Source code for qiskit.opflow.state_fns.state_fn

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# (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.
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"""StateFn Class"""

from typing import Callable, Dict, List, Optional, Set, Tuple, Union

import numpy as np

from qiskit import QuantumCircuit
from qiskit.circuit import Instruction, ParameterExpression
from qiskit.opflow.operator_base import OperatorBase
from qiskit.quantum_info import Statevector
from qiskit.result import Result
from qiskit.utils.deprecation import deprecate_func

[docs]class StateFn(OperatorBase): r""" Deprecated: A class for representing state functions and measurements. State functions are defined to be complex functions over a single binary string (as compared to an operator, which is defined as a function over two binary strings, or a function taking a binary function to another binary function). This function may be called by the eval() method. Measurements are defined to be functionals over StateFns, taking them to real values. Generally, this real value is interpreted to represent the probability of some classical state (binary string) being observed from a probabilistic or quantum system represented by a StateFn. This leads to the equivalent definition, which is that a measurement m is a function over binary strings producing StateFns, such that the probability of measuring a given binary string b from a system with StateFn f is equal to the inner product between f and m(b). NOTE: State functions here are not restricted to wave functions, as there is no requirement of normalization. """ def __init_subclass__(cls): cls.__new__ = lambda cls, *args, **kwargs: super().__new__(cls) @staticmethod # pylint: disable=unused-argument def __new__( cls, primitive: Union[ str, dict, Result, list, np.ndarray, Statevector, QuantumCircuit, Instruction, OperatorBase, ] = None, coeff: Union[complex, ParameterExpression] = 1.0, is_measurement: bool = False, ) -> "StateFn": """A factory method to produce the correct type of StateFn subclass based on the primitive passed in. Primitive, coeff, and is_measurement arguments are passed into subclass's init() as-is automatically by new(). Args: primitive: The primitive which defines the behavior of the underlying State function. coeff: A coefficient by which the state function is multiplied. is_measurement: Whether the StateFn is a measurement operator Returns: The appropriate StateFn subclass for ``primitive``. Raises: TypeError: Unsupported primitive type passed. """ # Prevents infinite recursion when subclasses are created if cls.__name__ != StateFn.__name__: return super().__new__(cls) # pylint: disable=cyclic-import if isinstance(primitive, (str, dict, Result)): from .dict_state_fn import DictStateFn return DictStateFn.__new__(DictStateFn) if isinstance(primitive, (list, np.ndarray, Statevector)): from .vector_state_fn import VectorStateFn return VectorStateFn.__new__(VectorStateFn) if isinstance(primitive, (QuantumCircuit, Instruction)): from .circuit_state_fn import CircuitStateFn return CircuitStateFn.__new__(CircuitStateFn) if isinstance(primitive, OperatorBase): from .operator_state_fn import OperatorStateFn return OperatorStateFn.__new__(OperatorStateFn) raise TypeError( "Unsupported primitive type {} passed into StateFn " "factory constructor".format(type(primitive)) ) # 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[ str, dict, Result, list, np.ndarray, Statevector, QuantumCircuit, Instruction, OperatorBase, ] = None, coeff: Union[complex, ParameterExpression] = 1.0, is_measurement: bool = False, ) -> None: """ Args: primitive: The primitive which defines the behavior of the underlying State function. coeff: A coefficient by which the state function is multiplied. is_measurement: Whether the StateFn is a measurement operator """ super().__init__() self._primitive = primitive self._is_measurement = is_measurement self._coeff = coeff @property def primitive(self): """The primitive which defines the behavior of the underlying State function.""" return self._primitive @property def coeff(self) -> Union[complex, ParameterExpression]: """A coefficient by which the state function is multiplied.""" return self._coeff @property def is_measurement(self) -> bool: """Whether the StateFn object is a measurement Operator.""" return self._is_measurement @property def settings(self) -> Dict: """Return settings.""" return { "primitive": self._primitive, "coeff": self._coeff, "is_measurement": self._is_measurement, }
[docs] def primitive_strings(self) -> Set[str]: raise NotImplementedError
@property def num_qubits(self) -> int: raise NotImplementedError
[docs] def add(self, other: OperatorBase) -> OperatorBase: raise NotImplementedError
[docs] def adjoint(self) -> OperatorBase: raise NotImplementedError
def _expand_dim(self, num_qubits: int) -> "StateFn": raise NotImplementedError
[docs] def permute(self, permutation: List[int]) -> OperatorBase: """Permute the qubits of the state function. Args: permutation: A list defining where each qubit should be permuted. The qubit at index j of the circuit should be permuted to position permutation[j]. Returns: A new StateFn containing the permuted primitive. """ raise NotImplementedError
[docs] def equals(self, other: OperatorBase) -> bool: if not isinstance(other, type(self)) or not self.coeff == other.coeff: return False return self.primitive == other.primitive
# Will return NotImplementedError if not supported
[docs] def mul(self, scalar: Union[complex, ParameterExpression]) -> OperatorBase: 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 hasattr(self, "from_operator"): return self.__class__( self.primitive, coeff=self.coeff * scalar, is_measurement=self.is_measurement, from_operator=self.from_operator, ) else: return self.__class__( self.primitive, coeff=self.coeff * scalar, is_measurement=self.is_measurement )
[docs] def tensor(self, other: OperatorBase) -> OperatorBase: r""" Return tensor product between self and other, overloaded by ``^``. Note: You must be conscious of Qiskit's big-endian bit printing convention. Meaning, Plus.tensor(Zero) produces a \|+⟩ on qubit 0 and a \|0⟩ on qubit 1, or \|+⟩⨂\|0⟩, but would produce a QuantumCircuit like \|0⟩-- \|+⟩-- Because Terra prints circuits and results with qubit 0 at the end of the string or circuit. Args: other: The ``OperatorBase`` to tensor product with self. Returns: An ``OperatorBase`` equivalent to the tensor product of self and other. """ raise NotImplementedError
[docs] def tensorpower(self, other: int) -> Union[OperatorBase, int]: if not isinstance(other, int) or other <= 0: raise TypeError("Tensorpower can only take positive int arguments") temp = StateFn( self.primitive, coeff=self.coeff, is_measurement=self.is_measurement ) # type: OperatorBase for _ in range(other - 1): temp = temp.tensor(self) return temp
def _expand_shorter_operator_and_permute( self, other: OperatorBase, permutation: Optional[List[int]] = None ) -> Tuple[OperatorBase, OperatorBase]: # pylint: disable=cyclic-import from ..operator_globals import Zero if self == StateFn({"0": 1}, is_measurement=True): # Zero is special - we'll expand it to the correct qubit number. return StateFn("0" * other.num_qubits, is_measurement=True), other elif other == Zero: # Zero is special - we'll expand it to the correct qubit number. return self, StateFn("0" * self.num_qubits) return super()._expand_shorter_operator_and_permute(other, permutation)
[docs] def to_matrix(self, massive: bool = False) -> np.ndarray: raise NotImplementedError
[docs] def to_density_matrix(self, massive: bool = False) -> np.ndarray: """Return matrix representing product of StateFn evaluated on pairs of basis states. Overridden by child classes. Args: massive: Whether to allow large conversions, e.g. creating a matrix representing over 16 qubits. Returns: The NumPy array representing the density matrix of the State function. Raises: ValueError: If massive is set to False, and exponentially large computation is needed. """ raise NotImplementedError
[docs] def compose( self, other: OperatorBase, permutation: Optional[List[int]] = None, front: bool = False ) -> OperatorBase: r""" Composition (Linear algebra-style: A@B(x) = A(B(x))) is not well defined for states in the binary function model, but is well defined for measurements. Args: other: The Operator to compose with self. permutation: ``List[int]`` which defines permutation on other operator. front: If front==True, return ``other.compose(self)``. Returns: An Operator equivalent to the function composition of self and other. Raises: ValueError: If self is not a measurement, it cannot be composed from the right. """ # TODO maybe allow outers later to produce density operators or projectors, but not yet. if not self.is_measurement and not front: raise ValueError( "Composition with a Statefunction in the first operand is not defined." ) new_self, other = self._expand_shorter_operator_and_permute(other, permutation) if front: return other.compose(self) # TODO maybe include some reduction here in the subclasses - vector and Op, op and Op, etc. from ..primitive_ops.circuit_op import CircuitOp if self.primitive == {"0" * self.num_qubits: 1.0} and isinstance(other, CircuitOp): # Returning CircuitStateFn return StateFn( other.primitive, is_measurement=self.is_measurement, coeff=self.coeff * other.coeff ) from ..list_ops.composed_op import ComposedOp if isinstance(other, ComposedOp): return ComposedOp([new_self] + other.oplist, coeff=new_self.coeff * other.coeff) return ComposedOp([new_self, other])
[docs] def power(self, exponent: int) -> OperatorBase: """Compose with Self Multiple Times, undefined for StateFns. Args: exponent: The number of times to compose self with self. Raises: ValueError: This function is not defined for StateFns. """ raise ValueError("Composition power over Statefunctions or Measurements is not defined.")
def __str__(self) -> str: prim_str = str(self.primitive) if self.coeff == 1.0: return "{}({})".format( "StateFunction" if not self.is_measurement else "Measurement", self.coeff ) else: return "{}({}) * {}".format( "StateFunction" if not self.is_measurement else "Measurement", self.coeff, prim_str ) def __repr__(self) -> str: return "{}({}, coeff={}, is_measurement={})".format( self.__class__.__name__, repr(self.primitive), self.coeff, self.is_measurement )
[docs] def eval( self, front: Optional[ Union[str, Dict[str, complex], np.ndarray, OperatorBase, Statevector] ] = None, ) -> Union[OperatorBase, complex]: raise NotImplementedError
@property def parameters(self): params = set() if isinstance(self.primitive, (OperatorBase, QuantumCircuit)): params.update(self.primitive.parameters) if isinstance(self.coeff, ParameterExpression): params.update(self.coeff.parameters) return params
[docs] 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): from ..list_ops.list_op import ListOp 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)
# Try collapsing primitives where possible. Nothing to collapse here.
[docs] def reduce(self) -> OperatorBase: return self
[docs] def traverse( self, convert_fn: Callable, coeff: Optional[Union[complex, ParameterExpression]] = None ) -> OperatorBase: r""" Apply the convert_fn to the internal primitive if the primitive is an Operator (as in the case of ``OperatorStateFn``). Otherwise do nothing. Used by converters. 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 StateFn. """ if coeff is None: coeff = self.coeff if isinstance(self.primitive, OperatorBase): return StateFn( convert_fn(self.primitive), coeff=coeff, is_measurement=self.is_measurement ) else: return self
[docs] def to_matrix_op(self, massive: bool = False) -> OperatorBase: """Return a ``VectorStateFn`` for this ``StateFn``. Args: massive: Whether to allow large conversions, e.g. creating a matrix representing over 16 qubits. Returns: A VectorStateFn equivalent to self. """ # pylint: disable=cyclic-import from .vector_state_fn import VectorStateFn return VectorStateFn(self.to_matrix(massive=massive), is_measurement=self.is_measurement)
[docs] def to_circuit_op(self) -> OperatorBase: """Returns a ``CircuitOp`` equivalent to this Operator.""" raise NotImplementedError
# TODO to_dict_op
[docs] def sample( self, shots: int = 1024, massive: bool = False, reverse_endianness: bool = False ) -> Dict[str, float]: """Sample the state function as a normalized probability distribution. Returns dict of bitstrings in order of probability, with values being probability. Args: shots: The number of samples to take to approximate the State function. massive: Whether to allow large conversions, e.g. creating a matrix representing over 16 qubits. reverse_endianness: Whether to reverse the endianness of the bitstrings in the return dict to match Terra's big-endianness. Returns: A dict containing pairs sampled strings from the State function and sampling frequency divided by shots. """ raise NotImplementedError