qiskit.opflow.state_fns.vector_state_fn のソースコード

# 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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"""VectorStateFn Class"""

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

import numpy as np

from qiskit import QuantumCircuit
from qiskit.circuit import ParameterExpression
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.state_fns.state_fn import StateFn
from qiskit.quantum_info import Statevector
from qiskit.utils import algorithm_globals, arithmetic
from qiskit.utils.deprecation import deprecate_func

[ドキュメント]class VectorStateFn(StateFn): """Deprecated: A class for state functions and measurements which are defined in vector representation, and stored using Terra's ``Statevector`` class. """ primitive: Statevector # TODO allow normalization somehow? @deprecate_func( since="0.24.0", additional_msg="For code migration guidelines, visit https://qisk.it/opflow_migration.", ) def __init__( self, primitive: Union[list, np.ndarray, Statevector] = None, coeff: Union[complex, ParameterExpression] = 1.0, is_measurement: bool = False, ) -> None: """ Args: primitive: The ``Statevector``, NumPy array, or list, which defines the behavior of the underlying function. coeff: A coefficient multiplying the state function. is_measurement: Whether the StateFn is a measurement operator """ # Lists and Numpy arrays representing statevectors are stored # in Statevector objects for easier handling. if isinstance(primitive, (np.ndarray, list)): primitive = Statevector(primitive) super().__init__(primitive, coeff=coeff, is_measurement=is_measurement)
[ドキュメント] def primitive_strings(self) -> Set[str]: return {"Vector"}
@property def num_qubits(self) -> int: return len(self.primitive.dims())
[ドキュメント] def add(self, other: OperatorBase) -> OperatorBase: if not self.num_qubits == other.num_qubits: raise ValueError( "Sum over statefns with different numbers of qubits, {} and {}, is not well " "defined".format(self.num_qubits, other.num_qubits) ) # Right now doesn't make sense to add a StateFn to a Measurement if isinstance(other, VectorStateFn) and self.is_measurement == other.is_measurement: # Covers Statevector and custom. return VectorStateFn( (self.coeff * self.primitive) + (other.primitive * other.coeff), is_measurement=self._is_measurement, ) return SummedOp([self, other])
[ドキュメント] def adjoint(self) -> "VectorStateFn": return VectorStateFn( self.primitive.conjugate(), coeff=self.coeff.conjugate(), is_measurement=(not self.is_measurement), )
[ドキュメント] def permute(self, permutation: List[int]) -> "VectorStateFn": new_self = self new_num_qubits = max(permutation) + 1 if self.num_qubits != len(permutation): # raise OpflowError("New index must be defined for each qubit of the operator.") pass if self.num_qubits < new_num_qubits: # pad the operator with identities new_self = self._expand_dim(new_num_qubits - self.num_qubits) qc = QuantumCircuit(new_num_qubits) # extend the permutation indices to match the size of the new matrix permutation = ( list(filter(lambda x: x not in permutation, range(new_num_qubits))) + permutation ) # decompose permutation into sequence of transpositions transpositions = arithmetic.transpositions(permutation) for trans in transpositions: qc.swap(trans[0], trans[1]) from ..primitive_ops.circuit_op import CircuitOp matrix = CircuitOp(qc).to_matrix() vector = new_self.primitive.data new_vector = cast(np.ndarray, matrix.dot(vector)) return VectorStateFn( primitive=new_vector, coeff=self.coeff, is_measurement=self.is_measurement )
[ドキュメント] def to_dict_fn(self) -> StateFn: """Creates the equivalent state function of type DictStateFn. Returns: A new DictStateFn equivalent to ``self``. """ from .dict_state_fn import DictStateFn num_qubits = self.num_qubits new_dict = {format(i, "b").zfill(num_qubits): v for i, v in enumerate(self.primitive.data)} return DictStateFn(new_dict, coeff=self.coeff, is_measurement=self.is_measurement)
def _expand_dim(self, num_qubits: int) -> "VectorStateFn": primitive = np.zeros(2**num_qubits, dtype=complex) return VectorStateFn( self.primitive.tensor(primitive), coeff=self.coeff, is_measurement=self.is_measurement )
[ドキュメント] def tensor(self, other: OperatorBase) -> OperatorBase: if isinstance(other, VectorStateFn): return StateFn( self.primitive.tensor(other.primitive), coeff=self.coeff * other.coeff, is_measurement=self.is_measurement, ) return TensoredOp([self, other])
[ドキュメント] def to_density_matrix(self, massive: bool = False) -> np.ndarray: OperatorBase._check_massive("to_density_matrix", True, self.num_qubits, massive) return self.primitive.to_operator().data * self.coeff
[ドキュメント] def to_matrix(self, massive: bool = False) -> np.ndarray: OperatorBase._check_massive("to_matrix", False, self.num_qubits, massive) vec = self.primitive.data * self.coeff return vec if not self.is_measurement else vec.reshape(1, -1)
[ドキュメント] def to_matrix_op(self, massive: bool = False) -> OperatorBase: return self
[ドキュメント] def to_circuit_op(self) -> OperatorBase: """Return ``StateFnCircuit`` corresponding to this StateFn.""" # pylint: disable=cyclic-import from .circuit_state_fn import CircuitStateFn csfn = CircuitStateFn.from_vector(self.primitive.data) * self.coeff return csfn.adjoint() if self.is_measurement else csfn
def __str__(self) -> str: prim_str = str(self.primitive) if self.coeff == 1.0: return "{}({})".format( "VectorStateFn" if not self.is_measurement else "MeasurementVector", prim_str ) else: return "{}({}) * {}".format( "VectorStateFn" if not self.is_measurement else "MeasurementVector", prim_str, self.coeff, ) # pylint: disable=too-many-return-statements
[ドキュメント] def eval( self, front: Optional[ Union[str, Dict[str, complex], np.ndarray, Statevector, OperatorBase] ] = None, ) -> Union[OperatorBase, complex]: if front is None: # this object is already a VectorStateFn return self if not self.is_measurement and isinstance(front, OperatorBase): raise ValueError( "Cannot compute overlap with StateFn or Operator if not Measurement. Try taking " "sf.adjoint() first to convert to measurement." ) if isinstance(front, ListOp) and front.distributive: return front.combo_fn( [self.eval(front.coeff * front_elem) for front_elem in front.oplist] ) if not isinstance(front, OperatorBase): front = StateFn(front) # pylint: disable=cyclic-import from ..operator_globals import EVAL_SIG_DIGITS from .operator_state_fn import OperatorStateFn from .circuit_state_fn import CircuitStateFn from .dict_state_fn import DictStateFn if isinstance(front, DictStateFn): return np.round( sum( v * self.primitive.data[int(b, 2)] * front.coeff for (b, v) in front.primitive.items() ) * self.coeff, decimals=EVAL_SIG_DIGITS, ) if isinstance(front, VectorStateFn): # Need to extract the element or np.array([1]) is returned. return np.round( np.dot(self.to_matrix(), front.to_matrix())[0], decimals=EVAL_SIG_DIGITS ) if isinstance(front, CircuitStateFn): # Don't reimplement logic from CircuitStateFn return np.conj(front.adjoint().eval(self.adjoint().primitive)) * self.coeff if isinstance(front, OperatorStateFn): return front.adjoint().eval(self.primitive) * self.coeff return front.adjoint().eval(self.adjoint().primitive).adjoint() * self.coeff # type: ignore
[ドキュメント] def sample( self, shots: int = 1024, massive: bool = False, reverse_endianness: bool = False ) -> dict: deterministic_counts = self.primitive.probabilities_dict() # Don't need to square because probabilities_dict already does. probs = np.array(list(deterministic_counts.values())) unique, counts = np.unique( algorithm_globals.random.choice( list(deterministic_counts.keys()), size=shots, p=(probs / sum(probs)) ), return_counts=True, ) counts = dict(zip(unique, counts)) if reverse_endianness: scaled_dict = {bstr[::-1]: (prob / shots) for (bstr, prob) in counts.items()} else: scaled_dict = {bstr: (prob / shots) for (bstr, prob) in counts.items()} return dict(sorted(scaled_dict.items(), key=lambda x: x[1], reverse=True))