def simulate_samples(self, repetitions: int) -> study.TrialResult:
"""Samples the circuit
Args:
repetitions: Number of times the circuit is simulated
Returns:
TrialResult from Cirq.Simulator
"""
if self.simulate_ideal:
noise_model = devices.NO_NOISE
else:
noise_model = AQTNoiseModel()
if self.circuit == Circuit():
raise RuntimeError('simulate ideal called without a valid circuit')
sim = DensityMatrixSimulator(noise=noise_model)
result = sim.run(self.circuit, repetitions=repetitions)
return result
def _circuit_to_choi(circuit: Circuit) -> np.ndarray:
"""Returns the density matrix of the Choi state associated to the
input circuit.
The density matrix completely characterizes the quantum channel induced by
the input circuit (including the effect of noise if present).
Args:
circuit: The input circuit.
Returns:
The density matrix of the Choi state associated to the input circuit.
"""
simulator = DensityMatrixSimulator()
num_qubits = len(circuit.all_qubits())
# Copy and remove all operations
full_circ = deepcopy(circuit)[0:0]
full_circ += _max_ent_state_circuit(2 * num_qubits)
full_circ += circuit
return simulator.simulate(full_circ).final_density_matrix # type: ignore
cooling_out_exists = False
if os.path.exists(outfile_reheating):
print(f'output file {outfile_reheating} exists already.')
reheating_out_exists = True
else:
reheating_out_exists = False
if cooling_out_exists and reheating_out_exists:
print('exiting.')
exit()
print('\nBuilding circuit')
stopwatch = time.time()
system = spinmodels.TFIMChain(L, JvB, 1)
system.normalize()
circuit = qdccirq.logsweep_protocol(system, K)
simulator = DensityMatrixSimulator()
print('done in', time.time() - stopwatch, 'seconds.')
# cooling
if not cooling_out_exists:
print('\nRunning cooling simulation')
stopwatch = time.time()
# build maximally-mixed system state tensored with |0><0| fridge
init_state = np.diag(
np.tile(np.array([1 / 2**L, 0], dtype=np.complex64), 2**L))
res = simulator.simulate(
circuit,
initial_state=init_state,
qubit_order=[*system.get_qubits(), qdccirq.FRIDGE])
"""This module contains methods for benchmarking mitiq error extrapolation
against a standard QAOA for MAXCUT.
"""
from typing import Callable, List, Optional, Tuple
import numpy as np
from cirq import Circuit, NamedQubit, X, ZZ, H, DensityMatrixSimulator
from cirq import identity_each as id
from scipy.optimize import minimize
from mitiq import execute_with_zne, QPROGRAM
from mitiq.zne.inference import Factory
from mitiq.benchmarks.utils import noisy_simulation
SIMULATOR = DensityMatrixSimulator()
def make_noisy_backend(
noise: float, obs: np.ndarray
) -> Callable[[Circuit], float]:
"""Helper function to match mitiq's backend type signature.
Args:
noise: The level of depolarizing noise.
obs: The observable that the backend should measure.
Returns:
A mitiq backend function.
"""