Source code for pennylane.templates.state_preparations.basis

# 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.
r"""
Contains the BasisStatePreparation template.
"""

import warnings

import numpy as np

import pennylane as qml
from pennylane.operation import AnyWires, Operation


class BasisStatePreparation(Operation):
    r"""
    Prepares a basis state on the given wires using a sequence of Pauli-X gates.

    .. warning::

        ``basis_state`` influences the circuit architecture and is therefore incompatible with
        gradient computations.

        ``BasisStatePreparation`` is deprecated and will be removed in version 0.40. Instead, please use ``BasisState``.

    Args:
        basis_state (array): Input array of shape ``(n,)``, where n is the number of wires
            the state preparation acts on.
        wires (Iterable): wires that the template acts on

    **Example**

    .. code-block:: python

        dev = qml.device("default.qubit", wires=4)

        @qml.qnode(dev)
        def circuit(basis_state):
            qml.BasisStatePreparation(basis_state, wires=range(4))
            return [qml.expval(qml.Z(i)) for i in range(4)]

        basis_state = [0, 1, 1, 0]

    >>> print(circuit(basis_state))
    [ 1. -1. -1.  1.]

    """

    num_params = 1
    num_wires = AnyWires
    grad_method = None
    ndim_params = (1,)

    def __init__(self, basis_state, wires, id=None):

        warnings.warn(
            "BasisStatePreparation is deprecated and will be removed in version 0.40. "
            "Instead, please use BasisState.",
            qml.PennyLaneDeprecationWarning,
        )

        basis_state = qml.math.stack(basis_state)

        # check if the `basis_state` param is batched
        batched = len(qml.math.shape(basis_state)) > 1

        state_batch = basis_state if batched else [basis_state]

        for i, state in enumerate(state_batch):
            shape = qml.math.shape(state)

            if len(shape) != 1:
                raise ValueError(
                    f"Basis states must be one-dimensional; state {i} has shape {shape}."
                )

            n_bits = shape[0]
            if n_bits != len(wires):
                raise ValueError(
                    f"Basis states must be of length {len(wires)}; state {i} has length {n_bits}."
                )

            if not qml.math.is_abstract(state):
                if any(bit not in [0, 1] for bit in state):
                    raise ValueError(
                        f"Basis states must only consist of 0s and 1s; state {i} is {state}"
                    )

        # TODO: basis_state should be a hyperparameter, not a trainable parameter.
        # However, this breaks a test that ensures compatibility with batch_transform.
        # The transform should be rewritten to support hyperparameters as well.
        super().__init__(basis_state, wires=wires, id=id)

    @staticmethod
    def compute_decomposition(basis_state, wires):  # pylint: disable=arguments-differ
        r"""Representation of the operator as a product of other operators.

        .. math:: O = O_1 O_2 \dots O_n.

        .. seealso:: :meth:`~.BasisStatePreparation.decomposition`.

        Args:
            basis_state (array): Input array of shape ``(len(wires),)``
            wires (Any or Iterable[Any]): wires that the operator acts on

        Returns:
            list[.Operator]: decomposition of the operator

        **Example**

        >>> qml.BasisStatePreparation.compute_decomposition(basis_state=[1, 1], wires=["a", "b"])
        [X('a'),
        X('b')]
        """
        if len(qml.math.shape(basis_state)) > 1:
            raise ValueError(
                "Broadcasting with BasisStatePreparation is not supported. Please use the "
                "qml.transforms.broadcast_expand transform to use broadcasting with "
                "BasisStatePreparation."
            )

        if not qml.math.is_abstract(basis_state):
            op_list = []
            for wire, state in zip(wires, basis_state):
                if state == 1:
                    op_list.append(qml.X(wire))
            return op_list

        op_list = []
        for wire, state in zip(wires, basis_state):
            op_list.append(qml.PhaseShift(state * np.pi / 2, wire))
            op_list.append(qml.RX(state * np.pi, wire))
            op_list.append(qml.PhaseShift(state * np.pi / 2, wire))

        return op_list

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