# This code is part of Qiskit.
# (C) Copyright IBM 2020, 2023.
# 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 List, Union, cast
import numpy as np
from qiskit.opflow.evolutions.trotterizations.trotterization_base import TrotterizationBase
from qiskit.opflow.list_ops.composed_op import ComposedOp
from qiskit.opflow.list_ops.summed_op import SummedOp
from qiskit.opflow.operator_base import OperatorBase
from qiskit.opflow.primitive_ops.pauli_sum_op import PauliSumOp
from qiskit.opflow.primitive_ops.primitive_op import PrimitiveOp
from qiskit.utils import algorithm_globals
from qiskit.utils.deprecation import deprecate_func
# pylint: disable=invalid-name
"""Deprecated: 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 https://arxiv.org/abs/1811.08017.
additional_msg="For code migration guidelines, visit https://qisk.it/opflow_migration.",
def __init__(self, reps: int = 1) -> None:
reps: The number of times to repeat the Trotterization circuit.
[documentos] def convert(self, operator: OperatorBase) -> OperatorBase:
if not isinstance(operator, (SummedOp, PauliSumOp)):
raise TypeError("Trotterization converters can only convert SummedOp or PauliSumOp.")
if not isinstance(operator.coeff, (float, int)):
"Trotterization converters can only convert operators with real coefficients."
operator_iter: Union[PauliSumOp, List[PrimitiveOp]]
if isinstance(operator, PauliSumOp):
operator_iter = operator
coeffs = operator.primitive.coeffs
coeff = operator.coeff
operator_iter = cast(List[PrimitiveOp], operator.oplist)
coeffs = [op.coeff for op in operator_iter]
coeff = operator.coeff
# We artificially make the weights positive, TODO check approximation performance
weights = np.abs(coeffs)
lambd = np.sum(weights)
N = 2 * (lambd**2) * (coeff**2)
factor = lambd * coeff / (N * self.reps)
# The protocol calls for the removal of the individual coefficients,
# and multiplication by a constant factor.
scaled_ops = [(op * (factor / op.coeff)).exp_i() for op in operator_iter]
sampled_ops = algorithm_globals.random.choice(
scaled_ops, size=(int(N * self.reps),), p=weights / lambd