def run_qv_circuits(n_qubits, n_trials, qr, qc, backend, basis_gates, shots):
"""
Args:
n_qubits <int>: number of qubits
n_trials <int>: number of random circuits to create for each subset
qr <QuantumRegister>: instance of QuantumRegister
qc <QuantumCircuit>: instance of QuantumCircuit
backend <AerBackend>: instance of Aer backend
basis_gates <list>: list of elementary gates
shots <int>: number of sampling shots
"""
# list of list of qubit subsets to generate QV circuits
# assuming: n_qunits > 2
qubit_lists = [list(range(n)) for n in range(3, n_qubits + 1)]
# QV sequences qv_circs, which is a list of lists of quantum volume circuits
qv_circs, qv_circs_nomeas = qv.qv_circuits(qubit_lists, n_trials, qr, qc)
sampling_results = []
for trial in range(n_trials):
sampling_results += [
execute(qv_circs[trial],
basis_gates=basis_gates,
backend=backend,
shots=shots).result()
]
return sampling_results
def test_qv_circuits_with_seed(self):
"""Ensure seed is propogated to QuantumVolme objects."""
qubit_lists = [list(range(5))]
qv_circs, qv_circs_no_meas = qv.qv_circuits(qubit_lists, seed=3)
meas_name = qv_circs[0][0].data[0][0].name
no_meas_name = qv_circs_no_meas[0][0].data[0][0].name
self.assertEqual(int(meas_name.split(',')[-1].rstrip(']')), 811)
self.assertEqual(int(no_meas_name.split(',')[-1].rstrip(']')), 811)
def test_measurements_in_circuits_qubit_list_gap(self):
"""Test that there are no measurement instructions in output nomeas circuits."""
qubit_lists = [[1, 3, 5, 7]]
qv_circs, qv_circs_no_meas = qv.qv_circuits(qubit_lists)
qv_circs_measure_qubits = [
x[1][0].index for x in qv_circs[0][0].data
if x[0].name == 'measure'
]
self.assertNotIn('measure',
[x[0].name for x in qv_circs_no_meas[0][0].data])
self.assertEqual([1, 3, 5, 7], qv_circs_measure_qubits)
def test_measurements_in_circuits(self):
"""Ensure measurements are set or not on output circuits."""
qubit_lists = [list(range(4))]
qv_circs, qv_circs_no_meas = qv.qv_circuits(qubit_lists)
qv_circs_measure_qubits = [
x[1][0].index for x in qv_circs[0][0].data
if x[0].name == 'measure'
]
self.assertNotIn('measure',
[x[0].name for x in qv_circs_no_meas[0][0].data])
self.assertEqual([0, 1, 2, 3], qv_circs_measure_qubits)
def quantumvolume(nbits=6, measure=True):
"""
This is a nbit quantum volume circuit
reference: https://qiskit.org/textbook/ch-quantum-hardware/measuring-quantum-volume.html
"""
qubit_lists = [list(range(0, nbits))]
qv_circs, qv_circs_nomeas = qv.qv_circuits(qubit_lists, 1)
if measure is True:
return qv_circs[0][0]
else:
return qv_circs_nomeas[0][0]
def qv_circuit_execution(qubit_lists: list, ntrials: int, shots: int):
"""
create quantum volume circuits, simulate the ideal state and run a noisy simulation
Args:
qubit_lists (list): list of lists of qubits to apply qv circuits to
ntrials (int): number of iterations (number of circuits)
shots (int): number of shots per simulation
Returns:
tuple: a tuple of 2 lists:
list of Results of the ideal statevector simulations
list of Results of the noisy circuits simulations
"""
# create the qv circuit
qv_circs, qv_circs_nomeas = qv.qv_circuits(qubit_lists, ntrials)
# get the ideal state
statevector_backend = qiskit.Aer.get_backend('statevector_simulator')
ideal_results = []
for trial in range(ntrials):
ideal_results.append(
qiskit.execute(qv_circs_nomeas[trial],
backend=statevector_backend).result())
# define noise_model
noise_model = NoiseModel()
p1q = 0.002
p2q = 0.02
noise_model.add_all_qubit_quantum_error(depolarizing_error(p1q, 1), 'u2')
noise_model.add_all_qubit_quantum_error(depolarizing_error(2 * p1q, 1),
'u3')
noise_model.add_all_qubit_quantum_error(depolarizing_error(p2q, 2), 'cx')
# get the noisy results
backend = qiskit.Aer.get_backend('qasm_simulator')
basis_gates = ['u1', 'u2', 'u3', 'cx'] # use U,CX for now
exp_results = []
for trial in range(ntrials):
print('Running trial %d' % trial)
exp_results.append(
qiskit.execute(qv_circs[trial],
basis_gates=basis_gates,
backend=backend,
noise_model=noise_model,
shots=shots,
seed_simulator=SEED,
backend_options={
'max_parallel_experiments': 0
}).result())
return ideal_results, exp_results
def test_qv_circuits(self):
""" Test circuit generation """
# Qubit list
qubit_lists = [[0, 1, 2], [0, 1, 2, 4], [0, 1, 2, 4, 7]]
ntrials = 5
qv_circs, _ = qv.qv_circuits(qubit_lists, ntrials)
self.assertEqual(len(qv_circs), ntrials, "Error: Not enough trials")
self.assertEqual(
len(qv_circs[0]), len(qubit_lists),
"Error: Not enough circuits for the "
"number of specified qubit lists")
def run_qv_circuits(n_qubits, n_trials, qr, qc, backend):
"""
Args:
n_qubits <int>: number of qubits
n_trials <int>: number of random circuits to create for each subset
qr <QuantumRegister>: instance of QuantumRegister
qc <QuantumCircuit>: instance of QuantumCircuit
backend <AerBackend>: instance of Aer backend
"""
# list of list of qubit subsets to generate QV circuits
# assuming: n_qunits > 2
qubit_lists = [list(range(n)) for n in range(3, n_qubits + 1)]
# QV sequences qv_circs, which is a list of lists of quantum volume circuits
_, qv_circs_nomeas = qv.qv_circuits(qubit_lists, n_trials, qr, qc)
ideal_results = []
for trial in range(n_trials):
ideal_results += [
execute(qv_circs_nomeas[trial], backend=backend).result()
]
# validate(qubit_lists, ideal_results)
return ideal_results
import numpy as np
import matplotlib.pyplot as plt
from qiskit import *
from qiskit.providers.aer.noise import NoiseModel
from qiskit.providers.aer.noise.errors.standard_errors import depolarizing_error, thermal_relaxation_error
import qiskit.ignis.verification.quantum_volume as qv
qubit_lists = [[2, 3, 4], [1, 2, 3, 4], [0, 1, 2, 3, 4]]
ntrials = 50
qv_circs, qv_circs_nomeas = qv.qv_circuits(qubit_lists, ntrials)
qv_circs_nomeas[0] = qiskit.compiler.transpile(
qv_circs_nomeas[0], basis_gates=['u1', 'u2', 'u3', 'cx'])
backend = Aer.get_backend('statevector_simulator')
ideal_results = []
for trial in range(ntrials):
print('Simulating trial %d' % trial)
ideal_results.append(
execute(qv_circs_nomeas[trial], backend=backend,
optimization_level=0).result())
qv_fitter = qv.QVFitter(qubit_lists=qubit_lists)
qv_fitter.add_statevectors(ideal_results)
# define noise model
n = 5
T1 = [j * 20 for j in range(1, n + 1)]
T2 = [2 * t1 for t1 in T1]
