Source code for pennylane.ops.functions.simplify

# 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.
"""
This module contains the qml.simplify function.
"""
from __future__ import annotations

from collections.abc import Callable
from copy import copy
from typing import TYPE_CHECKING, Union

import pennylane as qml
from pennylane.measurements import MeasurementProcess
from pennylane.operation import Operator
from pennylane.queuing import QueuingManager
from pennylane.tape import QuantumScript, QuantumScriptBatch
from pennylane.typing import PostprocessingFn

if TYPE_CHECKING:
    from pennylane.workflow import QNode



[docs]
def simplify(input: Union[Operator, MeasurementProcess, QuantumScript, QNode, Callable]):
    """Simplifies an operator, tape, qnode or quantum function by reducing its arithmetic depth
    or number of rotation parameters.

    Args:
        input (.Operator, .MeasurementProcess, pennylane.QNode, .QuantumTape, or Callable): an
            operator, quantum node, tape or function that applies quantum operations

    Returns:
        (Operator or MeasurementProcess or qnode (QNode) or quantum function (Callable)
        or tuple[List[QuantumTape], function]): Simplified input. If an operator or measurement
        process is provided as input, the simplified input is returned directly. Otherwise, the
        transformed circuit is returned as described in :func:`qml.transform <pennylane.transform>`.

    **Example**

    Given an instantiated operator, ``qml.simplify`` reduces the operator's arithmetic depth:

    >>> op = qml.adjoint(qml.RX(0.54, wires=0) + qml.X(0) + qml.Z(1))
    >>> op.arithmetic_depth
    3
    >>> sim_op = qml.simplify(op)
    >>> sim_op.arithmetic_depth
    2
    >>> type(sim_op)
    pennylane.ops.op_math.sum.Sum
    >>> sim_op.operands
    (Adjoint(RX)(0.54, wires=[0]),
    Adjoint(PauliX)(wires=[0]),
    Adjoint(PauliZ)(wires=[1]))

    This function can also simplify the number of rotation gate parameters:

    >>> qml.simplify(qml.Rot(np.pi / 2, 0.1, -np.pi / 2, wires=0))
    RX(0.1, wires=[0])

    Both types of simplification occur together:

    >>> op = qml.adjoint(qml.U2(-np.pi/2, np.pi/2, wires=0) + qml.X(0))
    >>> op
    Adjoint(Sum)([-1.5707963267948966, 1.5707963267948966], [], wires=[0])
    >>> qml.simplify(op)
    Adjoint(RX)(1.5707963267948966, wires=[0]) + Adjoint(PauliX)(wires=[0])

    Moreover, ``qml.simplify`` can be used to simplify QNodes or quantum functions:

    >>> dev = qml.device("default.qubit", wires=2)
    >>> @qml.qnode(dev)
    ... @qml.simplify
    ... def circuit():
    ...     qml.adjoint(qml.prod(qml.RX(1, 0) ** 1, qml.RY(1, 0), qml.RZ(1, 0)))
    ...     return qml.probs(wires=0)
    >>> circuit()
    tensor([0.64596329, 0.35403671], requires_grad=True)
    >>> tape = qml.workflow.construct_tape(circuit)()
    >>> list(tape)
    [RZ(11.566370614359172, wires=[0]) @ RY(11.566370614359172, wires=[0]) @ RX(11.566370614359172, wires=[0]),
     probs(wires=[0])]
    """
    if isinstance(input, (Operator, MeasurementProcess)):
        if QueuingManager.recording():
            with QueuingManager.stop_recording():
                new_op = copy(input.simplify())
            QueuingManager.remove(input)
            return qml.apply(new_op)
        return input.simplify()

    if isinstance(input, QuantumScript) or callable(input):
        return _simplify_transform(input)

    raise ValueError(f"Cannot simplify the object {input} of type {type(input)}.")



@qml.transform
def _simplify_transform(tape: QuantumScript) -> tuple[QuantumScriptBatch, PostprocessingFn]:
    with qml.QueuingManager.stop_recording():
        new_operations = [op.simplify() for op in tape.operations]
        new_measurements = [m.simplify() for m in tape.measurements]

    new_tape = tape.copy(operations=new_operations, measurements=new_measurements)

    def null_processing_fn(res):
        return res[0]

    return [new_tape], null_processing_fn
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