Source code for pennylane.transforms.optimization.undo_swaps

# Copyright 2018-2021 Xanadu Quantum Technologies Inc.

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

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"""Transform that eliminates the swap operators by reordering the wires."""
# pylint: disable=too-many-branches
from typing import Sequence, Callable

from pennylane.transforms import transform

from pennylane.tape import QuantumTape


def null_postprocessing(results):
    """A postprocesing function returned by a transform that only converts the batch of results
    into a result for a single ``QuantumTape``.
    """
    return results[0]


[docs]@transform def undo_swaps(tape: QuantumTape) -> (Sequence[QuantumTape], Callable): """Quantum function transform to remove SWAP gates by running from right to left through the circuit changing the position of the qubits accordingly. Args: tape (QNode or QuantumTape or Callable): A quantum circuit. Returns: qnode (QNode) or quantum function (Callable) or tuple[List[QuantumTape], function]: The transformed circuit as described in :func:`qml.transform <pennylane.transform>`. **Example** >>> dev = qml.device('default.qubit', wires=3) You can apply the transform directly on a :class:`QNode` .. code-block:: python @undo_swaps @qml.qnode(device=dev) def circuit(): qml.Hadamard(wires=0) qml.X(1) qml.SWAP(wires=[0,1]) qml.SWAP(wires=[0,2]) qml.Y(0) return qml.expval(qml.Z(0)) The SWAP gates are removed before execution. .. details:: :title: Usage Details Consider the following quantum function: .. code-block:: python def qfunc(): qml.Hadamard(wires=0) qml.X(1) qml.SWAP(wires=[0,1]) qml.SWAP(wires=[0,2]) qml.Y(0) return qml.expval(qml.Z(0)) The circuit before optimization: >>> dev = qml.device('default.qubit', wires=3) >>> qnode = qml.QNode(qfunc, dev) >>> print(qml.draw(qnode)()) 0: ──H──╭SWAP──╭SWAP──Y──┤ ⟨Z⟩ 1: ──X──╰SWAP──│─────────┤ 2: ────────────╰SWAP─────┤ We can remove the SWAP gates by running the ``undo_swap`` transform: >>> optimized_qfunc = undo_swaps(qfunc) >>> optimized_qnode = qml.QNode(optimized_qfunc, dev) >>> print(qml.draw(optimized_qnode)()) 0: ──Y──┤ ⟨Z⟩ 1: ──H──┤ 2: ──X──┤ """ wire_map = {wire: wire for wire in tape.wires} gates = [] for current_gate in reversed(tape.operations): if current_gate.name == "SWAP": swap_wires_0, swap_wires_1 = current_gate.wires wire_map[swap_wires_0], wire_map[swap_wires_1] = ( wire_map[swap_wires_1], wire_map[swap_wires_0], ) else: gates.append(current_gate.map_wires(wire_map)) gates.reverse() new_tape = type(tape)( gates, tape.measurements, shots=tape.shots, trainable_params=tape.trainable_params ) return [new_tape], null_postprocessing