qiskit.opflow.evolutions.evolved_op의 소스 코드

# 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
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"""EvolutionOp Class"""

from typing import List, Optional, Set, Union, cast

import numpy as np
import scipy

from qiskit.circuit import Instruction, ParameterExpression
from qiskit.opflow.exceptions import OpflowError
from qiskit.opflow.list_ops.composed_op import ComposedOp
from qiskit.opflow.list_ops.list_op import ListOp
from qiskit.opflow.list_ops.summed_op import SummedOp
from qiskit.opflow.list_ops.tensored_op import TensoredOp
from qiskit.opflow.operator_base import OperatorBase
from qiskit.opflow.primitive_ops.matrix_op import MatrixOp
from qiskit.opflow.primitive_ops.primitive_op import PrimitiveOp
from qiskit.quantum_info import Statevector
from qiskit.utils.deprecation import deprecate_func

[문서]class EvolvedOp(PrimitiveOp): r""" Deprecated: Class for wrapping Operator Evolutions for compilation (``convert``) by an EvolutionBase method later, essentially acting as a placeholder. Note that EvolvedOp is a weird case of PrimitiveOp. It happens to be that it fits into the PrimitiveOp interface nearly perfectly, and it essentially represents a placeholder for a PrimitiveOp later, even though it doesn't actually hold a primitive object. We could have chosen for it to be an OperatorBase, but would have ended up copying and pasting a lot of code from PrimitiveOp.""" primitive: PrimitiveOp @deprecate_func( since="0.24.0", additional_msg="For code migration guidelines, visit https://qisk.it/opflow_migration.", ) def __init__( self, primitive: OperatorBase, coeff: Union[complex, ParameterExpression] = 1.0 ) -> None: """ Args: primitive: The operator being wrapped to signify evolution later. coeff: A coefficient multiplying the operator """ super().__init__(primitive, coeff=coeff)
[문서] def primitive_strings(self) -> Set[str]: return self.primitive.primitive_strings()
@property def num_qubits(self) -> int: return self.primitive.num_qubits
[문서] def add(self, other: OperatorBase) -> Union["EvolvedOp", SummedOp]: if not self.num_qubits == other.num_qubits: raise ValueError( "Sum over operators with different numbers of qubits, {} and {}, is not well " "defined".format(self.num_qubits, other.num_qubits) ) if isinstance(other, EvolvedOp) and self.primitive == other.primitive: return EvolvedOp(self.primitive, coeff=self.coeff + other.coeff) if isinstance(other, SummedOp): op_list = [cast(OperatorBase, self)] + other.oplist return SummedOp(op_list) return SummedOp([self, other])
[문서] def adjoint(self) -> "EvolvedOp": return EvolvedOp(self.primitive.adjoint() * -1, coeff=self.coeff.conjugate())
[문서] def equals(self, other: OperatorBase) -> bool: if not isinstance(other, EvolvedOp) or not self.coeff == other.coeff: return False return self.primitive == other.primitive
[문서] def tensor(self, other: OperatorBase) -> TensoredOp: if isinstance(other, TensoredOp): return TensoredOp([cast(OperatorBase, self)] + other.oplist) return TensoredOp([self, other])
def _expand_dim(self, num_qubits: int) -> TensoredOp: # pylint: disable=cyclic-import from ..operator_globals import I return self.tensor(I ^ num_qubits)
[문서] def permute(self, permutation: List[int]) -> "EvolvedOp": return EvolvedOp(self.primitive.permute(permutation), coeff=self.coeff)
[문서] def compose( self, other: OperatorBase, permutation: Optional[List[int]] = None, front: bool = False ) -> OperatorBase: new_self, other = self._expand_shorter_operator_and_permute(other, permutation) if front: return other.compose(new_self) if isinstance(other, ComposedOp): return ComposedOp([new_self] + other.oplist) return ComposedOp([new_self, other])
def __str__(self) -> str: prim_str = str(self.primitive) if self.coeff == 1.0: return f"e^(-i*{prim_str})" else: return f"{self.coeff} * e^(-i*{prim_str})" def __repr__(self) -> str: return f"EvolvedOp({repr(self.primitive)}, coeff={self.coeff})"
[문서] def reduce(self) -> "EvolvedOp": return EvolvedOp(self.primitive.reduce(), coeff=self.coeff)
[문서] def assign_parameters(self, param_dict: dict) -> Union["EvolvedOp", ListOp]: param_value = self.coeff if isinstance(self.coeff, ParameterExpression): unrolled_dict = self._unroll_param_dict(param_dict) if isinstance(unrolled_dict, list): return ListOp([self.assign_parameters(param_dict) for param_dict in unrolled_dict]) if self.coeff.parameters <= set(unrolled_dict.keys()): binds = {param: unrolled_dict[param] for param in self.coeff.parameters} param_value = float(self.coeff.bind(binds)) return EvolvedOp(self.primitive.bind_parameters(param_dict), coeff=param_value)
[문서] def eval( self, front: Optional[Union[str, dict, np.ndarray, OperatorBase, Statevector]] = None ) -> Union[OperatorBase, complex]: return cast(Union[OperatorBase, complex], self.to_matrix_op().eval(front=front))
[문서] def to_matrix(self, massive: bool = False) -> np.ndarray: if ( isinstance(self.primitive, ListOp) and self.primitive.__class__.__name__ == ListOp.__name__ ): return np.array( [ op.exp_i().to_matrix(massive=massive) * self.primitive.coeff * self.coeff for op in self.primitive.oplist ], dtype=complex, ) prim_mat = -1.0j * self.primitive.to_matrix() return scipy.linalg.expm(prim_mat) * self.coeff
[문서] def to_matrix_op(self, massive: bool = False) -> Union[ListOp, MatrixOp]: """Returns a ``MatrixOp`` equivalent to this Operator.""" primitive = self.primitive if isinstance(primitive, ListOp) and primitive.__class__.__name__ == ListOp.__name__: return ListOp( [op.exp_i().to_matrix_op() for op in primitive.oplist], coeff=primitive.coeff * self.coeff, ) prim_mat = EvolvedOp(primitive).to_matrix(massive=massive) return MatrixOp(prim_mat, coeff=self.coeff)
[문서] def log_i(self, massive: bool = False) -> OperatorBase: return self.primitive * self.coeff
[문서] def to_instruction(self, massive: bool = False) -> Instruction: mat_op = self.to_matrix_op(massive=massive) if not isinstance(mat_op, MatrixOp): raise OpflowError("to_instruction is not allowed for ListOp.") return mat_op.to_instruction()