Source code for pennylane.operation

# Copyright 2018-2021 Xanadu Quantum Technologies Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at

#     http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# pylint: disable=protected-access
r"""
This module contains the abstract base classes for defining PennyLane
operations and observables.

Description
-----------

Qubit Operations
~~~~~~~~~~~~~~~~
The :class:`Operator` class serves as a base class for operators,
and is inherited by both the :class:`Observable` class and the
:class:`Operation` class. These classes are subclassed to implement quantum operations
and measure observables in PennyLane.

* Each :class:`~.Operator` subclass represents a general type of
  map between physical states. Each instance of these subclasses
  represents either

  - an application of the operator or
  - an instruction to measure and return the respective result.

  Operators act on a sequence of wires (subsystems) using given parameter values.

* Each :class:`~.Operation` subclass represents a type of quantum operation,
  for example a unitary quantum gate. Each instance of these subclasses
  represents an application of the operation with given parameter values to
  a given sequence of wires (subsystems).

* Each  :class:`~.Observable` subclass represents a type of physical observable.
  Each instance of these subclasses represents an instruction to measure and
  return the respective result for the given parameter values on a
  sequence of wires (subsystems).

Differentiation
^^^^^^^^^^^^^^^

In general, an :class:`Operation` is differentiable (at least using the finite-difference
method) with respect to a parameter iff

* the domain of that parameter is continuous.

For an :class:`Operation` to be differentiable with respect to a parameter using the
analytic method of differentiation, it must satisfy an additional constraint:

* the parameter domain must be real.

.. note::

    These conditions are *not* sufficient for analytic differentiation. For example,
    CV gates must also define a matrix representing their Heisenberg linear
    transformation on the quadrature operators.

CV Operation base classes
~~~~~~~~~~~~~~~~~~~~~~~~~

Due to additional requirements, continuous-variable (CV) operations must subclass the
:class:`~.CVOperation` or :class:`~.CVObservable` classes instead of :class:`~.Operation`
and :class:`~.Observable`.

Differentiation
^^^^^^^^^^^^^^^

To enable gradient computation using the analytic method for Gaussian CV operations, in addition, you need to
provide the static class method :meth:`~.CV._heisenberg_rep` that returns the Heisenberg representation of
the operation given its list of parameters, namely:

* For Gaussian CV Operations this method should return the matrix of the linear transformation carried out by the
  operation on the vector of quadrature operators :math:`\mathbf{r}` for the given parameter
  values.

* For Gaussian CV Observables this method should return a real vector (first-order observables)
  or symmetric matrix (second-order observables) of coefficients of the quadrature
  operators :math:`\x` and :math:`\p`.

PennyLane uses the convention :math:`\mathbf{r} = (\I, \x, \p)` for single-mode operations and observables
and :math:`\mathbf{r} = (\I, \x_0, \p_0, \x_1, \p_1, \ldots)` for multi-mode operations and observables.

.. note::
    Non-Gaussian CV operations and observables are currently only supported via
    the finite-difference method of gradient computation.
"""
# pylint:disable=access-member-before-definition
import abc
import copy
import functools
import itertools
import numbers
import warnings
from enum import IntEnum
from typing import List

import numpy as np
from numpy.linalg import multi_dot
from scipy.sparse import coo_matrix, eye, kron

import pennylane as qml
from pennylane.math import expand_matrix
from pennylane.queuing import QueuingManager
from pennylane.wires import Wires

from .utils import pauli_eigs

# =============================================================================
# Errors
# =============================================================================


[docs]class OperatorPropertyUndefined(Exception): """Generic exception to be used for undefined Operator properties or methods."""
[docs]class DecompositionUndefinedError(OperatorPropertyUndefined): """Raised when an Operator's representation as a decomposition is undefined."""
[docs]class TermsUndefinedError(OperatorPropertyUndefined): """Raised when an Operator's representation as a linear combination is undefined."""
[docs]class MatrixUndefinedError(OperatorPropertyUndefined): """Raised when an Operator's matrix representation is undefined."""
[docs]class SparseMatrixUndefinedError(OperatorPropertyUndefined): """Raised when an Operator's sparse matrix representation is undefined."""
[docs]class EigvalsUndefinedError(OperatorPropertyUndefined): """Raised when an Operator's eigenvalues are undefined."""
[docs]class DiagGatesUndefinedError(OperatorPropertyUndefined): """Raised when an Operator's diagonalizing gates are undefined."""
[docs]class AdjointUndefinedError(OperatorPropertyUndefined): """Raised when an Operator's adjoint version is undefined."""
[docs]class PowUndefinedError(OperatorPropertyUndefined): """Raised when an Operator's power is undefined."""
[docs]class GeneratorUndefinedError(OperatorPropertyUndefined): """Exception used to indicate that an operator does not have a generator"""
[docs]class ParameterFrequenciesUndefinedError(OperatorPropertyUndefined): """Exception used to indicate that an operator does not have parameter_frequencies"""
# ============================================================================= # Wire types # =============================================================================
[docs]class WiresEnum(IntEnum): """Integer enumeration class to represent the number of wires an operation acts on""" AnyWires = -1 AllWires = 0
AllWires = WiresEnum.AllWires """IntEnum: An enumeration which represents all wires in the subsystem. It is equivalent to an integer with value 0.""" AnyWires = WiresEnum.AnyWires """IntEnum: An enumeration which represents any wires in the subsystem. It is equivalent to an integer with value -1.""" # ============================================================================= # Class property # ============================================================================= class ClassPropertyDescriptor: # pragma: no cover """Allows a class property to be defined""" # pylint: disable=too-few-public-methods,too-many-public-methods def __init__(self, fget, fset=None): self.fget = fget self.fset = fset def __get__(self, obj, klass=None): if klass is None: klass = type(obj) return self.fget.__get__(obj, klass)() def __set__(self, obj, value): if not self.fset: raise AttributeError("can't set attribute") type_ = type(obj) return self.fset.__get__(obj, type_)(value) def setter(self, func): """Set the function as a class method, and store as an attribute.""" if not isinstance(func, (classmethod, staticmethod)): func = classmethod(func) self.fset = func return self
[docs]def classproperty(func): """The class property decorator""" if not isinstance(func, (classmethod, staticmethod)): func = classmethod(func) return ClassPropertyDescriptor(func)
# ============================================================================= # Base Operator class # ============================================================================= def _process_data(op): # Use qml.math.real to take the real part. We may get complex inputs for # example when differentiating holomorphic functions with JAX: a complex # valued QNode (one that returns qml.state) requires complex typed inputs. if op.name in ("RX", "RY", "RZ", "PhaseShift", "Rot"): return str([qml.math.round(qml.math.real(d) % (2 * np.pi), 10) for d in op.data]) if op.name in ("CRX", "CRY", "CRZ", "CRot"): return str([qml.math.round(qml.math.real(d) % (4 * np.pi), 10) for d in op.data]) return str(op.data)
[docs]class Operator(abc.ABC): r"""Base class representing quantum operators. Operators are uniquely defined by their name, the wires they act on, their (trainable) parameters, and their (non-trainable) hyperparameters. The trainable parameters can be tensors of any supported auto-differentiation framework. An operator can define any of the following representations: * Representation as a **matrix** (:meth:`.Operator.matrix`), as specified by a global wire order that tells us where the wires are found on a register. * Representation as a **sparse matrix** (:meth:`.Operator.sparse_matrix`). Currently, this is a SciPy CSR matrix format. * Representation via the **eigenvalue decomposition** specified by eigenvalues (:meth:`.Operator.eigvals`) and diagonalizing gates (:meth:`.Operator.diagonalizing_gates`). * Representation as a **product of operators** (:meth:`.Operator.decomposition`). * Representation as a **linear combination of operators** (:meth:`.Operator.terms`). * Representation by a **generator** via :math:`e^{G}` (:meth:`.Operator.generator`). Each representation method comes with a static method prefixed by ``compute_``, which takes the signature ``(*parameters, **hyperparameters)`` (for numerical representations that do not need to know about wire labels) or ``(*parameters, wires, **hyperparameters)``, where ``parameters``, ``wires``, and ``hyperparameters`` are the respective attributes of the operator class. Args: params (tuple[tensor_like]): trainable parameters wires (Iterable[Any] or Any): Wire label(s) that the operator acts on. If not given, args[-1] is interpreted as wires. do_queue (bool): indicates whether the operator should be recorded when created in a tape context id (str): custom label given to an operator instance, can be useful for some applications where the instance has to be identified **Example** A custom operator can be created by inheriting from :class:`~.Operator` or one of its subclasses. The following is an example for a custom gate that inherits from the :class:`~.Operation` subclass. It acts by potentially flipping a qubit and rotating another qubit. The custom operator defines a decomposition, which the devices can use (since it is unlikely that a device knows a native implementation for ``FlipAndRotate``). It also defines an adjoint operator. .. code-block:: python import pennylane as qml class FlipAndRotate(qml.operation.Operation): # Define how many wires the operator acts on in total. # In our case this may be one or two, which is why we # use the AnyWires Enumeration to indicate a variable number. num_wires = qml.operation.AnyWires # This attribute tells PennyLane what differentiation method to use. Here # we request parameter-shift (or "analytic") differentiation. grad_method = "A" def __init__(self, angle, wire_rot, wire_flip=None, do_flip=False, do_queue=True, id=None): # checking the inputs -------------- if do_flip and wire_flip is None: raise ValueError("Expected a wire to flip; got None.") #------------------------------------ # do_flip is not trainable but influences the action of the operator, # which is why we define it to be a hyperparameter self._hyperparameters = { "do_flip": do_flip } # we extract all wires that the operator acts on, # relying on the Wire class arithmetic all_wires = qml.wires.Wires(wire_rot) + qml.wires.Wires(wire_flip) # The parent class expects all trainable parameters to be fed as positional # arguments, and all wires acted on fed as a keyword argument. # The id keyword argument allows users to give their instance a custom name. # The do_queue keyword argument specifies whether or not # the operator is queued when created in a tape context. super().__init__(angle, wires=all_wires, do_queue=do_queue, id=id) @property def num_params(self): # if it is known before creation, define the number of parameters to expect here, # which makes sure an error is raised if the wrong number was passed. The angle # parameter is the only trainable parameter of the operation return 1 @property def ndim_params(self): # if it is known before creation, define the number of dimensions each parameter # is expected to have. This makes sure to raise an error if a wrongly-shaped # parameter was passed. The angle parameter is expected to be a scalar return (0,) @staticmethod def compute_decomposition(angle, wires, do_flip): # pylint: disable=arguments-differ # Overwriting this method defines the decomposition of the new gate, as it is # called by Operator.decomposition(). # The general signature of this function is (*parameters, wires, **hyperparameters). op_list = [] if do_flip: op_list.append(qml.PauliX(wires=wires[1])) op_list.append(qml.RX(angle, wires=wires[0])) return op_list def adjoint(self): # the adjoint operator of this gate simply negates the angle return FlipAndRotate(-self.parameters[0], self.wires[0], self.wires[1], do_flip=self.hyperparameters["do_flip"]) We can use the operation as follows: .. code-block:: python from pennylane import numpy as np dev = qml.device("default.qubit", wires=["q1", "q2", "q3"]) @qml.qnode(dev) def circuit(angle): FlipAndRotate(angle, wire_rot="q1", wire_flip="q1") return qml.expval(qml.PauliZ("q1")) >>> a = np.array(3.14) >>> circuit(a) -0.9999987318946099 """ # pylint: disable=too-many-public-methods, too-many-instance-attributes def __copy__(self): cls = self.__class__ copied_op = cls.__new__(cls) copied_op.data = self.data.copy() for attr, value in vars(self).items(): if attr != "data": setattr(copied_op, attr, value) return copied_op def __deepcopy__(self, memo): copied_op = object.__new__(type(self)) # The memo dict maps object ID to object, and is required by # the deepcopy function to keep track of objects it has already # deep copied. memo[id(self)] = copied_op for attribute, value in self.__dict__.items(): if attribute == "data": # Shallow copy the list of parameters. We avoid a deep copy # here, since PyTorch does not support deep copying of tensors # within a differentiable computation. copied_op.data = value.copy() else: # Deep copy everything else. setattr(copied_op, attribute, copy.deepcopy(value, memo)) return copied_op @property def hash(self): """int: Integer hash that uniquely represents the operator.""" return hash( ( str(self.name), tuple(self.wires.tolist()), str(self.hyperparameters.values()), _process_data(self), ) )
[docs] @staticmethod def compute_matrix(*params, **hyperparams): # pylint:disable=unused-argument r"""Representation of the operator as a canonical matrix in the computational basis (static method). The canonical matrix is the textbook matrix representation that does not consider wires. Implicitly, this assumes that the wires of the operator correspond to the global wire order. .. seealso:: :meth:`~.Operator.matrix` and :func:`~.matrix` Args: params (list): trainable parameters of the operator, as stored in the ``parameters`` attribute hyperparams (dict): non-trainable hyperparameters of the operator, as stored in the ``hyperparameters`` attribute Returns: tensor_like: matrix representation """ raise MatrixUndefinedError
# pylint: disable=no-self-argument, comparison-with-callable @classproperty def has_matrix(cls): r"""Bool: Whether or not the Operator returns a defined matrix. Note: Child classes may have this as an instance property instead of as a class property. """ return cls.compute_matrix != Operator.compute_matrix
[docs] def matrix(self, wire_order=None): r"""Representation of the operator as a matrix in the computational basis. If ``wire_order`` is provided, the numerical representation considers the position of the operator's wires in the global wire order. Otherwise, the wire order defaults to the operator's wires. If the matrix depends on trainable parameters, the result will be cast in the same autodifferentiation framework as the parameters. A ``MatrixUndefinedError`` is raised if the matrix representation has not been defined. .. seealso:: :meth:`~.Operator.compute_matrix` Args: wire_order (Iterable): global wire order, must contain all wire labels from the operator's wires Returns: tensor_like: matrix representation """ canonical_matrix = self.compute_matrix(*self.parameters, **self.hyperparameters) if wire_order is None or self.wires == Wires(wire_order): return canonical_matrix return expand_matrix(canonical_matrix, wires=self.wires, wire_order=wire_order)
[docs] @staticmethod def compute_sparse_matrix(*params, **hyperparams): # pylint:disable=unused-argument r"""Representation of the operator as a sparse matrix in the computational basis (static method). The canonical matrix is the textbook matrix representation that does not consider wires. Implicitly, this assumes that the wires of the operator correspond to the global wire order. .. seealso:: :meth:`~.Operator.sparse_matrix` Args: params (list): trainable parameters of the operator, as stored in the ``parameters`` attribute hyperparams (dict): non-trainable hyperparameters of the operator, as stored in the ``hyperparameters`` attribute Returns: scipy.sparse._csr.csr_matrix: sparse matrix representation """ raise SparseMatrixUndefinedError
[docs] def sparse_matrix(self, wire_order=None): r"""Representation of the operator as a sparse matrix in the computational basis. If ``wire_order`` is provided, the numerical representation considers the position of the operator's wires in the global wire order. Otherwise, the wire order defaults to the operator's wires. A ``SparseMatrixUndefinedError`` is raised if the sparse matrix representation has not been defined. .. seealso:: :meth:`~.Operator.compute_sparse_matrix` Args: wire_order (Iterable): global wire order, must contain all wire labels from the operator's wires Returns: scipy.sparse._csr.csr_matrix: sparse matrix representation """ canonical_sparse_matrix = self.compute_sparse_matrix( *self.parameters, **self.hyperparameters ) return expand_matrix(canonical_sparse_matrix, wires=self.wires, wire_order=wire_order)
[docs] @staticmethod def compute_eigvals(*params, **hyperparams): r"""Eigenvalues of the operator in the computational basis (static method). If :attr:`diagonalizing_gates` are specified and implement a unitary :math:`U`, the operator can be reconstructed as .. math:: O = U \Sigma U^{\dagger}, where :math:`\Sigma` is the diagonal matrix containing the eigenvalues. Otherwise, no particular order for the eigenvalues is guaranteed. .. seealso:: :meth:`~.Operator.eigvals` and :func:`~.eigvals` Args: params (list): trainable parameters of the operator, as stored in the ``parameters`` attribute hyperparams (dict): non-trainable hyperparameters of the operator, as stored in the ``hyperparameters`` attribute Returns: tensor_like: eigenvalues """ raise EigvalsUndefinedError
[docs] def eigvals(self): r"""Eigenvalues of the operator in the computational basis. If :attr:`diagonalizing_gates` are specified and implement a unitary :math:`U`, the operator can be reconstructed as .. math:: O = U \Sigma U^{\dagger}, where :math:`\Sigma` is the diagonal matrix containing the eigenvalues. Otherwise, no particular order for the eigenvalues is guaranteed. .. note:: When eigenvalues are not explicitly defined, they are computed automatically from the matrix representation. Currently, this computation is *not* differentiable. A ``EigvalsUndefinedError`` is raised if the eigenvalues have not been defined and cannot be inferred from the matrix representation. .. seealso:: :meth:`~.Operator.compute_eigvals` Returns: tensor_like: eigenvalues """ try: return self.compute_eigvals(*self.parameters, **self.hyperparameters) except EigvalsUndefinedError as e: # By default, compute the eigenvalues from the matrix representation if one is defined. if self.has_matrix: # pylint: disable=using-constant-test return qml.math.linalg.eigvals(self.matrix()) raise EigvalsUndefinedError from e
[docs] def terms(self): # pylint: disable=no-self-use r"""Representation of the operator as a linear combination of other operators. .. math:: O = \sum_i c_i O_i A ``TermsUndefinedError`` is raised if no representation by terms is defined. Returns: tuple[list[tensor_like or float], list[.Operation]]: list of coefficients :math:`c_i` and list of operations :math:`O_i` """ raise TermsUndefinedError
@property @abc.abstractmethod def num_wires(self): """Number of wires the operator acts on.""" @property def name(self): """String for the name of the operator.""" return self._name @property def id(self): """Custom string to label a specific operator instance.""" return self._id @name.setter def name(self, value): self._name = value
[docs] def label(self, decimals=None, base_label=None, cache=None): r"""A customizable string representation of the operator. Args: decimals=None (int): If ``None``, no parameters are included. Else, specifies how to round the parameters. base_label=None (str): overwrite the non-parameter component of the label cache=None (dict): dictionary that carries information between label calls in the same drawing Returns: str: label to use in drawings **Example:** >>> op = qml.RX(1.23456, wires=0) >>> op.label() "RX" >>> op.label(decimals=2) "RX\n(1.23)" >>> op.label(base_label="my_label") "my_label" >>> op.label(decimals=2, base_label="my_label") "my_label\n(1.23)" >>> op.inv() >>> op.label() "RX⁻¹" If the operation has a matrix-valued parameter and a cache dictionary is provided, unique matrices will be cached in the ``'matrices'`` key list. The label will contain the index of the matrix in the ``'matrices'`` list. >>> op2 = qml.QubitUnitary(np.eye(2), wires=0) >>> cache = {'matrices': []} >>> op2.label(cache=cache) 'U(M0)' >>> cache['matrices'] [tensor([[1., 0.], [0., 1.]], requires_grad=True)] >>> op3 = qml.QubitUnitary(np.eye(4), wires=(0,1)) >>> op3.label(cache=cache) 'U(M1)' >>> cache['matrices'] [tensor([[1., 0.], [0., 1.]], requires_grad=True), tensor([[1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]], requires_grad=True)] """ op_label = base_label or self.__class__.__name__ if self.num_params == 0: return op_label params = self.parameters if len(qml.math.shape(params[0])) != 0: # assume that if the first parameter is matrix-valued, there is only a single parameter # this holds true for all current operations and templates unless parameter broadcasting # is used # TODO[dwierichs]: Implement a proper label for broadcasted operators if ( cache is None or not isinstance(cache.get("matrices", None), list) or len(params) != 1 ): return op_label for i, mat in enumerate(cache["matrices"]): if qml.math.shape(params[0]) == qml.math.shape(mat) and qml.math.allclose( params[0], mat ): return f"{op_label}(M{i})" # matrix not in cache mat_num = len(cache["matrices"]) cache["matrices"].append(params[0]) return f"{op_label}(M{mat_num})" if decimals is None: return op_label def _format(x): try: return format(qml.math.toarray(x), f".{decimals}f") except ValueError: # If the parameter can't be displayed as a float return format(x) param_string = ",\n".join(_format(p) for p in params) return op_label + f"\n({param_string})"
def __init__(self, *params, wires=None, do_queue=True, id=None): # pylint: disable=too-many-branches self._name = self.__class__.__name__ #: str: name of the operator self._id = id self.queue_idx = None #: int, None: index of the Operator in the circuit queue, or None if not in a queue wires_from_args = False if wires is None: try: wires = params[-1] params = params[:-1] wires_from_args = True except IndexError as err: raise ValueError( f"Must specify the wires that {type(self).__name__} acts on" ) from err self._num_params = len(params) # Check if the expected number of parameters coincides with the one received. # This is always true for the default `Operator.num_params` property, but # subclasses may overwrite it to define a fixed expected value. if len(params) != self.num_params: if wires_from_args and len(params) == (self.num_params - 1): raise ValueError(f"Must specify the wires that {type(self).__name__} acts on") raise ValueError( f"{self.name}: wrong number of parameters. " f"{len(params)} parameters passed, {self.num_params} expected." ) self._wires = wires if isinstance(wires, Wires) else Wires(wires) # check that the number of wires given corresponds to required number if self.num_wires in {AllWires, AnyWires}: if ( not isinstance(self, (qml.Barrier, qml.Snapshot, qml.Hamiltonian)) and len(qml.wires.Wires(wires)) == 0 ): raise ValueError( f"{self.name}: wrong number of wires. " f"At least one wire has to be given." ) elif len(self._wires) != self.num_wires: raise ValueError( f"{self.name}: wrong number of wires. " f"{len(self._wires)} wires given, {self.num_wires} expected." ) self._check_batching(params) self.data = list(params) #: list[Any]: parameters of the operator if do_queue: self.queue() def _check_batching(self, params): """Check if the expected numbers of dimensions of parameters coincides with the ones received and sets the ``_batch_size`` attribute. Args: params (tuple): Parameters with which the operator is instantiated The check always passes and sets the ``_batch_size`` to ``None`` for the default ``Operator.ndim_params`` property but subclasses may overwrite it to define fixed expected numbers of dimensions, allowing to infer a batch size. """ self._batch_size = None try: ndims = tuple(qml.math.ndim(p) for p in params) except ValueError as e: # TODO:[dwierichs] When using tf.function with an input_signature that contains # an unknown-shaped input, ndim() will not be able to determine the number of # dimensions because they are not specified yet. Failing example: Let `fun` be # a single-parameter QNode. # `tf.function(fun, input_signature=(tf.TensorSpec(shape=None, dtype=tf.float32),))` # There might be a way to support batching nonetheless, which remains to be # investigated. For now, the batch_size is left to be `None` when instantiating # an operation with abstract parameters that make `qml.math.ndim` fail. if any(qml.math.is_abstract(p) for p in params): return raise e self._ndim_params = ndims if ndims != self.ndim_params: ndims_matches = [ (ndim == exp_ndim, ndim == exp_ndim + 1) for ndim, exp_ndim in zip(ndims, self.ndim_params) ] if not all(correct or batched for correct, batched in ndims_matches): raise ValueError( f"{self.name}: wrong number(s) of dimensions in parameters. " f"Parameters with ndims {ndims} passed, {self.ndim_params} expected." ) first_dims = [ qml.math.shape(p)[0] for (_, batched), p in zip(ndims_matches, params) if batched ] if not qml.math.allclose(first_dims, first_dims[0]): raise ValueError( "Broadcasting was attempted but the broadcasted dimensions " f"do not match: {first_dims}." ) self._batch_size = first_dims[0] def __repr__(self): """Constructor-call-like representation.""" if self.parameters: params = ", ".join([repr(p) for p in self.parameters]) return f"{self.name}({params}, wires={self.wires.tolist()})" return f"{self.name}(wires={self.wires.tolist()})" @property def num_params(self): """Number of trainable parameters that the operator depends on. By default, this property returns as many parameters as were used for the operator creation. If the number of parameters for an operator subclass is fixed, this property can be overwritten to return the fixed value. Returns: int: number of parameters """ return self._num_params @property def ndim_params(self): """Number of dimensions per trainable parameter of the operator. By default, this property returns the numbers of dimensions of the parameters used for the operator creation. If the parameter sizes for an operator subclass are fixed, this property can be overwritten to return the fixed value. Returns: tuple: Number of dimensions for each trainable parameter. """ return self._ndim_params @property def batch_size(self): r"""Batch size of the operator if it is used with broadcasted parameters. The ``batch_size`` is determined based on ``ndim_params`` and the provided parameters for the operator. If (some of) the latter have an additional dimension, and this dimension has the same size for all parameters, its size is the batch size of the operator. If no parameter has an additional dimension, the batch size is ``None``. Returns: int or None: Size of the parameter broadcasting dimension if present, else ``None``. """ return self._batch_size @property def wires(self): """Wires that the operator acts on. Returns: Wires: wires """ return self._wires @property def parameters(self): """Trainable parameters that the operator depends on.""" return self.data.copy() @property def hyperparameters(self): """dict: Dictionary of non-trainable variables that this operation depends on.""" # pylint: disable=attribute-defined-outside-init if hasattr(self, "_hyperparameters"): return self._hyperparameters self._hyperparameters = {} return self._hyperparameters @property def is_hermitian(self): """This property determines if an operator is hermitian.""" return False # pylint: disable=no-self-argument, comparison-with-callable @classproperty def has_decomposition(cls): r"""Bool: Whether or not the Operator returns a defined decomposition. Note: Child classes may have this as an instance property instead of as a class property. """ # Some operators will overwrite `decomposition` instead of `compute_decomposition` # Currently, those are mostly classes from the operator arithmetic module. return ( cls.compute_decomposition != Operator.compute_decomposition or cls.decomposition != Operator.decomposition )
[docs] def decomposition(self): r"""Representation of the operator as a product of other operators. .. math:: O = O_1 O_2 \dots O_n A ``DecompositionUndefinedError`` is raised if no representation by decomposition is defined. .. seealso:: :meth:`~.Operator.compute_decomposition`. Returns: list[Operator]: decomposition of the operator """ return self.compute_decomposition( *self.parameters, wires=self.wires, **self.hyperparameters )
[docs] @staticmethod def compute_decomposition(*params, wires=None, **hyperparameters): r"""Representation of the operator as a product of other operators (static method). .. math:: O = O_1 O_2 \dots O_n. .. note:: Operations making up the decomposition should be queued within the ``compute_decomposition`` method. .. seealso:: :meth:`~.Operator.decomposition`. Args: params (list): trainable parameters of the operator, as stored in the ``parameters`` attribute wires (Iterable[Any], Wires): wires that the operator acts on hyperparams (dict): non-trainable hyperparameters of the operator, as stored in the ``hyperparameters`` attribute Returns: list[Operator]: decomposition of the operator """ raise DecompositionUndefinedError
# pylint: disable=no-self-argument, comparison-with-callable @classproperty def has_diagonalizing_gates(cls): r"""Bool: Whether or not the Operator returns defined diagonalizing gates. Note: Child classes may have this as an instance property instead of as a class property. """ # Operators may overwrite `diagonalizing_gates` instead of `compute_diagonalizing_gates` # Currently, those are mostly classes from the operator arithmetic module. return ( cls.compute_diagonalizing_gates != Operator.compute_diagonalizing_gates or cls.diagonalizing_gates != Operator.diagonalizing_gates )
[docs] @staticmethod def compute_diagonalizing_gates( *params, wires, **hyperparams ): # pylint: disable=unused-argument r"""Sequence of gates that diagonalize the operator in the computational basis (static method). Given the eigendecomposition :math:`O = U \Sigma U^{\dagger}` where :math:`\Sigma` is a diagonal matrix containing the eigenvalues, the sequence of diagonalizing gates implements the unitary :math:`U^{\dagger}`. The diagonalizing gates rotate the state into the eigenbasis of the operator. .. seealso:: :meth:`~.Operator.diagonalizing_gates`. Args: params (list): trainable parameters of the operator, as stored in the ``parameters`` attribute wires (Iterable[Any], Wires): wires that the operator acts on hyperparams (dict): non-trainable hyperparameters of the operator, as stored in the ``hyperparameters`` attribute Returns: list[.Operator]: list of diagonalizing gates """ raise DiagGatesUndefinedError
[docs] def diagonalizing_gates(self): # pylint:disable=no-self-use r"""Sequence of gates that diagonalize the operator in the computational basis. Given the eigendecomposition :math:`O = U \Sigma U^{\dagger}` where :math:`\Sigma` is a diagonal matrix containing the eigenvalues, the sequence of diagonalizing gates implements the unitary :math:`U^{\dagger}`. The diagonalizing gates rotate the state into the eigenbasis of the operator. A ``DiagGatesUndefinedError`` is raised if no representation by decomposition is defined. .. seealso:: :meth:`~.Operator.compute_diagonalizing_gates`. Returns: list[.Operator] or None: a list of operators """ return self.compute_diagonalizing_gates( *self.parameters, wires=self.wires, **self.hyperparameters )
[docs] def generator(self): # pylint: disable=no-self-use r"""Generator of an operator that is in single-parameter-form. For example, for operator .. math:: U(\phi) = e^{i\phi (0.5 Y + Z\otimes X)} we get the generator >>> U.generator() (0.5) [Y0] + (1.0) [Z0 X1] The generator may also be provided in the form of a dense or sparse Hamiltonian (using :class:`.Hermitian` and :class:`.SparseHamiltonian` respectively). The default value to return is ``None``, indicating that the operation has no defined generator. """ raise GeneratorUndefinedError(f"Operation {self.name} does not have a generator")
[docs] def pow(self, z) -> List["Operator"]: """A list of new operators equal to this one raised to the given power. Args: z (float): exponent for the operator Returns: list[:class:`~.operation.Operator`] """ # Child methods may call super().pow(z%period) where op**period = I # For example, PauliX**2 = I, SX**4 = I # Hence we define 0 and 1 special cases here. if z == 0: return [] if z == 1: return [copy.copy(self)] raise PowUndefinedError
[docs] def queue(self, context=QueuingManager): """Append the operator to the Operator queue.""" context.append(self) return self # so pre-constructed Observable instances can be queued and returned in a single statement
@property def _queue_category(self): """Used for sorting objects into their respective lists in `QuantumTape` objects. This property is a temporary solution that should not exist long-term and should not be used outside of ``QuantumTape._process_queue``. Options are: * `"_prep"` * `"_ops"` * `"_measurements"` * `None` """ return "_ops" # pylint: disable=no-self-argument, comparison-with-callable @classproperty def has_adjoint(cls): r"""Bool: Whether or not the Operator can compute its own adjoint. Note: Child classes may have this as an instance property instead of as a class property. """ return cls.adjoint != Operator.adjoint
[docs] def adjoint(self) -> "Operator": # pylint:disable=no-self-use """Create an operation that is the adjoint of this one. Adjointed operations are the conjugated and transposed version of the original operation. Adjointed ops are equivalent to the inverted operation for unitary gates. Returns: The adjointed operation. """ raise AdjointUndefinedError
[docs] def expand(self): """Returns a tape that has recorded the decomposition of the operator. Returns: .QuantumTape: quantum tape """ if not self.has_decomposition: raise DecompositionUndefinedError tape = qml.tape.QuantumTape(do_queue=False) with tape: if getattr(self, "inverse", False): qml.adjoint(self.decomposition, lazy=False)() else: self.decomposition() if not self.data: # original operation has no trainable parameters tape.trainable_params = {} return tape
@property def arithmetic_depth(self) -> int: """Arithmetic depth of the operator.""" return 0
[docs] def map_wires(self, wire_map: dict): """Returns a copy of the current operator with its wires changed according to the given wire map. Args: wire_map (dict): dictionary containing the old wires as keys and the new wires as values Returns: .Operator: new operator """ new_op = copy.copy(self) new_op._wires = Wires([wire_map.get(wire, wire) for wire in self.wires]) return new_op
[docs] def simplify(self) -> "Operator": # pylint: disable=unused-argument """Reduce the depth of nested operators to the minimum. Returns: .Operator: simplified operator """ return self
def __add__(self, other): """The addition operation of Operator-Operator objects and Operator-scalar.""" if isinstance(other, Operator): return qml.op_sum(self, other) if other == 0: return self try: return qml.op_sum(self, qml.s_prod(scalar=other, operator=qml.Identity(self.wires))) except ValueError as e: raise ValueError(f"Cannot add Operator and {type(other)}") from e __radd__ = __add__ def __mul__(self, other): """The scalar multiplication between scalars and Operators.""" try: return qml.s_prod(scalar=other, operator=self) except ValueError as e: raise ValueError(f"Cannot multiply Operator and {type(other)}.") from e __rmul__ = __mul__ def __matmul__(self, other): """The product operation between Operator objects.""" if isinstance(other, Operator): return qml.prod(self, other) raise ValueError("Can only perform tensor products between operators.") def __sub__(self, other): """The substraction operation of Operator-Operator objects and Operator-scalar.""" if isinstance(other, (Operator, numbers.Number)): return self + (-other) raise ValueError(f"Cannot substract {type(other)} from Operator.") def __rsub__(self, other): """The reverse substraction operation of Operator-Operator objects and Operator-scalar.""" return -self + other def __neg__(self): """The negation operation of an Operator object.""" return qml.s_prod(scalar=-1, operator=self) def __pow__(self, other): r"""The power operation of an Operator object.""" if isinstance(other, numbers.Number): return qml.pow(self, z=other) raise ValueError(f"Cannot raise an Operator with an exponent of type {type(other)}")
# ============================================================================= # Base Operation class # =============================================================================
[docs]class Operation(Operator): r"""Base class representing quantum gates or channels applied to quantum states. Operations define some additional properties, that are used for external transformations such as gradient transforms. The following three class attributes are optional, but in most cases at least one should be clearly defined to avoid unexpected behavior during differentiation. * :attr:`~.Operation.grad_recipe` * :attr:`~.Operation.parameter_frequencies` * :attr:`~.Operation.generator` Note that ``grad_recipe`` takes precedence when computing parameter-shift derivatives. Finally, these optional class attributes are used by certain transforms, quantum optimizers, and gradient methods. For details on how they are used during differentiation and other transforms, please see the documentation for :class:`~.gradients.param_shift`, :class:`~.metric_tensor`, :func:`~.reconstruct`. Args: params (tuple[tensor_like]): trainable parameters wires (Iterable[Any] or Any): Wire label(s) that the operator acts on. If not given, args[-1] is interpreted as wires. do_queue (bool): indicates whether the operator should be recorded when created in a tape context id (str): custom label given to an operator instance, can be useful for some applications where the instance has to be identified """ @property def grad_method(self): """Gradient computation method. * ``'A'``: analytic differentiation using the parameter-shift method. * ``'F'``: finite difference numerical differentiation. * ``None``: the operation may not be differentiated. Default is ``'F'``, or ``None`` if the Operation has zero parameters. """ if self.num_params == 0: return None if self.grad_recipe != [None] * self.num_params: return "A" try: self.parameter_frequencies # pylint:disable=pointless-statement return "A" except ParameterFrequenciesUndefinedError: return "F" grad_recipe = None r"""tuple(Union(list[list[float]], None)) or None: Gradient recipe for the parameter-shift method. This is a tuple with one nested list per operation parameter. For parameter :math:`\phi_k`, the nested list contains elements of the form :math:`[c_i, a_i, s_i]` where :math:`i` is the index of the term, resulting in a gradient recipe of .. math:: \frac{\partial}{\partial\phi_k}f = \sum_{i} c_i f(a_i \phi_k + s_i). If ``None``, the default gradient recipe containing the two terms :math:`[c_0, a_0, s_0]=[1/2, 1, \pi/2]` and :math:`[c_1, a_1, s_1]=[-1/2, 1, -\pi/2]` is assumed for every parameter. """ # Attributes for compilation transforms basis = None """str or None: The target operation for controlled gates. target operation. If not ``None``, should take a value of ``"X"``, ``"Y"``, or ``"Z"``. For example, ``X`` and ``CNOT`` have ``basis = "X"``, whereas ``ControlledPhaseShift`` and ``RZ`` have ``basis = "Z"``. """ @property def control_wires(self): # pragma: no cover r"""Control wires of the operator. For operations that are not controlled, this is an empty ``Wires`` object of length ``0``. Returns: Wires: The control wires of the operation. """ return Wires([])
[docs] def single_qubit_rot_angles(self): r"""The parameters required to implement a single-qubit gate as an equivalent ``Rot`` gate, up to a global phase. Returns: tuple[float, float, float]: A list of values :math:`[\phi, \theta, \omega]` such that :math:`RZ(\omega) RY(\theta) RZ(\phi)` is equivalent to the original operation. """ raise NotImplementedError
[docs] def get_parameter_shift(self, idx): r"""Multiplier and shift for the given parameter, based on its gradient recipe. Args: idx (int): parameter index within the operation Returns: list[[float, float, float]]: list of multiplier, coefficient, shift for each term in the gradient recipe Note that the default value for ``shift`` is None, which is replaced by the default shift :math:`\pi/2`. """ warnings.warn( "The method get_parameter_shift is deprecated. Use the methods of " "the gradients module for general parameter-shift rules instead.", UserWarning, ) # get the gradient recipe for this parameter recipe = self.grad_recipe[idx] if recipe is not None: return recipe # We no longer assume any default parameter-shift rule to apply. raise OperatorPropertyUndefined( f"The operation {self.name} does not have a parameter-shift recipe defined." " This error might occur if previously the two-term shift rule was assumed" " silently. In this case, consider adding it explicitly to the operation." )
@property def parameter_frequencies(self): r"""Returns the frequencies for each operator parameter with respect to an expectation value of the form :math:`\langle \psi | U(\mathbf{p})^\dagger \hat{O} U(\mathbf{p})|\psi\rangle`. These frequencies encode the behaviour of the operator :math:`U(\mathbf{p})` on the value of the expectation value as the parameters are modified. For more details, please see the :mod:`.pennylane.fourier` module. Returns: list[tuple[int or float]]: Tuple of frequencies for each parameter. Note that only non-negative frequency values are returned. **Example** >>> op = qml.CRot(0.4, 0.1, 0.3, wires=[0, 1]) >>> op.parameter_frequencies [(0.5, 1), (0.5, 1), (0.5, 1)] For operators that define a generator, the parameter frequencies are directly related to the eigenvalues of the generator: >>> op = qml.ControlledPhaseShift(0.1, wires=[0, 1]) >>> op.parameter_frequencies [(1,)] >>> gen = qml.generator(op, format="observable") >>> gen_eigvals = qml.eigvals(gen) >>> qml.gradients.eigvals_to_frequencies(tuple(gen_eigvals)) (1.0,) For more details on this relationship, see :func:`.eigvals_to_frequencies`. """ if self.num_params == 1: # if the operator has a single parameter, we can query the # generator, and if defined, use its eigenvalues. try: gen = qml.generator(self, format="observable") except GeneratorUndefinedError as e: raise ParameterFrequenciesUndefinedError( f"Operation {self.name} does not have parameter frequencies defined." ) from e with warnings.catch_warnings(): warnings.filterwarnings( action="ignore", message=r".+ eigenvalues will be computed numerically\." ) eigvals = qml.eigvals(gen) eigvals = tuple(np.round(eigvals, 8)) return qml.gradients.eigvals_to_frequencies(eigvals) raise ParameterFrequenciesUndefinedError( f"Operation {self.name} does not have parameter frequencies defined, " "and parameter frequencies can not be computed as no generator is defined." ) @property def inverse(self): """Boolean determining if the inverse of the operation was requested.""" return self._inverse @inverse.setter def inverse(self, boolean): warnings.warn( "In-place inversion with inverse is deprecated. Please use qml.adjoint or" " qml.pow instead.", UserWarning, ) self._inverse = boolean
[docs] def inv(self): """Inverts the operator. This method concatenates a string to the name of the operation, to indicate that the inverse will be used for computations. Any subsequent call of this method will toggle between the original operation and the inverse of the operation. Returns: :class:`Operator`: operation to be inverted """ self.inverse = not self._inverse return self
[docs] def matrix(self, wire_order=None): canonical_matrix = self.compute_matrix(*self.parameters, **self.hyperparameters) if self.inverse: canonical_matrix = qml.math.conj(qml.math.moveaxis(canonical_matrix, -2, -1)) return expand_matrix(canonical_matrix, wires=self.wires, wire_order=wire_order)
[docs] def eigvals(self): op_eigvals = super().eigvals() if self.inverse: return qml.math.conj(op_eigvals) return op_eigvals
@property def base_name(self): """If inverse is requested, this is the name of the original operator to be inverted.""" return self.__class__.__name__ @property def name(self): """Name of the operator.""" return self._name + ".inv" if self.inverse else self._name
[docs] def label(self, decimals=None, base_label=None, cache=None): if self.inverse: base_label = base_label or self.__class__.__name__ base_label += "⁻¹" return super().label(decimals=decimals, base_label=base_label, cache=cache)
def __init__(self, *params, wires=None, do_queue=True, id=None): self._inverse = False super().__init__(*params, wires=wires, do_queue=do_queue, id=id) # check the grad_recipe validity if self.grad_recipe is None: # Make sure grad_recipe is an iterable of correct length instead of None self.grad_recipe = [None] * self.num_params
[docs]class Channel(Operation, abc.ABC): r"""Base class for quantum channels. Quantum channels have to define an additional numerical representation as Kraus matrices. Args: params (tuple[tensor_like]): trainable parameters wires (Iterable[Any] or Any): Wire label(s) that the operator acts on. If not given, args[-1] is interpreted as wires. do_queue (bool): indicates whether the operator should be recorded when created in a tape context id (str): custom label given to an operator instance, can be useful for some applications where the instance has to be identified """ # pylint: disable=abstract-method
[docs] @staticmethod @abc.abstractmethod def compute_kraus_matrices(*params, **hyperparams): # pylint:disable=unused-argument """Kraus matrices representing a quantum channel, specified in the computational basis. This is a static method that should be defined for all new channels, and which allows matrices to be computed directly without instantiating the channel first. To return the Kraus matrices of an *instantiated* channel, please use the :meth:`~.Operator.kraus_matrices()` method instead. .. note:: This method gets overwritten by subclasses to define the kraus matrix representation of a particular operator. Args: params (list): trainable parameters of the operator, as stored in the ``parameters`` attribute hyperparams (dict): non-trainable hyperparameters of the operator, as stored in the ``hyperparameters`` attribute Returns: list (array): list of Kraus matrices **Example** >>> qml.AmplitudeDamping.compute_kraus_matrices(0.1) [array([[1., 0.], [0., 0.9486833]]), array([[0., 0.31622777], [0., 0.]])] """ raise NotImplementedError
[docs] def kraus_matrices(self): r"""Kraus matrices of an instantiated channel in the computational basis. Returns: list (array): list of Kraus matrices ** Example** >>> U = qml.AmplitudeDamping(0.1, wires=1) >>> U.kraus_matrices() [array([[1., 0.], [0., 0.9486833]]), array([[0., 0.31622777], [0., 0.]])] """ return self.compute_kraus_matrices(*self.parameters, **self.hyperparameters)
# ============================================================================= # Base Observable class # =============================================================================
[docs]class Observable(Operator): """Base class representing observables. Observables define a return type Args: params (tuple[tensor_like]): trainable parameters wires (Iterable[Any] or Any): Wire label(s) that the operator acts on. If not given, args[-1] is interpreted as wires. do_queue (bool): indicates whether the operator should be recorded when created in a tape context id (str): custom label given to an operator instance, can be useful for some applications where the instance has to be identified """ @property def _queue_category(self): """Used for sorting objects into their respective lists in `QuantumTape` objects. This property is a temporary solution that should not exist long-term and should not be used outside of ``QuantumTape._process_queue``. Options are: * `"_prep"` * `"_ops"` * `"_measurements"` * None Non-pauli observables, like Tensor, Hermitian, and Hamiltonian, should not be processed into any queue. The Pauli observables double as Operations, and should therefore be processed into `_ops` if unowned. """ return "_ops" if isinstance(self, Operation) else None @property def is_hermitian(self): """All observables must be hermitian""" return True # pylint: disable=abstract-method return_type = None """None or ObservableReturnTypes: Measurement type that this observable is called with.""" def __repr__(self): """Constructor-call-like representation.""" temp = super().__repr__() if self.return_type is None: return temp if self.return_type is qml.measurements.Probability: return repr(self.return_type) + f"(wires={self.wires.tolist()})" return repr(self.return_type) + "(" + temp + ")" def __matmul__(self, other): if isinstance(other, (Tensor, qml.Hamiltonian)): return other.__rmatmul__(self) if isinstance(other, Observable): return Tensor(self, other) try: return super().__matmul__(other=other) except ValueError as e: raise ValueError("Can only perform tensor products between operators.") from e def _obs_data(self): r"""Extracts the data from a Observable or Tensor and serializes it in an order-independent fashion. This allows for comparison between observables that are equivalent, but are expressed in different orders. For example, `qml.PauliX(0) @ qml.PauliZ(1)` and `qml.PauliZ(1) @ qml.PauliX(0)` are equivalent observables with different orderings. **Example** >>> tensor = qml.PauliX(0) @ qml.PauliZ(1) >>> print(tensor._obs_data()) {("PauliZ", <Wires = [1]>, ()), ("PauliX", <Wires = [0]>, ())} """ obs = Tensor(self).non_identity_obs tensor = set() for ob in obs: parameters = tuple(param.tobytes() for param in ob.parameters) tensor.add((ob.name, ob.wires, parameters)) return tensor
[docs] def compare(self, other): r"""Compares with another :class:`~.Hamiltonian`, :class:`~Tensor`, or :class:`~Observable`, to determine if they are equivalent. Observables/Hamiltonians are equivalent if they represent the same operator (their matrix representations are equal), and they are defined on the same wires. .. Warning:: The compare method does **not** check if the matrix representation of a :class:`~.Hermitian` observable is equal to an equivalent observable expressed in terms of Pauli matrices. To do so would require the matrix form of Hamiltonians and Tensors be calculated, which would drastically increase runtime. Returns: (bool): True if equivalent. **Examples** >>> ob1 = qml.PauliX(0) @ qml.Identity(1) >>> ob2 = qml.Hamiltonian([1], [qml.PauliX(0)]) >>> ob1.compare(ob2) True >>> ob1 = qml.PauliX(0) >>> ob2 = qml.Hermitian(np.array([[0, 1], [1, 0]]), 0) >>> ob1.compare(ob2) False """ if isinstance(other, qml.Hamiltonian): return other.compare(self) if isinstance(other, (Tensor, Observable)): return other._obs_data() == self._obs_data() raise ValueError( "Can only compare an Observable/Tensor, and a Hamiltonian/Observable/Tensor." )
def __add__(self, other): r"""The addition operation between Observables/Tensors/qml.Hamiltonian objects.""" if isinstance(other, qml.Hamiltonian): return other + self if isinstance(other, (Observable, Tensor)): return qml.Hamiltonian([1, 1], [self, other], simplify=True) try: return super().__add__(other=other) except ValueError as e: raise ValueError(f"Cannot add Observable and {type(other)}") from e __radd__ = __add__ def __mul__(self, a): r"""The scalar multiplication operation between a scalar and an Observable/Tensor.""" if isinstance(a, (int, float)): return qml.Hamiltonian([a], [self], simplify=True) try: return super().__mul__(other=a) except ValueError as e: raise ValueError(f"Cannot multiply Observable by {type(a)}") from e __rmul__ = __mul__ def __sub__(self, other): r"""The subtraction operation between Observables/Tensors/qml.Hamiltonian objects.""" if isinstance(other, (Observable, Tensor, qml.Hamiltonian)): return self.__add__(other.__mul__(-1)) try: return super().__sub__(other=other) except ValueError as e: raise ValueError(f"Cannot subtract {type(other)} from Observable") from e
[docs]class Tensor(Observable): """Container class representing tensor products of observables. To create a tensor, simply initiate it like so: >>> T = Tensor(qml.PauliX(0), qml.Hermitian(A, [1, 2])) You can also create a tensor from other Tensors: >>> T = Tensor(T, qml.PauliZ(4)) The ``@`` symbol can be used as a tensor product operation: >>> T = qml.PauliX(0) @ qml.Hadamard(2) .. note: This class is marked for deletion or overhaul. """ # pylint: disable=abstract-method return_type = None tensor = True def __init__(self, *args): # pylint: disable=super-init-not-called self._eigvals_cache = None self.obs: List[Observable] = [] self._args = args self.queue(init=True)
[docs] def label(self, decimals=None, base_label=None, cache=None): r"""How the operator is represented in diagrams and drawings. Args: decimals=None (Int): If ``None``, no parameters are included. Else, how to round the parameters. base_label=None (Iterable[str]): overwrite the non-parameter component of the label. Must be same length as ``obs`` attribute. cache=None (dict): dictionary that carries information between label calls in the same drawing Returns: str: label to use in drawings >>> T = qml.PauliX(0) @ qml.Hadamard(2) >>> T.label() '[email protected]' >>> T.label(base_label=["X0", "H2"]) '[email protected]' """ if base_label is not None: if len(base_label) != len(self.obs): raise ValueError( "Tensor label requires ``base_label`` keyword to be same length" " as tensor components." ) return "@".join( ob.label(decimals=decimals, base_label=lbl) for ob, lbl in zip(self.obs, base_label) ) return "@".join(ob.label(decimals=decimals) for ob in self.obs)
[docs] def queue(self, context=QueuingManager, init=False): # pylint: disable=arguments-differ constituents = self.obs if init: constituents = self._args for o in constituents: if init: if isinstance(o, Tensor): self.obs.extend(o.obs) elif isinstance(o, Observable): self.obs.append(o) else: raise ValueError("Can only perform tensor products between observables.") context.update_info(o, owner=self) context.append(self, owns=tuple(constituents)) return self
def __copy__(self): cls = self.__class__ copied_op = cls.__new__(cls) # pylint: disable=no-value-for-parameter copied_op.obs = self.obs.copy() copied_op._eigvals_cache = self._eigvals_cache return copied_op def __repr__(self): """Constructor-call-like representation.""" s = " @ ".join([repr(o) for o in self.obs]) if self.return_type is None: return s if self.return_type is qml.measurements.Probability: return repr(self.return_type) + f"(wires={self.wires.tolist()})" return repr(self.return_type) + "(" + s + ")" @property def name(self): """All constituent observable names making up the tensor product. Returns: list[str]: list containing all observable names """ return [o.name for o in self.obs] @property def num_wires(self): """Number of wires the tensor product acts on. Returns: int: number of wires """ return len(self.wires) @property def wires(self): """All wires in the system the tensor product acts on. Returns: Wires: wires addressed by the observables in the tensor product """ return Wires.all_wires([o.wires for o in self.obs]) @property def data(self): """Raw parameters of all constituent observables in the tensor product. Returns: list[Any]: flattened list containing all dependent parameters """ return sum((o.data for o in self.obs), []) @property def num_params(self): """Raw parameters of all constituent observables in the tensor product. Returns: list[Any]: flattened list containing all dependent parameters """ return len(self.data) @property def parameters(self): """Evaluated parameter values of all constituent observables in the tensor product. Returns: list[list[Any]]: nested list containing the parameters per observable in the tensor product """ return [o.parameters for o in self.obs] @property def non_identity_obs(self): """Returns the non-identity observables contained in the tensor product. Returns: list[:class:`~.Observable`]: list containing the non-identity observables in the tensor product """ return [obs for obs in self.obs if not isinstance(obs, qml.Identity)] @property def arithmetic_depth(self) -> int: return 1 + max(o.arithmetic_depth for o in self.obs) def __matmul__(self, other): if isinstance(other, Tensor): self.obs.extend(other.obs) elif isinstance(other, Observable): self.obs.append(other) else: raise ValueError("Can only perform tensor products between observables.") if QueuingManager.recording() and self not in QueuingManager.active_context()._queue: QueuingManager.append(self) QueuingManager.update_info(self, owns=tuple(self.obs)) QueuingManager.update_info(other, owner=self) return self def __rmatmul__(self, other): if isinstance(other, Observable): self.obs[:0] = [other] QueuingManager.update_info(self, owns=tuple(self.obs)) QueuingManager.update_info(other, owner=self) return self raise ValueError("Can only perform tensor products between observables.") __imatmul__ = __matmul__
[docs] def eigvals(self): """Return the eigenvalues of the specified tensor product observable. This method uses pre-stored eigenvalues for standard observables where possible. Returns: array[float]: array containing the eigenvalues of the tensor product observable """ if self._eigvals_cache is not None: return self._eigvals_cache standard_observables = {"PauliX", "PauliY", "PauliZ", "Hadamard"} # observable should be Z^{\otimes n} self._eigvals_cache = pauli_eigs(len(self.wires)) # check if there are any non-standard observables (such as Identity) if set(self.name) - standard_observables: # Tensor product of observables contains a mixture # of standard and non-standard observables self._eigvals_cache = np.array([1]) for k, g in itertools.groupby(self.obs, lambda x: x.name in standard_observables): if k: # Subgroup g contains only standard observables. self._eigvals_cache = np.kron(self._eigvals_cache, pauli_eigs(len(list(g)))) else: # Subgroup g contains only non-standard observables. for ns_ob in g: # loop through all non-standard observables self._eigvals_cache = np.kron(self._eigvals_cache, ns_ob.eigvals()) return self._eigvals_cache
# pylint: disable=arguments-renamed, invalid-overridden-method @property def has_diagonalizing_gates(self): r"""Bool: Whether or not the Tensor returns defined diagonalizing gates.""" return all(o.has_diagonalizing_gates for o in self.obs)
[docs] def diagonalizing_gates(self): """Return the gate set that diagonalizes a circuit according to the specified tensor observable. This method uses pre-stored eigenvalues for standard observables where possible and stores the corresponding eigenvectors from the eigendecomposition. Returns: list: list containing the gates diagonalizing the tensor observable """ diag_gates = [] for o in self.obs: diag_gates.extend(o.diagonalizing_gates()) return diag_gates
[docs] def matrix(self, wire_order=None): r"""Matrix representation of the Tensor operator in the computational basis. .. note:: The wire_order argument is added for compatibility, but currently not implemented. The Tensor class is planned to be removed soon. Args: wire_order (Iterable): global wire order, must contain all wire labels in the operator's wires Returns: array: matrix representation **Example** >>> O = qml.PauliZ(0) @ qml.PauliZ(2) >>> O.matrix() array([[ 1, 0, 0, 0], [ 0, -1, 0, 0], [ 0, 0, -1, 0], [ 0, 0, 0, 1]]) To get the full :math:`2^3\times 2^3` Hermitian matrix acting on the 3-qubit system, the identity on wire 1 must be explicitly included: >>> O = qml.PauliZ(0) @ qml.Identity(1) @ qml.PauliZ(2) >>> O.matrix() array([[ 1., 0., 0., 0., 0., 0., 0., 0.], [ 0., -1., 0., -0., 0., -0., 0., -0.], [ 0., 0., 1., 0., 0., 0., 0., 0.], [ 0., -0., 0., -1., 0., -0., 0., -0.], [ 0., 0., 0., 0., -1., -0., -0., -0.], [ 0., -0., 0., -0., -0., 1., -0., 0.], [ 0., 0., 0., 0., -0., -0., -1., -0.], [ 0., -0., 0., -0., -0., 0., -0., 1.]]) """ if wire_order is not None: raise NotImplementedError("The wire_order argument is currently not implemented.") # Check for partially (but not fully) overlapping wires in the observables partial_overlap = self.check_wires_partial_overlap() # group the observables based on what wires they act on U_list = [] for _, g in itertools.groupby(self.obs, lambda x: x.wires.labels): # extract the matrices of each diagonalizing gate mats = [i.matrix() for i in g] if len(mats) > 1: # multiply all unitaries together before appending mats = [multi_dot(mats)] # append diagonalizing unitary for specific wire to U_list U_list.append(mats[0]) mat_size = np.prod([np.shape(mat)[0] for mat in U_list]) wire_size = 2 ** len(self.wires) if mat_size != wire_size: if partial_overlap: warnings.warn( "The matrix for Tensors of Tensors/Observables with partially " "overlapping wires might yield unexpected results. In particular " "the matrix size might be larger than intended." ) else: warnings.warn( f"The size of the returned matrix ({mat_size}) will not be compatible " f"with the subspace of the wires of the Tensor ({wire_size}). " "This likely is due to wires being used in multiple tensor product " "factors of the Tensor." ) # Return the Hermitian matrix representing the observable # over the defined wires. return functools.reduce(np.kron, U_list)
[docs] def check_wires_partial_overlap(self): r"""Tests whether any two observables in the Tensor have partially overlapping wires and raise a warning if they do. .. note:: Fully overlapping wires, i.e., observables with same (sets of) wires are not reported, as the ``matrix`` method is well-defined and implemented for this scenario. """ for o1, o2 in itertools.combinations(self.obs, r=2): shared = qml.wires.Wires.shared_wires([o1.wires, o2.wires]) if shared and (shared != o1.wires or shared != o2.wires): return 1 return 0
[docs] def sparse_matrix( self, wires=None, format="csr" ): # pylint:disable=arguments-renamed, arguments-differ r"""Computes, by default, a `scipy.sparse.csr_matrix` representation of this Tensor. This is useful for larger qubit numbers, where the dense matrix becomes very large, while consisting mostly of zero entries. Args: wires (Iterable): Wire labels that indicate the order of wires according to which the matrix is constructed. If not provided, ``self.wires`` is used. format: the output format for the sparse representation. All scipy sparse formats are accepted. Returns: :class:`scipy.sparse._csr.csr_matrix`: sparse matrix representation **Example** Consider the following tensor: >>> t = qml.PauliX(0) @ qml.PauliZ(1) Without passing wires, the sparse representation is given by: >>> print(t.sparse_matrix()) (0, 2) 1 (1, 3) -1 (2, 0) 1 (3, 1) -1 If we define a custom wire ordering, the matrix representation changes accordingly: >>> print(t.sparse_matrix(wires=[1, 0])) (0, 1) 1 (1, 0) 1 (2, 3) -1 (3, 2) -1 We can also enforce implicit identities by passing wire labels that are not present in the constituent operations: >>> res = t.sparse_matrix(wires=[0, 1, 2]) >>> print(res.shape) (8, 8) """ if wires is None: wires = self.wires else: wires = Wires(wires) list_of_sparse_ops = [eye(2, format="coo")] * len(wires) for o in self.obs: if len(o.wires) > 1: # todo: deal with multi-qubit operations that do not act on consecutive qubits raise ValueError( f"Can only compute sparse representation for tensors whose operations " f"act on consecutive wires; got {o}." ) # store the single-qubit ops according to the order of their wires idx = wires.index(o.wires) list_of_sparse_ops[idx] = coo_matrix(o.matrix()) return functools.reduce(lambda i, j: kron(i, j, format=format), list_of_sparse_ops)
[docs] def prune(self): """Returns a pruned tensor product of observables by removing :class:`~.Identity` instances from the observables building up the :class:`~.Tensor`. The ``return_type`` attribute is preserved while pruning. If the tensor product only contains one observable, then this observable instance is returned. Note that, as a result, this method can return observables that are not a :class:`~.Tensor` instance. **Example:** Pruning that returns a :class:`~.Tensor`: >>> O = qml.PauliZ(0) @ qml.Identity(1) @ qml.PauliZ(2) >>> O.prune() <pennylane.operation.Tensor at 0x7fc1642d1590 >>> [(o.name, o.wires) for o in O.prune().obs] [('PauliZ', [0]), ('PauliZ', [2])] Pruning that returns a single observable: >>> O = qml.PauliZ(0) @ qml.Identity(1) >>> O_pruned = O.prune() >>> (O_pruned.name, O_pruned.wires) ('PauliZ', [0]) Returns: ~.Observable: the pruned tensor product of observables """ if len(self.non_identity_obs) == 0: # Return a single Identity as the tensor only contains Identities obs = qml.Identity(self.wires[0]) elif len(self.non_identity_obs) == 1: obs = self.non_identity_obs[0] else: obs = Tensor(*self.non_identity_obs) obs.return_type = self.return_type return obs
[docs] def map_wires(self, wire_map: dict): """Returns a copy of the current tensor with its wires changed according to the given wire map. Args: wire_map (dict): dictionary containing the old wires as keys and the new wires as values Returns: .Tensor: new tensor """ cls = self.__class__ new_op = cls.__new__(cls) # pylint: disable=no-value-for-parameter new_op.obs = [obs.map_wires(wire_map) for obs in self.obs] new_op._eigvals_cache = self._eigvals_cache return new_op
# ============================================================================= # CV Operations and observables # =============================================================================
[docs]class CV: """A mixin base class denoting a continuous-variable operation.""" # pylint: disable=no-member
[docs] def heisenberg_expand(self, U, wire_order): """Expand the given local Heisenberg-picture array into a full-system one. Args: U (array[float]): array to expand (expected to be of the dimension ``1+2*self.num_wires``) wire_order (Wires): global wire order defining which subspace the operator acts on Raises: ValueError: if the size of the input matrix is invalid or `num_wires` is incorrect Returns: array[float]: expanded array, dimension ``1+2*num_wires`` """ U_dim = len(U) nw = len(self.wires) if U.ndim > 2: raise ValueError("Only order-1 and order-2 arrays supported.") if U_dim != 1 + 2 * nw: raise ValueError(f"{self.name}: Heisenberg matrix is the wrong size {U_dim}.") if len(wire_order) == 0 or len(self.wires) == len(wire_order): # no expansion necessary (U is a full-system matrix in the correct order) return U if not wire_order.contains_wires(self.wires): raise ValueError( f"{self.name}: Some observable wires {self.wires} do not exist on this device with wires {wire_order}" ) # get the indices that the operation's wires have on the device wire_indices = wire_order.indices(self.wires) # expand U into the I, x_0, p_0, x_1, p_1, ... basis dim = 1 + len(wire_order) * 2 def loc(w): "Returns the slice denoting the location of (x_w, p_w) in the basis." ind = 2 * w + 1 return slice(ind, ind + 2) if U.ndim == 1: W = np.zeros(dim) W[0] = U[0] for k, w in enumerate(wire_indices): W[loc(w)] = U[loc(k)] elif U.ndim == 2: if isinstance(self, Observable): W = np.zeros((dim, dim)) else: W = np.eye(dim) W[0, 0] = U[0, 0] for k1, w1 in enumerate(wire_indices): s1 = loc(k1) d1 = loc(w1) # first column W[d1, 0] = U[s1, 0] # first row (for gates, the first row is always (1, 0, 0, ...), but not for observables!) W[0, d1] = U[0, s1] for k2, w2 in enumerate(wire_indices): W[d1, loc(w2)] = U[s1, loc(k2)] # block k1, k2 in U goes to w1, w2 in W. return W
@staticmethod def _heisenberg_rep(p): r"""Heisenberg picture representation of the operation. * For Gaussian CV gates, this method returns the matrix of the linear transformation carried out by the gate for the given parameter values. The method is not defined for non-Gaussian gates. **The existence of this method is equivalent to setting** ``grad_method = 'A'``. * For observables, returns a real vector (first-order observables) or symmetric matrix (second-order observables) of expansion coefficients of the observable. For single-mode Operations we use the basis :math:`\mathbf{r} = (\I, \x, \p)`. For multi-mode Operations we use the basis :math:`\mathbf{r} = (\I, \x_0, \p_0, \x_1, \p_1, \ldots)`. .. note:: For gates, we assume that the inverse transformation is obtained by negating the first parameter. Args: p (Sequence[float]): parameter values for the transformation Returns: array[float]: :math:`\tilde{U}` or :math:`q` """ # pylint: disable=unused-argument return None @classproperty def supports_heisenberg(self): """Whether a CV operator defines a Heisenberg representation. This indicates that it is Gaussian and does not block the use of the parameter-shift differentiation method if found between the differentiated gate and an observable. Returns: boolean """ return CV._heisenberg_rep != self._heisenberg_rep
[docs]class CVOperation(CV, Operation): """Base class representing continuous-variable quantum gates. CV operations provide a special Heisenberg representation, as well as custom methods for differentiation. Args: params (tuple[tensor_like]): trainable parameters wires (Iterable[Any] or Any): Wire label(s) that the operator acts on. If not given, args[-1] is interpreted as wires. do_queue (bool): indicates whether the operator should be recorded when created in a tape context id (str): custom label given to an operator instance, can be useful for some applications where the instance has to be identified """ # pylint: disable=abstract-method @classproperty def supports_parameter_shift(self): """Returns True iff the CV Operation supports the parameter-shift differentiation method. This means that it has ``grad_method='A'`` and has overridden the :meth:`~.CV._heisenberg_rep` static method. """ return self.grad_method == "A" and self.supports_heisenberg
[docs] def heisenberg_pd(self, idx): """Partial derivative of the Heisenberg picture transform matrix. Computed using grad_recipe. Args: idx (int): index of the parameter with respect to which the partial derivative is computed. Returns: array[float]: partial derivative """ # get the gradient recipe for this parameter recipe = self.grad_recipe[idx] # Default values multiplier = 0.5 a = 1 shift = np.pi / 2 # We set the default recipe to as follows: # ∂f(x) = c*f(x+s) - c*f(x-s) default_param_shift = [[multiplier, a, shift], [-multiplier, a, -shift]] param_shift = default_param_shift if recipe is None else recipe pd = None # partial derivative of the transformation p = self.parameters original_p_idx = p[idx] for c, _a, s in param_shift: # evaluate the transform at the shifted parameter values p[idx] = _a * original_p_idx + s U = self._heisenberg_rep(p) # pylint: disable=assignment-from-none if pd is None: pd = c * U else: pd += c * U return pd
[docs] def heisenberg_tr(self, wire_order, inverse=False): r"""Heisenberg picture representation of the linear transformation carried out by the gate at current parameter values. Given a unitary quantum gate :math:`U`, we may consider its linear transformation in the Heisenberg picture, :math:`U^\dagger(\cdot) U`. If the gate is Gaussian, this linear transformation preserves the polynomial order of any observables that are polynomials in :math:`\mathbf{r} = (\I, \x_0, \p_0, \x_1, \p_1, \ldots)`. This also means it maps :math:`\text{span}(\mathbf{r})` into itself: .. math:: U^\dagger \mathbf{r}_i U = \sum_j \tilde{U}_{ij} \mathbf{r}_j For Gaussian CV gates, this method returns the transformation matrix for the current parameter values of the Operation. The method is not defined for non-Gaussian (and non-CV) gates. Args: wire_order (Wires): global wire order defining which subspace the operator acts on inverse (bool): if True, return the inverse transformation instead Raises: RuntimeError: if the specified operation is not Gaussian or is missing the `_heisenberg_rep` method Returns: array[float]: :math:`\tilde{U}`, the Heisenberg picture representation of the linear transformation """ p = [qml.math.toarray(a) for a in self.parameters] if inverse: try: # TODO: expand this for the new par domain class, for non-unitary matrices. p[0] = np.linalg.inv(p[0]) except np.linalg.LinAlgError: p[0] = -p[0] # negate first parameter U = self._heisenberg_rep(p) # pylint: disable=assignment-from-none # not defined? if U is None: raise RuntimeError( f"{self.name} is not a Gaussian operation, or is missing the _heisenberg_rep method." ) return self.heisenberg_expand(U, wire_order)
[docs]class CVObservable(CV, Observable): r"""Base class representing continuous-variable observables. CV observables provide a special Heisenberg representation. The class attribute :attr:`~.ev_order` can be defined to indicate to PennyLane whether the corresponding CV observable is a polynomial in the quadrature operators. If so, * ``ev_order = 1`` indicates a first order polynomial in quadrature operators :math:`(\x, \p)`. * ``ev_order = 2`` indicates a second order polynomial in quadrature operators :math:`(\x, \p)`. If :attr:`~.ev_order` is not ``None``, then the Heisenberg representation of the observable should be defined in the static method :meth:`~.CV._heisenberg_rep`, returning an array of the correct dimension. Args: params (tuple[tensor_like]): trainable parameters wires (Iterable[Any] or Any): Wire label(s) that the operator acts on. If not given, args[-1] is interpreted as wires. do_queue (bool): indicates whether the operator should be recorded when created in a tape context id (str): custom label given to an operator instance, can be useful for some applications where the instance has to be identified """ # pylint: disable=abstract-method ev_order = None #: None, int: Order in `(x, p)` that a CV observable is a polynomial of.
[docs] def heisenberg_obs(self, wire_order): r"""Representation of the observable in the position/momentum operator basis. Returns the expansion :math:`q` of the observable, :math:`Q`, in the basis :math:`\mathbf{r} = (\I, \x_0, \p_0, \x_1, \p_1, \ldots)`. * For first-order observables returns a real vector such that :math:`Q = \sum_i q_i \mathbf{r}_i`. * For second-order observables returns a real symmetric matrix such that :math:`Q = \sum_{ij} q_{ij} \mathbf{r}_i \mathbf{r}_j`. Args: wire_order (Wires): global wire order defining which subspace the operator acts on Returns: array[float]: :math:`q` """ p = self.parameters U = self._heisenberg_rep(p) # pylint: disable=assignment-from-none return self.heisenberg_expand(U, wire_order)
[docs]def operation_derivative(operation) -> np.ndarray: r"""Calculate the derivative of an operation. For an operation :math:`e^{i \hat{H} \phi t}`, this function returns the matrix representation in the standard basis of its derivative with respect to :math:`t`, i.e., .. math:: \frac{d \, e^{i \hat{H} \phi t}}{dt} = i \phi \hat{H} e^{i \hat{H} \phi t}, where :math:`\phi` is a real constant. Args: operation (.Operation): The operation to be differentiated. Returns: array: the derivative of the operation as a matrix in the standard basis Raises: ValueError: if the operation does not have a generator or is not composed of a single trainable parameter """ generator = qml.matrix( qml.generator(operation, format="observable"), wire_order=operation.wires ) return 1j * generator @ operation.matrix()
[docs]@qml.BooleanFn def not_tape(obj): """Returns ``True`` if the object is not a quantum tape""" return isinstance(obj, qml.tape.QuantumTape)
[docs]@qml.BooleanFn def has_gen(obj): """Returns ``True`` if an operator has a generator defined.""" try: obj.generator() except (AttributeError, OperatorPropertyUndefined, GeneratorUndefinedError): return False return True
[docs]@qml.BooleanFn def has_grad_method(obj): """Returns ``True`` if an operator has a grad_method defined.""" return obj.grad_method is not None
[docs]@qml.BooleanFn def has_multipar(obj): """Returns ``True`` if an operator has more than one parameter according to ``num_params``.""" return obj.num_params > 1
[docs]@qml.BooleanFn def has_nopar(obj): """Returns ``True`` if an operator has no parameters according to ``num_params``.""" return obj.num_params == 0
[docs]@qml.BooleanFn def has_unitary_gen(obj): """Returns ``True`` if an operator has a unitary_generator according to the ``has_unitary_generator`` flag.""" # Linting check disabled as static analysis can misidentify qml.ops as the set instance qml.ops.qubit.ops return obj in qml.ops.qubit.attributes.has_unitary_generator # pylint:disable=no-member
[docs]@qml.BooleanFn def is_measurement(obj): """Returns ``True`` if an operator is a ``MeasurementProcess`` instance.""" return isinstance(obj, qml.measurements.MeasurementProcess)
[docs]@qml.BooleanFn def is_trainable(obj): """Returns ``True`` if any of the parameters of an operator is trainable according to ``qml.math.requires_grad``.""" return any(qml.math.requires_grad(p) for p in obj.parameters)
[docs]@qml.BooleanFn def defines_diagonalizing_gates(obj): """Returns ``True`` if an operator defines the diagonalizing gates are defined. This helper function is useful if the property is to be checked in a queuing context, but the resulting gates must not be queued. """ return obj.has_diagonalizing_gates
[docs]@qml.BooleanFn def gen_is_multi_term_hamiltonian(obj): """Returns ``True`` if an operator has a generator defined and it is a Hamiltonian with more than one term.""" try: o = obj.generator() except (AttributeError, OperatorPropertyUndefined, GeneratorUndefinedError): return False if isinstance(o, qml.Hamiltonian): if len(o.coeffs) > 1: return True return False