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Código fuente para qiskit.opflow.list_ops.list_op

# 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.

""" ListOp Operator Class """

from functools import reduce
from numbers import Number
from typing import Any, Callable, Dict, Iterator, List, Optional, Set, Sequence, Union, cast

import numpy as np
from scipy.sparse import spmatrix

from qiskit.circuit import ParameterExpression, QuantumCircuit
from qiskit.opflow.exceptions import OpflowError
from qiskit.opflow.operator_base import OperatorBase
from qiskit.quantum_info import Statevector
from qiskit.utils import arithmetic


[documentos]class ListOp(OperatorBase): """ A Class for manipulating List Operators, and parent class to ``SummedOp``, ``ComposedOp``, and ``TensoredOp``. List Operators are classes for storing and manipulating lists of Operators, State functions, or Measurements, and include some rule or ``combo_fn`` defining how the Operator functions of the list constituents should be combined to form to cumulative Operator function of the ``ListOp``. For example, a ``SummedOp`` has an addition-based ``combo_fn``, so once the Operators in its list are evaluated against some bitstring to produce a list of results, we know to add up those results to produce the final result of the ``SummedOp``'s evaluation. In theory, this ``combo_fn`` can be any function over classical complex values, but for convenience we've chosen for them to be defined over NumPy arrays and values. This way, large numbers of evaluations, such as after calling ``to_matrix`` on the list constituents, can be efficiently combined. While the combination function is defined over classical values, it should be understood as the operation by which each Operators' underlying function is combined to form the underlying Operator function of the ``ListOp``. In this way, the ``ListOps`` are the basis for constructing large and sophisticated Operators, State Functions, and Measurements. The base ``ListOp`` class is particularly interesting, as its ``combo_fn`` is "the identity list Operation". Meaning, if we understand the ``combo_fn`` as a function from a list of complex values to some output, one such function is returning the list as-is. This is powerful for constructing compact hierarchical Operators which return many measurements in multiple dimensional lists. """ def __init__( self, oplist: Sequence[OperatorBase], combo_fn: Optional[Callable] = None, coeff: Union[complex, ParameterExpression] = 1.0, abelian: bool = False, grad_combo_fn: Optional[Callable] = None, ) -> None: """ Args: oplist: The list of ``OperatorBases`` defining this Operator's underlying function. combo_fn: The recombination function to combine classical results of the ``oplist`` Operators' eval functions (e.g. sum). Default is lambda x: x. coeff: A coefficient multiplying the operator abelian: Indicates whether the Operators in ``oplist`` are known to mutually commute. grad_combo_fn: The gradient of recombination function. If None, the gradient will be computed automatically. Note that the default "recombination function" lambda above is essentially the identity - it accepts the list of values, and returns them in a list. """ super().__init__() self._oplist = self._check_input_types(oplist) self._combo_fn = combo_fn self._coeff = coeff self._abelian = abelian self._grad_combo_fn = grad_combo_fn def _check_input_types(self, oplist): if all(isinstance(x, OperatorBase) for x in oplist): return list(oplist) else: badval = next(x for x in oplist if not isinstance(x, OperatorBase)) raise TypeError(f"ListOp expecting objects of type OperatorBase, got {badval}") def _state( self, coeff: Optional[Union[complex, ParameterExpression]] = None, combo_fn: Optional[Callable] = None, abelian: Optional[bool] = None, grad_combo_fn: Optional[Callable] = None, ) -> Dict: return { "coeff": coeff if coeff is not None else self.coeff, "combo_fn": combo_fn if combo_fn is not None else self.combo_fn, "abelian": abelian if abelian is not None else self.abelian, "grad_combo_fn": grad_combo_fn if grad_combo_fn is not None else self.grad_combo_fn, } @property def settings(self) -> Dict: """Return settings.""" return { "oplist": self._oplist, "combo_fn": self._combo_fn, "coeff": self._coeff, "abelian": self._abelian, "grad_combo_fn": self._grad_combo_fn, } @property def oplist(self) -> List[OperatorBase]: """The list of ``OperatorBases`` defining the underlying function of this Operator. Returns: The Operators defining the ListOp """ return self._oplist
[documentos] @staticmethod def default_combo_fn(x: Any) -> Any: """ListOp default combo function i.e. lambda x: x""" return x
@property def combo_fn(self) -> Callable: """The function defining how to combine ``oplist`` (or Numbers, or NumPy arrays) to produce the Operator's underlying function. For example, SummedOp's combination function is to add all of the Operators in ``oplist``. Returns: The combination function. """ if self._combo_fn is None: return ListOp.default_combo_fn return self._combo_fn @property def grad_combo_fn(self) -> Optional[Callable]: """The gradient of ``combo_fn``.""" return self._grad_combo_fn @property def abelian(self) -> bool: """Whether the Operators in ``oplist`` are known to commute with one another. Returns: A bool indicating whether the ``oplist`` is Abelian. """ return self._abelian @property def distributive(self) -> bool: """Indicates whether the ListOp or subclass is distributive under composition. ListOp and SummedOp are, meaning that (opv @ op) = (opv[0] @ op + opv[1] @ op) (using plus for SummedOp, list for ListOp, etc.), while ComposedOp and TensoredOp do not behave this way. Returns: A bool indicating whether the ListOp is distributive under composition. """ return True @property def coeff(self) -> Union[complex, ParameterExpression]: """The scalar coefficient multiplying the Operator. Returns: The coefficient. """ return self._coeff @property def coeffs(self) -> List[Union[complex, ParameterExpression]]: """Return a list of the coefficients of the operators listed. Raises exception for nested Listops. """ if any(isinstance(op, ListOp) for op in self.oplist): raise TypeError("Coefficients are not returned for nested ListOps.") return [self.coeff * op.coeff for op in self.oplist]
[documentos] def primitive_strings(self) -> Set[str]: return reduce(set.union, [op.primitive_strings() for op in self.oplist])
@property def num_qubits(self) -> int: num_qubits0 = self.oplist[0].num_qubits if not all(num_qubits0 == op.num_qubits for op in self.oplist): raise ValueError("Operators in ListOp have differing numbers of qubits.") return num_qubits0
[documentos] def add(self, other: OperatorBase) -> "ListOp": if self == other: return self.mul(2.0) # Avoid circular dependency # pylint: disable=cyclic-import from .summed_op import SummedOp return SummedOp([self, other])
[documentos] def adjoint(self) -> "ListOp": # TODO do this lazily? Basically rebuilds the entire tree, and ops and adjoints almost # always come in pairs, so an AdjointOp holding a reference could save copying. if self.__class__ == ListOp: return ListOp( [op.adjoint() for op in self.oplist], **self._state(coeff=self.coeff.conjugate()) ) # coeff is conjugated return self.__class__( [op.adjoint() for op in self.oplist], coeff=self.coeff.conjugate(), abelian=self.abelian )
[documentos] def traverse( self, convert_fn: Callable, coeff: Optional[Union[complex, ParameterExpression]] = None ) -> "ListOp": """Apply the convert_fn to each node in the oplist. Args: convert_fn: The function to apply to the internal OperatorBase. coeff: A coefficient to multiply by after applying convert_fn. If it is None, self.coeff is used instead. Returns: The converted ListOp. """ if coeff is None: coeff = self.coeff if self.__class__ == ListOp: return ListOp([convert_fn(op) for op in self.oplist], **self._state(coeff=coeff)) return self.__class__( [convert_fn(op) for op in self.oplist], coeff=coeff, abelian=self.abelian )
[documentos] def equals(self, other: OperatorBase) -> bool: if not isinstance(other, type(self)) or not len(self.oplist) == len(other.oplist): return False # Note, ordering matters here (i.e. different list orders will return False) return self.coeff == other.coeff and all( op1 == op2 for op1, op2 in zip(self.oplist, other.oplist) )
# We need to do this because otherwise Numpy takes over scalar multiplication and wrecks it if # isinstance(scalar, np.number) - this started happening when we added __get_item__(). __array_priority__ = 10000
[documentos] def mul(self, scalar: Union[complex, ParameterExpression]) -> "ListOp": if not isinstance(scalar, (int, float, complex, ParameterExpression)): raise ValueError( "Operators can only be scalar multiplied by float or complex, not " "{} of type {}.".format(scalar, type(scalar)) ) if self.__class__ == ListOp: return ListOp(self.oplist, **self._state(coeff=scalar * self.coeff)) return self.__class__(self.oplist, coeff=scalar * self.coeff, abelian=self.abelian)
[documentos] def tensor(self, other: OperatorBase) -> OperatorBase: # Avoid circular dependency # pylint: disable=cyclic-import from .tensored_op import TensoredOp return TensoredOp([self, other])
[documentos] def tensorpower(self, other: int) -> Union[OperatorBase, int]: # Hack to make op1^(op2^0) work as intended. if other == 0: return 1 if not isinstance(other, int) or other <= 0: raise TypeError("Tensorpower can only take positive int arguments") # Avoid circular dependency # pylint: disable=cyclic-import from .tensored_op import TensoredOp return TensoredOp([self] * other)
def _expand_dim(self, num_qubits: int) -> "ListOp": oplist = [ op._expand_dim(num_qubits + self.num_qubits - op.num_qubits) for op in self.oplist ] return ListOp(oplist, **self._state())
[documentos] def permute(self, permutation: List[int]) -> "OperatorBase": """Permute the qubits of the operator. 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 ListOp representing the permuted operator. Raises: OpflowError: if indices do not define a new index for each qubit. """ new_self = self circuit_size = max(permutation) + 1 try: if self.num_qubits != len(permutation): raise OpflowError("New index must be defined for each qubit of the operator.") except ValueError: raise OpflowError( "Permute is only possible if all operators in the ListOp have the " "same number of qubits." ) from ValueError if self.num_qubits < circuit_size: # pad the operator with identities new_self = self._expand_dim(circuit_size - self.num_qubits) qc = QuantumCircuit(circuit_size) # extend the indices to match the size of the circuit permutation = ( list(filter(lambda x: x not in permutation, range(circuit_size))) + permutation ) # decompose permutation into sequence of transpositions transpositions = arithmetic.transpositions(permutation) for trans in transpositions: qc.swap(trans[0], trans[1]) # pylint: disable=cyclic-import from ..primitive_ops.circuit_op import CircuitOp return CircuitOp(qc.reverse_ops()) @ new_self @ CircuitOp(qc)
[documentos] 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(ListOp, new_self) if front: return other.compose(new_self) # Avoid circular dependency # pylint: disable=cyclic-import from .composed_op import ComposedOp return ComposedOp([new_self, other])
[documentos] def power(self, exponent: int) -> OperatorBase: if not isinstance(exponent, int) or exponent <= 0: raise TypeError("power can only take positive int arguments") # Avoid circular dependency # pylint: disable=cyclic-import from .composed_op import ComposedOp return ComposedOp([self] * exponent)
[documentos] def to_matrix(self, massive: bool = False) -> np.ndarray: OperatorBase._check_massive("to_matrix", True, self.num_qubits, massive) # Combination function must be able to handle classical values. # Note: this can end up, when we have list operators containing other list operators, as a # ragged array and numpy 1.19 raises a deprecation warning unless this is explicitly # done as object type now - was implicit before. mat = self.combo_fn( # pylint: disable=not-callable np.asarray( [op.to_matrix(massive=massive) * self.coeff for op in self.oplist], dtype=object ) ) # Note: As ComposedOp has a combo function of inner product we can end up here not with # a matrix (array) but a scalar. In which case we make a single element array of it. if isinstance(mat, Number): mat = [mat] return np.asarray(mat, dtype=complex)
[documentos] def to_spmatrix(self) -> Union[spmatrix, List[spmatrix]]: """Returns SciPy sparse matrix representation of the Operator. Returns: CSR sparse matrix representation of the Operator, or List thereof. """ # Combination function must be able to handle classical values # pylint: disable=not-callable return self.combo_fn([op.to_spmatrix() for op in self.oplist]) * self.coeff
[documentos] def eval( self, front: Optional[ Union[str, Dict[str, complex], np.ndarray, OperatorBase, Statevector] ] = None, ) -> Union[OperatorBase, complex]: """ Evaluate the Operator's underlying function, either on a binary string or another Operator. A square binary Operator can be defined as a function taking a binary function to another binary function. This method returns the value of that function for a given StateFn or binary string. For example, ``op.eval('0110').eval('1110')`` can be seen as querying the Operator's matrix representation by row 6 and column 14, and will return the complex value at those "indices." Similarly for a StateFn, ``op.eval('1011')`` will return the complex value at row 11 of the vector representation of the StateFn, as all StateFns are defined to be evaluated from Zero implicitly (i.e. it is as if ``.eval('0000')`` is already called implicitly to always "indexing" from column 0). ListOp's eval recursively evaluates each Operator in ``oplist``, and combines the results using the recombination function ``combo_fn``. Args: front: The bitstring, dict of bitstrings (with values being coefficients), or StateFn to evaluated by the Operator's underlying function. Returns: The output of the ``oplist`` Operators' evaluation function, combined with the ``combo_fn``. If either self or front contain proper ``ListOps`` (not ListOp subclasses), the result is an n-dimensional list of complex or StateFn results, resulting from the recursive evaluation by each OperatorBase in the ListOps. Raises: NotImplementedError: Raised if called for a subclass which is not distributive. TypeError: Operators with mixed hierarchies, such as a ListOp containing both PrimitiveOps and ListOps, are not supported. NotImplementedError: Attempting to call ListOp's eval from a non-distributive subclass. """ # pylint: disable=cyclic-import from ..state_fns.dict_state_fn import DictStateFn from ..state_fns.vector_state_fn import VectorStateFn from ..state_fns.sparse_vector_state_fn import SparseVectorStateFn # The below code only works for distributive ListOps, e.g. ListOp and SummedOp if not self.distributive: raise NotImplementedError( "ListOp's eval function is only defined for distributive ListOps." ) evals = [op.eval(front) for op in self.oplist] # Handle application of combo_fn for DictStateFn resp VectorStateFn operators if self._combo_fn is not None: # If not using default. if ( all(isinstance(op, DictStateFn) for op in evals) or all(isinstance(op, VectorStateFn) for op in evals) or all(isinstance(op, SparseVectorStateFn) for op in evals) ): if not all( op.is_measurement == evals[0].is_measurement for op in evals # type: ignore ): raise NotImplementedError( "Combo_fn not yet supported for mixed measurement " "and non-measurement StateFns" ) result = self.combo_fn(evals) # pylint: disable=not-callable if isinstance(result, list): multiplied = self.coeff * np.array(result) return multiplied.tolist() return self.coeff * result if all(isinstance(op, OperatorBase) for op in evals): return self.__class__(evals) # type: ignore elif any(isinstance(op, OperatorBase) for op in evals): raise TypeError("Cannot handle mixed scalar and Operator eval results.") else: result = self.combo_fn(evals) # pylint: disable=not-callable if isinstance(result, list): multiplied = self.coeff * np.array(result) return multiplied.tolist() return self.coeff * result
[documentos] def exp_i(self) -> OperatorBase: """Return an ``OperatorBase`` equivalent to an exponentiation of self * -i, e^(-i*op).""" # pylint: disable=unidiomatic-typecheck if type(self) == ListOp: return ListOp( [op.exp_i() for op in self.oplist], **self._state(abelian=False) # type: ignore ) # pylint: disable=cyclic-import from ..evolutions.evolved_op import EvolvedOp return EvolvedOp(self)
[documentos] def log_i(self, massive: bool = False) -> OperatorBase: """Return a ``MatrixOp`` equivalent to log(H)/-i for this operator H. This function is the effective inverse of exp_i, equivalent to finding the Hermitian Operator which produces self when exponentiated. For proper ListOps, applies ``log_i`` to all ops in oplist. """ if self.__class__.__name__ == ListOp.__name__: return ListOp( [op.log_i(massive=massive) for op in self.oplist], # type: ignore **self._state(abelian=False), ) return self.to_matrix_op(massive=massive).log_i(massive=massive)
def __str__(self) -> str: content_string = ",\n".join([str(op) for op in self.oplist]) main_string = "{}([\n{}\n])".format( self.__class__.__name__, self._indent(content_string, indentation=self.INDENTATION) ) if self.abelian: main_string = "Abelian" + main_string if self.coeff != 1.0: main_string = f"{self.coeff} * " + main_string return main_string def __repr__(self) -> str: return "{}({}, coeff={}, abelian={})".format( self.__class__.__name__, repr(self.oplist), self.coeff, self.abelian ) @property def parameters(self): params = set() for op in self.oplist: params.update(op.parameters) if isinstance(self.coeff, ParameterExpression): params.update(self.coeff.parameters) return params
[documentos] def assign_parameters(self, param_dict: dict) -> OperatorBase: 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 self.traverse(lambda x: x.assign_parameters(param_dict), coeff=param_value)
[documentos] def reduce(self) -> OperatorBase: reduced_ops = [op.reduce() for op in self.oplist] if self.__class__ == ListOp: return ListOp(reduced_ops, **self._state()) return self.__class__(reduced_ops, coeff=self.coeff, abelian=self.abelian)
[documentos] def to_matrix_op(self, massive: bool = False) -> "ListOp": """Returns an equivalent Operator composed of only NumPy-based primitives, such as ``MatrixOp`` and ``VectorStateFn``.""" if self.__class__ == ListOp: return cast( ListOp, ListOp( [op.to_matrix_op(massive=massive) for op in self.oplist], **self._state() ).reduce(), ) return cast( ListOp, self.__class__( [op.to_matrix_op(massive=massive) for op in self.oplist], coeff=self.coeff, abelian=self.abelian, ).reduce(), )
[documentos] def to_circuit_op(self) -> OperatorBase: """Returns an equivalent Operator composed of only QuantumCircuit-based primitives, such as ``CircuitOp`` and ``CircuitStateFn``.""" # pylint: disable=cyclic-import from ..state_fns.operator_state_fn import OperatorStateFn if self.__class__ == ListOp: return ListOp( [ op.to_circuit_op() if not isinstance(op, OperatorStateFn) else op for op in self.oplist ], **self._state(), ).reduce() return self.__class__( [ op.to_circuit_op() if not isinstance(op, OperatorStateFn) else op for op in self.oplist ], coeff=self.coeff, abelian=self.abelian, ).reduce()
[documentos] def to_pauli_op(self, massive: bool = False) -> "ListOp": """Returns an equivalent Operator composed of only Pauli-based primitives, such as ``PauliOp``.""" # pylint: disable=cyclic-import from ..state_fns.state_fn import StateFn if self.__class__ == ListOp: return ListOp( [ op.to_pauli_op(massive=massive) # type: ignore if not isinstance(op, StateFn) else op for op in self.oplist ], **self._state(), ).reduce() return self.__class__( [ op.to_pauli_op(massive=massive) # type: ignore if not isinstance(op, StateFn) else op for op in self.oplist ], coeff=self.coeff, abelian=self.abelian, ).reduce()
def _is_empty(self): return len(self.oplist) == 0 # Array operations: def __getitem__(self, offset: Union[int, slice]) -> OperatorBase: """Allows array-indexing style access to the Operators in ``oplist``. Args: offset: The index of ``oplist`` desired. Returns: The ``OperatorBase`` at index ``offset`` of ``oplist``, or another ListOp with the same properties as this one if offset is a slice. """ if isinstance(offset, int): return self.oplist[offset] if self.__class__ == ListOp: return ListOp(oplist=self._oplist[offset], **self._state()) return self.__class__(oplist=self._oplist[offset], coeff=self._coeff, abelian=self._abelian) def __iter__(self) -> Iterator: """Returns an iterator over the operators in ``oplist``. Returns: An iterator over the operators in ``oplist`` """ return iter(self.oplist) def __len__(self) -> int: """Length of ``oplist``. Returns: An int equal to the length of ``oplist``. """ return len(self.oplist)