# 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.
"""PauliExpectation Class"""
import logging
from typing import Union
import numpy as np
from qiskit.opflow.converters.abelian_grouper import AbelianGrouper
from qiskit.opflow.converters.pauli_basis_change import PauliBasisChange
from qiskit.opflow.expectations.expectation_base import ExpectationBase
from qiskit.opflow.list_ops.composed_op import ComposedOp
from qiskit.opflow.list_ops.list_op import ListOp
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.opflow.state_fns.operator_state_fn import OperatorStateFn
from qiskit.opflow.state_fns.state_fn import StateFn
from qiskit.utils.deprecation import deprecate_func
logger = logging.getLogger(__name__)
[documentos]class PauliExpectation(ExpectationBase):
r"""
An Expectation converter for Pauli-basis observables by changing Pauli measurements to a
diagonal ({Z, I}^n) basis and appending circuit post-rotations to the measured state function.
Optionally groups the Paulis with the same post-rotations (those that commute with one
another, or form Abelian groups) into single measurements to reduce circuit execution
overhead.
"""
@deprecate_func(
since="0.24.0",
additional_msg="For code migration guidelines, visit https://qisk.it/opflow_migration.",
)
def __init__(self, group_paulis: bool = True) -> None:
"""
Args:
group_paulis: Whether to group the Pauli measurements into commuting sums, which all
have the same diagonalizing circuit.
"""
super().__init__()
self._grouper = AbelianGrouper() if group_paulis else None
[documentos] def convert(self, operator: OperatorBase) -> OperatorBase:
"""Accepts an Operator and returns a new Operator with the Pauli measurements replaced by
diagonal Pauli post-rotation based measurements so they can be evaluated by sampling and
averaging.
Args:
operator: The operator to convert.
Returns:
The converted operator.
"""
if isinstance(operator, ListOp):
return operator.traverse(self.convert).reduce()
if isinstance(operator, OperatorStateFn) and operator.is_measurement:
# Change to Pauli representation if necessary
if (
isinstance(operator.primitive, (ListOp, PrimitiveOp))
and not isinstance(operator.primitive, PauliSumOp)
and {"Pauli", "SparsePauliOp"} < operator.primitive_strings()
):
logger.warning(
"Measured Observable is not composed of only Paulis, converting to "
"Pauli representation, which can be expensive."
)
# Setting massive=False because this conversion is implicit. User can perform this
# action on the Observable with massive=True explicitly if they so choose.
pauli_obsv = operator.primitive.to_pauli_op(massive=False)
operator = StateFn(pauli_obsv, is_measurement=True, coeff=operator.coeff)
if self._grouper and isinstance(operator.primitive, (ListOp, PauliSumOp)):
grouped = self._grouper.convert(operator.primitive)
operator = StateFn(grouped, is_measurement=True, coeff=operator.coeff)
# Convert the measurement into diagonal basis (PauliBasisChange chooses
# this basis by default).
cob = PauliBasisChange(replacement_fn=PauliBasisChange.measurement_replacement_fn)
return cob.convert(operator).reduce()
return operator
[documentos] def compute_variance(self, exp_op: OperatorBase) -> Union[list, float, np.ndarray]:
def sum_variance(operator):
if isinstance(operator, ComposedOp):
sfdict = operator.oplist[1]
measurement = operator.oplist[0]
average = np.asarray(measurement.eval(sfdict))
variance = sum(
(v * (np.asarray(measurement.eval(b)) - average)) ** 2
for (b, v) in sfdict.primitive.items()
)
return operator.coeff * variance
elif isinstance(operator, ListOp):
return operator.combo_fn([sum_variance(op) for op in operator.oplist])
return 0.0
return sum_variance(exp_op)