qiskit.opflow.primitive_ops.matrix_op のソースコード

# This code is part of Qiskit.
# (C) Copyright IBM 2020.
# 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.

"""MatrixOp Class """

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

import numpy as np
from scipy.sparse import spmatrix

from qiskit import QuantumCircuit
from qiskit.circuit import Instruction, ParameterExpression
from qiskit.extensions.hamiltonian_gate import HamiltonianGate
from qiskit.opflow.exceptions import OpflowError
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.circuit_op import CircuitOp
from qiskit.opflow.primitive_ops.primitive_op import PrimitiveOp
from qiskit.quantum_info import Operator, Statevector
from qiskit.utils import arithmetic

[ドキュメント]class MatrixOp(PrimitiveOp): """Class for Operators represented by matrices, backed by Terra's ``Operator`` module.""" primitive: Operator def __init__( self, primitive: Union[list, np.ndarray, spmatrix, Operator], coeff: Union[complex, ParameterExpression] = 1.0, ) -> None: """ Args: primitive: The matrix-like object which defines the behavior of the underlying function. coeff: A coefficient multiplying the primitive Raises: TypeError: invalid parameters. ValueError: invalid parameters. """ primitive_orig = primitive if isinstance(primitive, spmatrix): primitive = primitive.toarray() if isinstance(primitive, (list, np.ndarray)): primitive = Operator(primitive) if not isinstance(primitive, Operator): type_hints = get_type_hints(MatrixOp.__init__).get("primitive") valid_cls = [cls.__name__ for cls in type_hints.__args__] raise TypeError( f"MatrixOp can only be instantiated with {valid_cls}, " f"not '{primitive_orig.__class__.__name__}'" ) if primitive.input_dims() != primitive.output_dims(): raise ValueError("Cannot handle non-square matrices yet.") super().__init__(primitive, coeff=coeff)
[ドキュメント] def primitive_strings(self) -> Set[str]: return {"Matrix"}
@property def num_qubits(self) -> int: return len(self.primitive.input_dims())
[ドキュメント] def add(self, other: OperatorBase) -> Union["MatrixOp", 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, MatrixOp) and self.primitive == other.primitive: return MatrixOp(self.primitive, coeff=self.coeff + other.coeff) # Terra's Operator cannot handle ParameterExpressions if ( isinstance(other, MatrixOp) and not isinstance(self.coeff, ParameterExpression) and not isinstance(other.coeff, ParameterExpression) ): return MatrixOp((self.coeff * self.primitive) + (other.coeff * other.primitive)) # Covers Paulis, Circuits, and all else. return SummedOp([self, other])
[ドキュメント] def adjoint(self) -> "MatrixOp": return MatrixOp(self.primitive.adjoint(), coeff=self.coeff.conjugate())
[ドキュメント] def equals(self, other: OperatorBase) -> bool: if not isinstance(other, MatrixOp): return False if isinstance(self.coeff, ParameterExpression) ^ isinstance( other.coeff, ParameterExpression ): return False if isinstance(self.coeff, ParameterExpression) and isinstance( other.coeff, ParameterExpression ): return self.coeff == other.coeff and self.primitive == other.primitive return self.coeff * self.primitive == other.coeff * other.primitive
def _expand_dim(self, num_qubits: int) -> "MatrixOp": identity = np.identity(2**num_qubits, dtype=complex) return MatrixOp(self.primitive.tensor(Operator(identity)), coeff=self.coeff)
[ドキュメント] def tensor(self, other: OperatorBase) -> Union["MatrixOp", TensoredOp]: if isinstance(other, MatrixOp): return MatrixOp(self.primitive.tensor(other.primitive), coeff=self.coeff * other.coeff) return TensoredOp([self, other])
[ドキュメント] 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) new_self = cast(MatrixOp, new_self) if front: return other.compose(new_self) if isinstance(other, MatrixOp): return MatrixOp( new_self.primitive.compose(other.primitive, front=True), coeff=new_self.coeff * other.coeff, ) return super(MatrixOp, new_self).compose(other)
[ドキュメント] def permute(self, permutation: Optional[List[int]] = None) -> OperatorBase: """Creates a new MatrixOp that acts on the permuted qubits. Args: permutation: A list defining where each qubit should be permuted. The qubit at index j should be permuted to position permutation[j]. Returns: A new MatrixOp representing the permuted operator. Raises: OpflowError: if indices do not define a new index for each qubit. """ new_self = self new_matrix_size = max(permutation) + 1 if self.num_qubits != len(permutation): raise OpflowError("New index must be defined for each qubit of the operator.") if self.num_qubits < new_matrix_size: # pad the operator with identities new_self = self._expand_dim(new_matrix_size - self.num_qubits) qc = QuantumCircuit(new_matrix_size) # extend the indices to match the size of the new matrix permutation = ( list(filter(lambda x: x not in permutation, range(new_matrix_size))) + permutation ) # decompose permutation into sequence of transpositions transpositions = arithmetic.transpositions(permutation) for trans in transpositions: qc.swap(trans[0], trans[1]) matrix = CircuitOp(qc).to_matrix() return MatrixOp(matrix.transpose()) @ new_self @ MatrixOp(matrix)
[ドキュメント] def to_matrix(self, massive: bool = False) -> np.ndarray: return self.primitive.data * self.coeff
def __str__(self) -> str: prim_str = str(self.primitive) if self.coeff == 1.0: return prim_str else: return f"{self.coeff} * {prim_str}"
[ドキュメント] def eval( self, front: Optional[ Union[str, Dict[str, complex], np.ndarray, OperatorBase, Statevector] ] = None, ) -> Union[OperatorBase, complex]: # For other ops' eval we return self.to_matrix_op() here, but that's unnecessary here. if front is None: return self # pylint: disable=cyclic-import from ..list_ops import ListOp from ..state_fns import StateFn, VectorStateFn, OperatorStateFn new_front = None # For now, always do this. If it's not performant, we can be more granular. if not isinstance(front, OperatorBase): front = StateFn(front, is_measurement=False) if isinstance(front, ListOp) and front.distributive: new_front = front.combo_fn( [self.eval(front.coeff * front_elem) for front_elem in front.oplist] ) elif isinstance(front, OperatorStateFn): new_front = OperatorStateFn(self.adjoint().compose(front.to_matrix_op()).compose(self)) elif isinstance(front, OperatorBase): new_front = VectorStateFn(self.to_matrix() @ front.to_matrix()) return new_front
[ドキュメント] def exp_i(self) -> OperatorBase: """Return a ``CircuitOp`` equivalent to e^-iH for this operator H""" return CircuitOp(HamiltonianGate(self.primitive, time=self.coeff))
# Op Conversions
[ドキュメント] def to_matrix_op(self, massive: bool = False) -> "MatrixOp": return self
[ドキュメント] def to_instruction(self) -> Instruction: return (self.coeff * self.primitive).to_instruction()