Source code for mitiq.benchmarks.random_circuits

# Copyright (C) 2020 Unitary Fund
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"""Contains methods used for testing Mitiq's performance on random circuits."""
from typing import Tuple, Callable, Union, Optional
import numpy as np

from cirq.testing import random_circuit
from cirq import NamedQubit, Circuit

from mitiq.zne import execute_with_zne
from mitiq._typing import QPROGRAM
from mitiq.zne.inference import Factory
from mitiq.zne.scaling import fold_gates_at_random
from mitiq.benchmarks.utils import noisy_simulation


[docs]def sample_projector( n_qubits: int, seed: Optional[Union[int, np.random.RandomState]] = None ) -> np.ndarray: """Constructs a projector on a random computational basis state of n_qubits. Args: n_qubits: A number of qubits seed: Optional seed for random number generator. Returns: A random computational basis projector on n_qubits. E.g., for two qubits this could be ``np.diag([0, 0, 0, 1])``, corresponding to the projector on the :math:`\\left|11\\right\\rangle` state. """ obs = np.zeros(int(2 ** n_qubits)) if seed is None: rnd_state = np.random elif isinstance(seed, int): rnd_state = np.random.RandomState(seed) else: rnd_state = seed chosenZ = rnd_state.randint(2 ** n_qubits) obs[chosenZ] = 1 return np.diag(obs)
[docs]def rand_circuit_zne( n_qubits: int, depth: int, trials: int, noise: float, fac: Optional[Factory] = None, scale_noise: Callable[[QPROGRAM, float], QPROGRAM] = fold_gates_at_random, op_density: float = 0.99, silent: bool = True, seed: Optional[int] = None, ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: """Benchmarks a zero-noise extrapolation method and noise scaling executor by running on randomly sampled quantum circuits. Args: n_qubits: The number of qubits. depth: The depth in moments of the random circuits. trials: The number of random circuits to average over. noise: The noise level of the depolarizing channel for simulation. fac: The Factory giving the extrapolation method. scale_noise: The method for scaling noise, e.g. fold_gates_at_random op_density: The expected proportion of qubits that are acted on in any moment. silent: If False will print out statements every tenth trial to track progress. seed: Optional seed for random number generator. Returns: The triple (exacts, unmitigateds, mitigateds) where each is a list whose values are the expectations of that trial in noiseless, noisy, and error-mitigated runs respectively. """ exacts = [] unmitigateds = [] mitigateds = [] qubits = [NamedQubit(str(xx)) for xx in range(n_qubits)] if seed: rnd_state = np.random.RandomState(seed) else: rnd_state = None for ii in range(trials): if not silent and ii % 10 == 0: print(ii) qc = random_circuit( qubits, n_moments=depth, op_density=op_density, random_state=rnd_state, ) wvf = qc.final_state_vector() # calculate the exact obs = sample_projector(n_qubits, seed=rnd_state) exact = np.conj(wvf).T @ obs @ wvf # make sure it is real exact = np.real_if_close(exact) assert np.isreal(exact) # create the simulation type def obs_sim(circ: Circuit) -> float: # we only want the expectation value not the variance # this is why we return [0] return noisy_simulation(circ, noise, obs) # evaluate the noisy answer unmitigated = obs_sim(qc) # evaluate the ZNE answer mitigated = execute_with_zne( circuit=qc, executor=obs_sim, # type: ignore scale_noise=scale_noise, factory=fac, ) exacts.append(exact) unmitigateds.append(unmitigated) mitigateds.append(mitigated) return np.asarray(exacts), np.asarray(unmitigateds), np.asarray(mitigateds)