Source code for pennylane.transforms.optimization.remove_barrier
# 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.
"""Transform for removing the Barrier gate from quantum circuits."""
# pylint: disable=too-many-branches
from pennylane.tape import QuantumScript, QuantumScriptBatch
from pennylane.transforms import transform
from pennylane.typing import PostprocessingFn
[docs]@transform
def remove_barrier(tape: QuantumScript) -> tuple[QuantumScriptBatch, PostprocessingFn]:
"""Quantum transform to remove Barrier gates.
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**
The transform can be applied on :class:`QNode` directly.
.. code-block:: python
@remove_barrier
@qml.qnode(device=dev)
def circuit(x, y):
qml.Hadamard(wires=0)
qml.Hadamard(wires=1)
qml.Barrier(wires=[0,1])
qml.X(0)
return qml.expval(qml.Z(0))
The barrier is then removed before execution.
.. details::
:title: Usage Details
Consider the following quantum function:
.. code-block:: python
def qfunc(x, y):
qml.Hadamard(wires=0)
qml.Hadamard(wires=1)
qml.Barrier(wires=[0,1])
qml.X(0)
return qml.expval(qml.Z(0))
The circuit before optimization:
>>> dev = qml.device('default.qubit', wires=2)
>>> qnode = qml.QNode(qfunc, dev)
>>> print(qml.draw(qnode)(1, 2))
0: ──H──╭||──X──┤ ⟨Z⟩
1: ──H──╰||─────┤
We can remove the Barrier by running the ``remove_barrier`` transform:
>>> optimized_qfunc = remove_barrier(qfunc)
>>> optimized_qnode = qml.QNode(optimized_qfunc, dev)
>>> print(qml.draw(optimized_qnode)(1, 2))
0: ──H──X──┤ ⟨Z⟩
1: ──H─────┤
"""
operations = filter(lambda op: op.name != "Barrier", tape.operations)
new_tape = type(tape)(operations, tape.measurements, shots=tape.shots)
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] # pragma: no cover
return [new_tape], null_postprocessing
_modules/pennylane/transforms/optimization/remove_barrier
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