Source code for pyquil.api._wavefunction_simulator

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# Copyright 2018 Rigetti Computing
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from typing import List, Union, Optional, Dict, Any

import numpy as np
from six import integer_types

from pyquil.api._base_connection import ForestConnection
from pyquil.api._error_reporting import _record_call
from pyquil.paulis import PauliSum, PauliTerm
from pyquil.quil import Program, MemoryReference, percolate_declares
from pyquil.gates import MOVE
from pyquil.wavefunction import Wavefunction


[docs]class WavefunctionSimulator: @_record_call def __init__(self, connection: ForestConnection = None, random_seed: Optional[int] = None) -> None: """ A simulator that propagates a wavefunction representation of a quantum state. :param connection: A connection to the Forest web API. :param random_seed: A seed for the simulator's random number generators. Either None (for an automatically generated seed) or a non-negative integer. """ if connection is None: connection = ForestConnection() self.connection = connection if random_seed is None: self.random_seed = None elif isinstance(random_seed, integer_types) and random_seed >= 0: self.random_seed = random_seed else: raise TypeError("random_seed should be None or a non-negative int")
[docs] @_record_call def wavefunction(self, quil_program: Program, memory_map: Dict[MemoryReference, Any] = None) -> Wavefunction: """ Simulate a Quil program and return the wavefunction. .. note:: If your program contains measurements or noisy gates, this method may not do what you want. If the execution of ``quil_program`` is **non-deterministic** then the final wavefunction only represents a stochastically generated sample and the wavefunctions returned by *different* ``wavefunction`` calls *will generally be different*. :param quil_program: A Quil program. :param memory_map: An assignment of classical registers to values, representing an initial state for the QAM's classical memory. :return: A Wavefunction object representing the state of the QVM. """ if memory_map is not None: quil_program = self.augment_program_with_memory_values(quil_program, memory_map) return self.connection._wavefunction(quil_program=quil_program, random_seed=self.random_seed)
[docs] @_record_call def expectation(self, prep_prog: Program, pauli_terms: Union[PauliSum, List[PauliTerm]], memory_map: Dict[MemoryReference, Any] = None) -> Union[float, np.ndarray]: """ Calculate the expectation value of Pauli operators given a state prepared by prep_program. If ``pauli_terms`` is a ``PauliSum`` then the returned value is a single ``float``, otherwise the returned value is an array of values, one for each ``PauliTerm`` in the list. .. note:: If your program contains measurements or noisy gates, this method may not do what you want. If the execution of ``quil_program`` is **non-deterministic** then the final wavefunction from which the expectation value is calculated only represents a stochastically generated sample and the wavefunctions returned by *different* ``wavefunction`` calls *will generally be different*. :param prep_prog: A program that prepares the state on which we measure the expectation. :param pauli_terms: A Pauli representation of a quantum operator. :param memory_map: An assignment of classical registers to values, representing an initial state for the QAM's classical memory. :return: Either a float or array floats depending on ``pauli_terms``. """ is_pauli_sum = False if isinstance(pauli_terms, PauliSum): progs, coeffs = pauli_terms.get_programs() is_pauli_sum = True else: coeffs = np.array([pt.coefficient for pt in pauli_terms]) progs = [pt.program for pt in pauli_terms] if memory_map is not None: prep_prog = self.augment_program_with_memory_values(prep_prog, memory_map) bare_results = self.connection._expectation(prep_prog, progs, random_seed=self.random_seed) results = coeffs * bare_results if is_pauli_sum: return np.sum(results) return results
[docs] @_record_call def run_and_measure(self, quil_program: Program, qubits: List[int] = None, trials: int = 1, memory_map: Dict[MemoryReference, Any] = None) -> np.ndarray: """ Run a Quil program once to determine the final wavefunction, and measure multiple times. Alternatively, consider using ``wavefunction`` and calling ``sample_bitstrings`` on the resulting object. For a large wavefunction and a low-medium number of trials, use this function. On the other hand, if you're sampling a small system many times you might want to use ``Wavefunction.sample_bitstrings``. .. note:: If your program contains measurements or noisy gates, this method may not do what you want. If the execution of ``quil_program`` is **non-deterministic** then the final wavefunction from which the returned bitstrings are sampled itself only represents a stochastically generated sample and the outcomes sampled from *different* ``run_and_measure`` calls *generally sample different bitstring distributions*. :param quil_program: The program to run and measure :param qubits: An optional list of qubits to measure. The order of this list is respected in the returned bitstrings. If not provided, all qubits used in the program will be measured and returned in their sorted order. :param int trials: Number of times to sample from the prepared wavefunction. :param memory_map: An assignment of classical registers to values, representing an initial state for the QAM's classical memory. :return: An array of measurement results (0 or 1) of shape (trials, len(qubits)) """ if qubits is None: qubits = sorted(quil_program.get_qubits(indices=True)) if memory_map is not None: quil_program = self.augment_program_with_memory_values(quil_program, memory_map) return self.connection._run_and_measure(quil_program=quil_program, qubits=qubits, trials=trials, random_seed=self.random_seed)
@staticmethod def augment_program_with_memory_values(quil_program, memory_map): p = Program() for k, v in memory_map.items(): p += MOVE(k, v) p += quil_program return percolate_declares(p)