Source code for qiskit.aqua.operators.primitive_ops.matrix_op
# This code is part of Qiskit.
#
# (C) Copyright IBM 2020, 2021.
#
# 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 Union, Optional, Set, Dict, List, cast, get_type_hints
import logging
import numpy as np
from scipy.sparse import spmatrix
from qiskit import QuantumCircuit
from qiskit.quantum_info import Operator
from qiskit.circuit import ParameterExpression, Instruction
from qiskit.extensions.hamiltonian_gate import HamiltonianGate
from ..operator_base import OperatorBase
from ..primitive_ops.circuit_op import CircuitOp
from ..list_ops.summed_op import SummedOp
from ..list_ops.tensored_op import TensoredOp
from .primitive_op import PrimitiveOp
from ..legacy.matrix_operator import MatrixOperator
from ...utils import arithmetic
from ... import AquaError
logger = logging.getLogger(__name__)
[docs]class MatrixOp(PrimitiveOp):
""" Class for Operators represented by matrices, backed by Terra's ``Operator`` module.
"""
def __init__(self,
primitive: Union[list, np.ndarray, spmatrix, Operator],
coeff: Union[int, float, 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 not primitive.input_dims() == primitive.output_dims():
raise ValueError('Cannot handle non-square matrices yet.')
super().__init__(primitive, coeff=coeff)
@property
def num_qubits(self) -> int:
return len(self.primitive.input_dims()) # type: ignore
[docs] def add(self, other: OperatorBase) -> OperatorBase:
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)) # type: ignore
# Covers Paulis, Circuits, and all else.
return SummedOp([self, other])
[docs] def adjoint(self) -> OperatorBase:
return MatrixOp(self.primitive.conjugate().transpose(), # type: ignore
coeff=np.conj(self.coeff))
[docs] 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 # type: ignore
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) # type: ignore
[docs] def tensor(self, other: OperatorBase) -> OperatorBase:
if isinstance(other.primitive, Operator): # type: ignore
return MatrixOp(self.primitive.tensor(other.primitive), # type: ignore
coeff=self.coeff * other.coeff) # type: ignore
return TensoredOp([self, other])
[docs] 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), # type: ignore
coeff=new_self.coeff * other.coeff)
return super(MatrixOp, new_self).compose(other)
[docs] def permute(self, permutation: Optional[List[int]] = None) -> 'MatrixOp':
"""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:
AquaError: 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 AquaError("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) # type: ignore
[docs] def to_matrix(self, massive: bool = False) -> np.ndarray:
return self.primitive.data * self.coeff # type: ignore
def __str__(self) -> str:
prim_str = str(self.primitive)
if self.coeff == 1.0:
return prim_str
else:
return "{} * {}".format(self.coeff, prim_str)
[docs] def eval(self,
front: Optional[Union[str, Dict[str, complex], np.ndarray, OperatorBase]] = None
) -> Union[OperatorBase, float, 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,import-outside-toplevel
from ..list_ops import ListOp
from ..state_fns import StateFn, 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) # type: ignore
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 = StateFn(self.to_matrix() @ front.to_matrix())
return new_front
[docs] 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
[docs] def to_instruction(self) -> Instruction:
return (self.coeff * self.primitive).to_instruction() # type: ignore
[docs] def to_legacy_op(self, massive: bool = False) -> MatrixOperator:
return MatrixOperator(self.to_matrix(massive=massive))