Source code for qiskit.opflow.list_ops.tensored_op

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# (C) Copyright IBM 2020, 2023.
# This code is licensed under the Apache License, Version 2.0. You may
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"""TensoredOp Class"""

from functools import partial, reduce
from typing import List, Union, cast, Dict

import numpy as np

from qiskit.circuit import ParameterExpression, QuantumCircuit
from qiskit.opflow.exceptions import OpflowError
from qiskit.opflow.list_ops.list_op import ListOp
from qiskit.opflow.operator_base import OperatorBase
from qiskit.quantum_info import Statevector
from qiskit.utils.deprecation import deprecate_func

[docs]class TensoredOp(ListOp): """Deprecated: A class for lazily representing tensor products of Operators. Often Operators cannot be efficiently tensored to one another, but may be manipulated further so that they can be later. This class holds logic to indicate that the Operators in ``oplist`` are meant to be tensored together, and therefore if they reach a point in which they can be, such as after conversion to QuantumCircuits, they can be reduced by tensor product.""" @deprecate_func( since="0.24.0", package_name="qiskit-terra", additional_msg="For code migration guidelines, visit", ) def __init__( self, oplist: List[OperatorBase], coeff: Union[complex, ParameterExpression] = 1.0, abelian: bool = False, ) -> None: """ Args: oplist: The Operators being tensored. coeff: A coefficient multiplying the operator abelian: Indicates whether the Operators in ``oplist`` are known to mutually commute. """ super().__init__(oplist, combo_fn=partial(reduce, np.kron), coeff=coeff, abelian=abelian) @property def num_qubits(self) -> int: return sum(op.num_qubits for op in self.oplist) @property def distributive(self) -> bool: return False @property def settings(self) -> Dict: """Return settings.""" return {"oplist": self._oplist, "coeff": self._coeff, "abelian": self._abelian} def _expand_dim(self, num_qubits: int) -> "TensoredOp": """Appends I ^ num_qubits to ``oplist``. Choice of PauliOp as identity is arbitrary and can be substituted for other PrimitiveOp identity. Returns: TensoredOp expanded with identity operator. """ # pylint: disable=cyclic-import from ..operator_globals import I return TensoredOp(self.oplist + [I ^ num_qubits], coeff=self.coeff)
[docs] def tensor(self, other: OperatorBase) -> OperatorBase: if isinstance(other, TensoredOp): return TensoredOp(self.oplist + other.oplist, coeff=self.coeff * other.coeff) return TensoredOp(self.oplist + [other], coeff=self.coeff)
# TODO eval should partial trace the input into smaller StateFns each of size # op.num_qubits for each op in oplist. Right now just works through matmul.
[docs] def eval( self, front: Union[str, dict, np.ndarray, OperatorBase, Statevector] = None ) -> Union[OperatorBase, complex]: if self._is_empty(): return 0.0 return cast(Union[OperatorBase, complex], self.to_matrix_op().eval(front=front))
# Try collapsing list or trees of tensor products. # TODO do this smarter
[docs] def reduce(self) -> OperatorBase: reduced_ops = [op.reduce() for op in self.oplist] if self._is_empty(): return self.__class__([], coeff=self.coeff, abelian=self.abelian) reduced_ops = reduce(lambda x, y: x.tensor(y), reduced_ops) * self.coeff if isinstance(reduced_ops, ListOp) and len(reduced_ops.oplist) == 1: return reduced_ops.oplist[0] else: return cast(OperatorBase, reduced_ops)
[docs] def to_circuit(self) -> QuantumCircuit: """Returns the quantum circuit, representing the tensored operator. Returns: The circuit representation of the tensored operator. Raises: OpflowError: for operators where a single underlying circuit can not be produced. """ circuit_op = self.to_circuit_op() # pylint: disable=cyclic-import from ..state_fns.circuit_state_fn import CircuitStateFn from ..primitive_ops.primitive_op import PrimitiveOp if isinstance(circuit_op, (PrimitiveOp, CircuitStateFn)): return circuit_op.to_circuit() raise OpflowError( "Conversion to_circuit supported only for operators, where a single " "underlying circuit can be produced." )
[docs] def to_matrix(self, massive: bool = False) -> np.ndarray: OperatorBase._check_massive("to_matrix", True, self.num_qubits, massive) mat = self.coeff * reduce(np.kron, [np.asarray(op.to_matrix()) for op in self.oplist]) return np.asarray(mat, dtype=complex)