Source code for qiskit.opflow.expectations.pauli_expectation

# 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
# 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__)

[docs]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", ) 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
[docs] 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
[docs] 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)