Código fonte de qiskit.synthesis.evolution.qdrift
# This code is part of Qiskit.
# (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.
from typing import Union, Optional, Callable
import numpy as np
from qiskit.circuit.quantumcircuit import QuantumCircuit
from qiskit.quantum_info.operators import SparsePauliOp, Pauli
from qiskit.utils import algorithm_globals
from .product_formula import ProductFormula
from .lie_trotter import LieTrotter
r"""The QDrift Trotterization method, which selects each each term in the
Trotterization randomly, with a probability proportional to its weight. Based on the work
of Earl Campbell in Ref. .
: E. Campbell, "A random compiler for fast Hamiltonian simulation" (2018).
reps: int = 1,
insert_barriers: bool = False,
cx_structure: str = "chain",
Callable[[Union[Pauli, SparsePauliOp], float], QuantumCircuit]
] = None,
) -> None:
reps: The number of times to repeat the Trotterization circuit.
insert_barriers: Whether to insert barriers between the atomic evolutions.
cx_structure: How to arrange the CX gates for the Pauli evolutions, can be
"chain", where next neighbor connections are used, or "fountain", where all
qubits are connected to one.
atomic_evolution: A function to construct the circuit for the evolution of single
Pauli string. Per default, a single Pauli evolution is decomopsed in a CX chain
and a single qubit Z rotation.
super().__init__(1, reps, insert_barriers, cx_structure, atomic_evolution)
self.sampled_ops = None
[documentos] def synthesize(self, evolution):
# get operators and time to evolve
operators = evolution.operator
time = evolution.time
if not isinstance(operators, list):
pauli_list = [(Pauli(op), coeff) for op, coeff in operators.to_list()]
coeffs = [np.real(coeff) for op, coeff in operators.to_list()]
pauli_list = [(op, 1) for op in operators]
coeffs = [1 for op in operators]
# We artificially make the weights positive
weights = np.abs(coeffs)
lambd = np.sum(weights)
num_gates = int(np.ceil(2 * (lambd**2) * (time**2) * self.reps))
# The protocol calls for the removal of the individual coefficients,
# and multiplication by a constant evolution time.
evolution_time = lambd * time / num_gates
self.sampled_ops = algorithm_globals.random.choice(
p=weights / lambd,
# Update the coefficients of sampled_ops
self.sampled_ops = [(op, evolution_time) for op, coeff in self.sampled_ops]
# pylint: disable=cyclic-import
from qiskit.circuit.library.pauli_evolution import PauliEvolutionGate
# Build the evolution circuit using the LieTrotter synthesis with the sampled operators
lie_trotter = LieTrotter(
evolution_circuit = PauliEvolutionGate(
sum(SparsePauliOp(op) for op, coeff in self.sampled_ops),