bits in the input.
# Therefore we would have to check on avg. half of all inputs to be 100% positve the function was 2:1.
# ========== Quantum Solution: ==========
# Steps:
# 1. Two n-qubit input registers are initialized to the 0 state:
# |ψ1> = |0⟩^⊗n |0⟩^⊗n
# 2. Apply a Hadamard transform to the first register:
# |ψ2⟩ = 1/sqrt(2^n)*∑[x∈{0,1}^n] |x⟩|0⟩⊗n
# 3. Apply the query function Qf:
# |ψ3⟩=1/sqrt(2^n) * ∑x∈[{0,1}^n] |x⟩|f(x)⟩
# 4. Measure the second register. A certain value of f(x) will be observed.
# BC of the setting of the problem, the observed value f(x) could
# correspond to two possible inputs: x & y = x⊕s
# Therefore the first register becomes:
# |ψ4⟩ = [1/sqrt(2)]( |x⟩ + |y⟩ )
# 5. Apply Hadamard on the first regisiter:
# |ψ5⟩ = 1/sqrt(2^[n+1])*∑z∈[{0,1}n] [ (−1)^(x⋅z) + (−1)^(y⋅z) ] | z⟩
# 6. Measuring the first register will give an output only if:
# (−1)^(x⋅z)=(−1)^(y⋅z)
# which means:
# x*z = y*z
# x*z = (x XOR s)*z
# x*z = x*(z XOR s)*z
# s*z = 0%2
# A string z whose inner product with s will be measured. Thus, repeating the
# algorithm ~~ n times, we will be able to obtain n different values of z & the
# following system of equations can be written:
#
# s*z1 = 0
# s*z2 = 0
# ...
# s*zn = 0
# From which we can find s using Gaussian elimination.
# currently using a fixed string implementation, should expand to a random general case:
# Creating registers
# qubits and classical bits for querying the oracle and finding the hidden period s
n = 2*len(str(s))
simonCircuit = QuantumCircuit(n)
barriers = True
# Apply Hadamard gates before querying the oracle
simonCircuit.h(range(len(str(s))))
# Apply barrier
if barriers:
simonCircuit.barrier()
# Apply the query function
## 2-qubit oracle for s = 11
simonCircuit.cx(0, len(str(s)) + 0)
simonCircuit.cx(0, len(str(s)) + 1)
simonCircuit.cx(1, len(str(s)) + 0)
simonCircuit.cx(1, len(str(s)) + 1)
# Apply barrier
if barriers:
simonCircuit.barrier()
# Apply Hadamard gates to the input register
simonCircuit.h(range(len(str(s))))
# Measure ancilla qubits
simonCircuit.measure_all()
simonCircuit.draw(output='mpl')
# use local simalator:
backend = BasicAer.get_backend('qasm_simulator')
shots = 1024
results = execute(simonCircuit,backend=backend,shots=shots).result()
answer = results.get_counts()
answer_plot = {}
for measresult in answer.keys():
measresult_input = measresult[len(str(s)):]
if measresult_input in answer_plot:
answer_plot[measresult_input] += answer[measresult]
else:
answer_plot[measresult_input] = answer[measresult]
print(answer_plot)
plot_histogram(answer_plot)
# Calculate the dot product of the results
def sdotz(a,b):
accum = 0
for i in range(len(a)):
accum += int(a[i]) * int(b[i])
return( accum % 2 )
print('a,z,s.z(mod 2)')
for z_rev in answer_plot:
z = z_rev[::-1]
print('{},{},{},{}={}'.format(s,z,s,z,sdotz(s,z)))
# Run our circuit on the least busy backend
shots = 1024
job = execute(simonCircuit,backend=backend,shots=shots)
job_monitor(job,interval=2)
# Categorize measurements by input register values
answer_plot = {}
for measresult in answer.keys():
measresult_input = measresult[len(str(s)):]
if measresult_input in answer_plot:
answer_plot[measresult_input] += answer[measresult]
else:
answer_plot[measresult_input] = answer[measresult]
# Plot the categorized results
print( answer_plot )
plot_histogram(answer_plot)
# Calculate the dot product of the most significant results
print('s, z, s.z (mod 2)')
for z_rev in answer_plot:
if answer_plot[z_rev] >= 0.1*shots:
z = z_rev[::-1]
print( '{}, {}, {}.{}={}'.format(s, z, s,z,sdotz(s,z)) )
|
https://github.com/qiskit-community/qiskit-translations-staging
|
qiskit-community
|
from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit
from qiskit.dagcircuit import DAGCircuit
from qiskit.converters import circuit_to_dag
from qiskit.visualization import dag_drawer
q = QuantumRegister(3, 'q')
c = ClassicalRegister(3, 'c')
circ = QuantumCircuit(q, c)
circ.h(q[0])
circ.cx(q[0], q[1])
circ.measure(q[0], c[0])
circ.rz(0.5, q[1]).c_if(c, 2)
dag = circuit_to_dag(circ)
dag_drawer(dag)
|
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 derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""A collection of discrete probability metrics."""
from __future__ import annotations
import numpy as np
def hellinger_distance(dist_p: dict, dist_q: dict) -> float:
"""Computes the Hellinger distance between
two counts distributions.
Parameters:
dist_p (dict): First dict of counts.
dist_q (dict): Second dict of counts.
Returns:
float: Distance
References:
`Hellinger Distance @ wikipedia <https://en.wikipedia.org/wiki/Hellinger_distance>`_
"""
p_sum = sum(dist_p.values())
q_sum = sum(dist_q.values())
p_normed = {}
for key, val in dist_p.items():
p_normed[key] = val / p_sum
q_normed = {}
for key, val in dist_q.items():
q_normed[key] = val / q_sum
total = 0
for key, val in p_normed.items():
if key in q_normed:
total += (np.sqrt(val) - np.sqrt(q_normed[key])) ** 2
del q_normed[key]
else:
total += val
total += sum(q_normed.values())
dist = np.sqrt(total) / np.sqrt(2)
return dist
def hellinger_fidelity(dist_p: dict, dist_q: dict) -> float:
"""Computes the Hellinger fidelity between
two counts distributions.
The fidelity is defined as :math:`\\left(1-H^{2}\\right)^{2}` where H is the
Hellinger distance. This value is bounded in the range [0, 1].
This is equivalent to the standard classical fidelity
:math:`F(Q,P)=\\left(\\sum_{i}\\sqrt{p_{i}q_{i}}\\right)^{2}` that in turn
is equal to the quantum state fidelity for diagonal density matrices.
Parameters:
dist_p (dict): First dict of counts.
dist_q (dict): Second dict of counts.
Returns:
float: Fidelity
Example:
.. code-block::
from qiskit import QuantumCircuit, execute, BasicAer
from qiskit.quantum_info.analysis import hellinger_fidelity
qc = QuantumCircuit(5, 5)
qc.h(2)
qc.cx(2, 1)
qc.cx(2, 3)
qc.cx(3, 4)
qc.cx(1, 0)
qc.measure(range(5), range(5))
sim = BasicAer.get_backend('qasm_simulator')
res1 = execute(qc, sim).result()
res2 = execute(qc, sim).result()
hellinger_fidelity(res1.get_counts(), res2.get_counts())
References:
`Quantum Fidelity @ wikipedia <https://en.wikipedia.org/wiki/Fidelity_of_quantum_states>`_
`Hellinger Distance @ wikipedia <https://en.wikipedia.org/wiki/Hellinger_distance>`_
"""
dist = hellinger_distance(dist_p, dist_q)
return (1 - dist**2) ** 2
|
https://github.com/Tojarieh97/VQE
|
Tojarieh97
|
import sys
sys.path.append('../../')
import numpy as np
from qiskit import QuantumCircuit
from qiskit.opflow import I, X, Y, Z
import matplotlib.pyplot as plt
from volta.observables import sample_hamiltonian
from volta.hamiltonians import BCS_hamiltonian
EPSILONS = [3, 3]
V = -2
hamiltonian = BCS_hamiltonian(EPSILONS, V)
print(hamiltonian)
eigenvalues, _ = np.linalg.eigh(hamiltonian.to_matrix())
print(f"Eigenvalues: {eigenvalues}")
def create_circuit(n: int) -> list:
return [QuantumCircuit(n) for _ in range(2)]
n = hamiltonian.num_qubits
init_states = create_circuit(n)
def copy_unitary(list_states: list) -> list:
out_states = []
for state in list_states:
out_states.append(state.copy())
return out_states
import textwrap
def apply_initialization(list_states: list) -> None:
for ind, state in enumerate(list_states):
b = bin(ind)[2:]
if len(b) != n:
b = '0'*(n - len(b)) + b
spl = textwrap.wrap(b, 1)
for qubit, val in enumerate(spl):
if val == '1':
state.x(qubit)
apply_initialization(init_states)
initialization = copy_unitary(init_states)
from qiskit.circuit.library import TwoLocal
ansatz = TwoLocal(hamiltonian.num_qubits, ['ry','rz'], 'cx', reps=2)
def apply_ansatz(ansatz: QuantumCircuit, list_states: list) -> None:
for states in list_states:
states.append(ansatz, range(n))
apply_ansatz(ansatz, init_states)
init_states[1].draw('mpl')
w = np.arange(n, 0, -1)
w
def _apply_varform_params(ansatz, params: list):
"""Get an hardware-efficient ansatz for n_qubits
given parameters.
"""
# Define variational Form
var_form = ansatz
# Get Parameters from the variational form
var_form_params = sorted(var_form.parameters, key=lambda p: p.name)
# Check if the number of parameters is compatible
assert len(var_form_params) == len(params), "The number of parameters don't match"
# Create a dictionary with the parameters and values
param_dict = dict(zip(var_form_params, params))
# Assing those values for the ansatz
wave_function = var_form.assign_parameters(param_dict)
return wave_function
from qiskit import BasicAer
from qiskit.utils import QuantumInstance
def cost_function(params:list) -> float:
backend = BasicAer.get_backend('qasm_simulator')
backend = QuantumInstance(backend, shots=10000)
cost = 0
# Define Ansatz
for i, state in enumerate(init_states):
qc = _apply_varform_params(state, params)
# Hamiltonian
hamiltonian_eval = sample_hamiltonian(hamiltonian=hamiltonian,
ansatz=qc,
backend=backend)
cost += w[i] * hamiltonian_eval
return cost
from qiskit.aqua.components.optimizers import COBYLA
optimizer = COBYLA(maxiter=1000)
n_parameters = len(init_states[0].parameters)
params = np.random.rand(n_parameters)
optimal_params, mean_energy, n_iters = optimizer.optimize(num_vars=n_parameters,
objective_function=cost_function,
initial_point=params)
mean_energy
# Optimized first ansatz
ansatz_1 = _apply_varform_params(ansatz, optimal_params)
ansatz_1.name = 'U(θ)'
apply_ansatz(ansatz_1, init_states)
init_states[2].draw()
from qiskit.aqua import QuantumInstance
def cost_function_ind(ind: int, params:list) -> float:
backend = BasicAer.get_backend('qasm_simulator')
backend = QuantumInstance(backend, shots=10000)
cost = 0
# Define Ansatz
qc = _apply_varform_params(init_states[ind], params)
# Hamiltonian
hamiltonian_eval = sample_hamiltonian(hamiltonian=hamiltonian,
ansatz=qc,
backend=backend)
cost += hamiltonian_eval
return - cost
from functools import partial
cost = partial(cost_function_ind, 2)
n_parameters = len(init_states[0].parameters)
params = np.random.rand(n_parameters)
optimal_params, energy_gs, n_iters = optimizer.optimize(num_vars=n_parameters,
objective_function=cost,
initial_point=params)
energy_gs
eigenvalues
# Optimized second ansatz
ansatz_2 = _apply_varform_params(ansatz, optimal_params)
ansatz_2.name = 'V(ϕ)'
|
https://github.com/GabrielPontolillo/QiskitPBT
|
GabrielPontolillo
|
# class that inherits from property based test
from qiskit import QuantumCircuit
from QiskitPBT.property import Property
from QiskitPBT.input_generators import RandomState, RandomUnitary
from QiskitPBT.case_studies.quantum_teleportation.quantum_teleportation import quantum_teleportation
class UnitaryBeforeAndAfterTeleport(Property):
# specify the inputs that are to be generated
def get_input_generators(self):
state = RandomState(1)
unitary = RandomUnitary(1, 1)
return [state, unitary]
# specify the preconditions for the test
def preconditions(self, q0, unitary):
return True
# specify the operations to be performed on the input
def operations(self, q0, unitary):
# apply unitary on first qubit then teleport
qc = QuantumCircuit(3, 3)
qc.initialize(q0, [0])
qc.append(unitary, [0])
qt = quantum_teleportation()
# stitch qc and quantum_teleportation together
qc = qc.compose(qt)
# apply teleport then apply unitary on third qubit
qc2 = QuantumCircuit(3, 3)
qc2.initialize(q0, [0])
qt2 = quantum_teleportation()
# stitch qc and quantum_teleportation together
qc2 = qc2.compose(qt2)
qc2.append(unitary, [2])
self.statistical_analysis.assert_equal(self, [0, 1, 2], qc, [0, 1, 2], qc2)
|
https://github.com/minnukota381/Quantum-Computing-Qiskit
|
minnukota381
|
import qiskit as q
%matplotlib inline
from qiskit.visualization import plot_histogram
from qiskit.tools.visualization import plot_bloch_multivector
#secretnum = '10100'
# convert decimal num to binary
a = int(input("Enter Secret Number:"))
secretnum = "{0:b}".format(a)
circuit = q.QuantumCircuit(len(secretnum)+1, len(secretnum))
# circuit.h([0,1,2,3,4,5])
circuit.h(range(len(secretnum)))
# circuit.x(6)
# circuit.h(6)
circuit.x(len(secretnum))
circuit.h(len(secretnum))
circuit.barrier()
for i, j in enumerate(reversed(secretnum)):
if j == '1':
circuit.cx(i, len(secretnum))
# circuit.cx(5, 6)
# circuit.cx(3, 6)
# circuit.cx(0, 6)
circuit.barrier()
# circuit.h([0,1,2,3,4,5])
circuit.h(range(len(secretnum)))
circuit.barrier()
# circuit.measure([0,1,2,3,4,5],[0,1,2,3,4,5])
circuit.measure(range(len(secretnum)),range(len(secretnum)))
circuit.barrier()
circuit.draw(output='mpl')
simulator = q.Aer.get_backend('qasm_simulator')
result = q.execute(circuit, backend=simulator, shots=1).result()
counts = result.get_counts()
print(counts)
counts = str(counts)
counts = (counts[2:len(secretnum)+2])
print(counts)
# convert binary num to decimal
num = int(counts, 2)
print("Your Secret Number is :", num)
# convert decimal num to binary
a = "{0:b}".format(50)
print(a)
# convert binary num to decimal
# Convert a to base 2
s = int(a, 2)
print(s)
|
https://github.com/aryashah2k/Quantum-Computing-Collection-Of-Resources
|
aryashah2k
|
#
# your code is here
# (you may find the values by hand (in mind) as well)
#
# vector |v>
print("vector |v>")
values = [-0.1, -0.3, 0.4, 0.5]
total = 0 # summation of squares
for i in range(len(values)):
total += values[i]**2; # add the square of each value
print("total is ",total)
print("the missing part is",1-total)
print("so, the value of 'a' can be",(1-total)**0.5,"or",-(1-total)**0.5) # square root of the missing part
print()
print("vector |u>")
values = [1/(2**0.5), -1/(3**0.5)]
total = 0 # summation of squares
for i in range(len(values)):
total += values[i]**2; # add the square of each value
print("total is ",total)
print("the missing part is",1-total)
# the missing part is 1/b, square of 1/sqrt(b)
# thus, b is 1/missing_part
print("so, the value of 'b' should be",1/(1-total))
#
# you may define your first function in a separate cell
#
# randomly creating a 2-dimensional quantum state
from random import randrange
def random_quantum_state():
first_entry = randrange(-100,101)
second_entry = randrange(-100,101)
length_square = first_entry**2+second_entry**2
while length_square == 0:
first_entry = randrange(-100,101)
second_entry = randrange(-100,101)
length_square = first_entry**2+second_entry**2
first_entry = first_entry / length_square**0.5
second_entry = second_entry / length_square**0.5
return [first_entry,second_entry]
#
# your code is here
#
# testing whether a given quantum state is valid
def is_quantum_state(quantum_state):
length_square = 0
for i in range(len(quantum_state)):
length_square += quantum_state[i]**2
print("summation of entry squares is",length_square)
# there might be precision problem
# the length may be very close to 1 but not exactly 1
# so we use the following trick
if (length_square - 1)**2 < 0.00000001: return True
return False # else
# defining a function for Hadamard multiplication
def hadamard(quantum_state):
result_quantum_state = [0,0] # define with zero entries
result_quantum_state[0] = (1/(2**0.5)) * quantum_state[0] + (1/(2**0.5)) * quantum_state[1]
result_quantum_state[1] = (1/(2**0.5)) * quantum_state[0] - (1/(2**0.5)) * quantum_state[1]
return result_quantum_state
# we are ready
for i in range(10):
picked_quantum_state=random_quantum_state()
print(picked_quantum_state,"this is randomly picked quantum state")
print("Is it valid?",is_quantum_state(picked_quantum_state))
new_quantum_state = hadamard(picked_quantum_state)
print(new_quantum_state,"this is new quantum state")
print("Is it valid?",is_quantum_state(new_quantum_state))
print() # print an empty line
|
https://github.com/qiskit-community/qiskit-translations-staging
|
qiskit-community
|
from qiskit import *
from qiskit.visualization import plot_histogram
# quantum circuit to make a Bell state
bell = QuantumCircuit(2, 2)
bell.h(0)
bell.cx(0, 1)
meas = QuantumCircuit(2, 2)
meas.measure([0,1], [0,1])
# execute the quantum circuit
backend = BasicAer.get_backend('qasm_simulator') # the device to run on
circ = bell.compose(meas)
result = backend.run(transpile(circ, backend), shots=1000).result()
counts = result.get_counts(circ)
print(counts)
plot_histogram(counts)
# Execute 2-qubit Bell state again
second_result = backend.run(transpile(circ, backend), shots=1000).result()
second_counts = second_result.get_counts(circ)
# Plot results with legend
legend = ['First execution', 'Second execution']
plot_histogram([counts, second_counts], legend=legend)
plot_histogram([counts, second_counts], legend=legend, sort='desc', figsize=(15,12),
color=['orange', 'black'], bar_labels=False)
from qiskit.visualization import plot_state_city, plot_bloch_multivector
from qiskit.visualization import plot_state_paulivec, plot_state_hinton
from qiskit.visualization import plot_state_qsphere
# execute the quantum circuit
backend = BasicAer.get_backend('statevector_simulator') # the device to run on
result = backend.run(transpile(bell, backend)).result()
psi = result.get_statevector(bell)
plot_state_city(psi)
plot_state_hinton(psi)
plot_state_qsphere(psi)
plot_state_paulivec(psi)
plot_bloch_multivector(psi)
plot_state_city(psi, title="My City", color=['black', 'orange'])
plot_state_hinton(psi, title="My Hinton")
plot_state_paulivec(psi, title="My Paulivec", color=['purple', 'orange', 'green'])
plot_bloch_multivector(psi, title="My Bloch Spheres")
from qiskit.visualization import plot_bloch_vector
plot_bloch_vector([0,1,0])
plot_bloch_vector([0,1,0], title='My Bloch Sphere')
import qiskit.tools.jupyter
%qiskit_version_table
%qiskit_copyright
|
https://github.com/jonasmaziero/computacao_quantica_qiskit
|
jonasmaziero
|
import numpy as np
from sklearn.datasets.samples_generator import make_blobs
from qiskit.aqua.utils import split_dataset_to_data_and_labels
from sklearn import svm
from utility import breast_cancer_pca
from matplotlib import pyplot as plt
%matplotlib inline
%load_ext autoreload
%autoreload 2
n = 2 # number of principal components kept
training_dataset_size = 20
testing_dataset_size = 10
sample_Total, training_input, test_input, class_labels = breast_cancer_pca(training_dataset_size, testing_dataset_size, n)
data_train, _ = split_dataset_to_data_and_labels(training_input)
data_test, _ = split_dataset_to_data_and_labels(test_input)
print (f"data_train[0].shape: {data_train[0].shape}" )
print (f"data_train[1].shape: {data_train[1].shape}" )
# We use the function of scikit learn to generate linearly separable blobs
centers = [(2.5,0),(0,2.5)]
x, y = make_blobs(n_samples=100, centers=centers, n_features=2,random_state=0,cluster_std=0.5)
fig,ax=plt.subplots(1,2,figsize=(12,4))
ax[0].scatter(data_train[0][:,0],data_train[0][:,1],c=data_train[1])
ax[0].set_title('Breast Cancer dataset');
ax[1].scatter(x[:,0],x[:,1],c=y)
ax[1].set_title('Blobs linearly separable');
plt.scatter(data_train[0][:,0],data_train[0][:,1],c=data_train[1])
plt.title('Breast Cancer dataset');
model= svm.LinearSVC()
model.fit(data_train[0], data_train[1])
#small utility function
# some utility functions
def make_meshgrid(x, y, h=.02):
x_min, x_max = x.min() - 1, x.max() + 1
y_min, y_max = y.min() - 1, y.max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
return xx, yy
def plot_contours(ax, clf, xx, yy, **params):
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
out = ax.contourf(xx, yy, Z, **params)
return out
accuracy_train = model.score(data_train[0], data_train[1])
accuracy_test = model.score(data_test[0], data_test[1])
X0, X1 = data_train[0][:, 0], data_train[0][:, 1]
xx, yy = make_meshgrid(X0, X1)
Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
fig,ax=plt.subplots(1,2,figsize=(15,5))
ax[0].contourf(xx, yy, Z, cmap=plt.cm.coolwarm)
ax[0].scatter(data_train[0][:,0], data_train[0][:,1], c=data_train[1])
ax[0].set_title('Accuracy on the training set: '+str(accuracy_train));
ax[1].contourf(xx, yy, Z, cmap=plt.cm.coolwarm)
ax[1].scatter(data_test[0][:,0], data_test[0][:,1], c=data_test[1])
ax[1].set_title('Accuracy on the test set: '+str(accuracy_test));
clf = svm.SVC(gamma = 'scale')
clf.fit(data_train[0], data_train[1]);
accuracy_train = clf.score(data_train[0], data_train[1])
accuracy_test = clf.score(data_test[0], data_test[1])
X0, X1 = data_train[0][:, 0], data_train[0][:, 1]
xx, yy = make_meshgrid(X0, X1)
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
fig,ax=plt.subplots(1,2,figsize=(15,5))
ax[0].contourf(xx, yy, Z, cmap=plt.cm.coolwarm)
ax[0].scatter(data_train[0][:,0], data_train[0][:,1], c=data_train[1])
ax[0].set_title('Accuracy on the training set: '+str(accuracy_train));
ax[1].contourf(xx, yy, Z, cmap=plt.cm.coolwarm)
ax[1].scatter(data_test[0][:,0], data_test[0][:,1], c=data_test[1])
ax[1].set_title('Accuracy on the test set: '+str(accuracy_test));
import qiskit as qk
# Creating Qubits
q = qk.QuantumRegister(2)
# Creating Classical Bits
c = qk.ClassicalRegister(2)
circuit = qk.QuantumCircuit(q, c)
circuit.draw('mpl')
# Initialize empty circuit
circuit = qk.QuantumCircuit(q, c)
# Hadamard Gate on the first Qubit
circuit.h(q[0])
# CNOT Gate on the first and second Qubits
circuit.cx(q[0], q[1])
# Measuring the Qubits
circuit.measure(q, c)
circuit.draw('mpl')
# Using Qiskit Aer's Qasm Simulator: Define where do you want to run the simulation.
simulator = qk.BasicAer.get_backend('qasm_simulator')
# Simulating the circuit using the simulator to get the result
job = qk.execute(circuit, simulator, shots=100)
result = job.result()
# Getting the aggregated binary outcomes of the circuit.
counts = result.get_counts(circuit)
print (counts)
from qiskit.aqua.components.feature_maps import SecondOrderExpansion
feature_map = SecondOrderExpansion(feature_dimension=2, depth=1)
x = np.array([0.6, 0.3])
#feature_map.construct_circuit(x)
print(feature_map.construct_circuit(x))
from qiskit.aqua.algorithms import QSVM
qsvm = QSVM(feature_map, training_input, test_input)
#from qiskit.aqua import run_algorithm, QuantumInstance
from qiskit.aqua import algorithm, QuantumInstance
from qiskit import BasicAer
backend = BasicAer.get_backend('qasm_simulator')
quantum_instance = QuantumInstance(backend, shots=1024, seed_simulator=10598, seed_transpiler=10598)
result = qsvm.run(quantum_instance)
plt.scatter(training_input['Benign'][:,0], training_input['Benign'][:,1])
plt.scatter(training_input['Malignant'][:,0], training_input['Malignant'][:,1])
length_data = len(training_input['Benign']) + len(training_input['Malignant'])
print("size training set: {}".format(length_data))
#print("Matrix dimension: {}".format(result['kernel_matrix_training'].shape))
print("testing success ratio: ", result['testing_accuracy'])
test_set = np.concatenate((test_input['Benign'], test_input['Malignant']))
y_test = qsvm.predict(test_set, quantum_instance)
plt.scatter(test_set[:, 0], test_set[:,1], c=y_test)
plt.show()
plt.scatter(test_input['Benign'][:,0], test_input['Benign'][:,1])
plt.scatter(test_input['Malignant'][:,0], test_input['Malignant'][:,1])
plt.show()
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