Source code for pennylane.qaoa.layers

# 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.
"""
Methods that define cost and mixer layers for use in QAOA workflows.
"""
import pennylane as qml
from pennylane.operation import Tensor


def _diagonal_terms(hamiltonian):
    r"""Checks if all terms in a Hamiltonian are products of diagonal Pauli gates
    (:class:`~.PauliZ` and :class:`~.Identity`).

    Args:
        hamiltonian (.Hamiltonian): The Hamiltonian being checked

    Returns:
        bool: ``True`` if all terms are products of diagonal Pauli gates, ``False`` otherwise
    """
    val = True

    for i in hamiltonian.ops:
        obs = i.obs if isinstance(i, Tensor) else [i]
        for j in obs:
            if j.name not in ("PauliZ", "Identity"):
                val = False
                break

    return val


def cost_layer(gamma, hamiltonian):
    r"""Applies the QAOA cost layer corresponding to a cost Hamiltonian.

    For the cost Hamiltonian :math:`H_C`, this is defined as the following unitary:

    .. math:: U_C \ = \ e^{-i \gamma H_C}

    where :math:`\gamma` is a variational parameter.

    Args:
        gamma (int or float): The variational parameter passed into the cost layer
        hamiltonian (.Hamiltonian): The cost Hamiltonian

    Raises:
        ValueError: if the terms of the supplied cost Hamiltonian are not exclusively products of diagonal Pauli gates

    .. details::
        :title: Usage Details

        We first define a cost Hamiltonian:

        .. code-block:: python3

            from pennylane import qaoa
            import pennylane as qml

            cost_h = qml.Hamiltonian([1, 1], [qml.Z(0), qml.Z(0) @ qml.Z(1)])

        We can then pass it into ``qaoa.cost_layer``, within a quantum circuit:

        .. code-block:: python

            dev = qml.device('default.qubit', wires=2)

            @qml.qnode(dev)
            def circuit(gamma):

                for i in range(2):
                    qml.Hadamard(wires=i)

                qaoa.cost_layer(gamma, cost_h)

                return [qml.expval(qml.Z(i)) for i in range(2)]

        which gives us a circuit of the form:

        >>> print(qml.draw(circuit)(0.5))
        0: ──H─╭ApproxTimeEvolution(1.00,1.00,0.50)─┤  <Z>
        1: ──H─╰ApproxTimeEvolution(1.00,1.00,0.50)─┤  <Z>
        >>> print(qml.draw(circuit, expansion_strategy="device")(0.5))
        0: ──H──RZ(1.00)─╭RZZ(1.00)─┤  <Z>
        1: ──H───────────╰RZZ(1.00)─┤  <Z>

    """
    if not isinstance(hamiltonian, qml.Hamiltonian):
        raise ValueError(
            f"hamiltonian must be of type pennylane.Hamiltonian, got {type(hamiltonian).__name__}"
        )

    if not _diagonal_terms(hamiltonian):
        raise ValueError("hamiltonian must be written only in terms of PauliZ and Identity gates")

    qml.templates.ApproxTimeEvolution(hamiltonian, gamma, 1)


def mixer_layer(alpha, hamiltonian):
    r"""Applies the QAOA mixer layer corresponding to a mixer Hamiltonian.

    For a mixer Hamiltonian :math:`H_M`, this is defined as the following unitary:

    .. math:: U_M \ = \ e^{-i \alpha H_M}

    where :math:`\alpha` is a variational parameter.

    Args:
        alpha (int or float): The variational parameter passed into the mixer layer
        hamiltonian (.Hamiltonian): The mixer Hamiltonian

    .. details::
        :title: Usage Details

        We first define a mixer Hamiltonian:

        .. code-block:: python3

            from pennylane import qaoa
            import pennylane as qml

            mixer_h = qml.Hamiltonian([1, 1], [qml.X(0), qml.X(0) @ qml.X(1)])

        We can then pass it into ``qaoa.mixer_layer``, within a quantum circuit:

        .. code-block:: python

            dev = qml.device('default.qubit', wires=2)

            @qml.qnode(dev)
            def circuit(alpha):

                for i in range(2):
                    qml.Hadamard(wires=i)

                qaoa.mixer_layer(alpha, mixer_h)

                return [qml.expval(qml.Z(i)) for i in range(2)]

        which gives us a circuit of the form:

        >>> print(qml.draw(circuit)(0.5))
        0: ──H─╭ApproxTimeEvolution(1.00,1.00,0.50)─┤  <Z>
        1: ──H─╰ApproxTimeEvolution(1.00,1.00,0.50)─┤  <Z>
        >>> print(qml.draw(circuit, expansion_strategy="device")(0.5))
        0: ──H──RX(1.00)─╭RXX(1.00)─┤  <Z>
        1: ──H───────────╰RXX(1.00)─┤  <Z>

    """
    if not isinstance(hamiltonian, qml.Hamiltonian):
        raise ValueError(
            f"hamiltonian must be of type pennylane.Hamiltonian, got {type(hamiltonian).__name__}"
        )

    qml.templates.ApproxTimeEvolution(hamiltonian, alpha, 1)

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