qml.change_op_basis¶

change_op_basis(compute_op, target_op, uncompute_op=None)[source]¶

Construct an operator that represents the product of the operators provided; particularly a compute-uncompute pattern.

Parameters:

compute_op (Operator) – A single operator or product that applies quantum operations.
target_op (Operator) – A single operator or a product that applies quantum operations.
uncompute_op (None | Operator) – An optional single operator or a product that applies quantum operations. None corresponds to uncompute_op=qml.adjoint(compute_op).

Returns:

the operator representing the compute-uncompute pattern.

Return type:

ChangeOpBasis

Example

Consider the following example involving a ChangeOpBasis. The compute, uncompute pattern is composed of a Quantum Fourier Transform (QFT), followed by a PhaseAdder, and finally an inverse QFT.

import pennylane as qml
from functools import partial

qml.decomposition.enable_graph()

dev = qml.device("default.qubit")
@qml.qnode(dev)
def circuit():
    qml.H(0)
    qml.CNOT([1,2])
    qml.ctrl(
        qml.change_op_basis(qml.QFT([1,2]), qml.PhaseAdder(1, x_wires=[1,2])),
        control=0
    )
    return qml.state()

circuit2 = qml.transforms.decompose(circuit, max_expansion=1)

When this circuit is decomposed, the compute_op and uncompute_op are not controlled, resulting in a much more resource-efficient decomposition:

>>> print(qml.draw(circuit2)())
──H──────╭●────────────────┤  State
─╭●─╭QFT─├PhaseAdder─╭QFT†─┤  State
─╰X─╰QFT─╰PhaseAdder─╰QFT†─┤  State

qml.change_op_basis¶

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