qml.transforms.merge_amplitude_embedding¶

merge_amplitude_embedding(tape)[source]¶

Quantum function transform to combine amplitude embedding templates that act on different qubits.

Parameters:: tape (QNode or QuantumTape or Callable) – A quantum circuit.
Returns:: The transformed circuit as described in qml.transform.
Return type:: qnode (QNode) or quantum function (Callable) or tuple[List[.QuantumTape], function]

Example

>>> dev = qml.device('default.qubit', wires=4)

You can apply the transform directly on QNode:

@qml.transforms.merge_amplitude_embedding
@qml.qnode(device=dev)
def circuit():
    qml.CNOT(wires = [0,1])
    qml.AmplitudeEmbedding([0,1], wires = 2)
    qml.AmplitudeEmbedding([0,1], wires = 3)
    return qml.state()

>>> circuit()
array([0.+0.j, 0.+0.j, 0.+0.j, 1.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j,
       0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j])

Usage Details

You can also apply it on quantum function.

def qfunc():
    qml.CNOT(wires = [0,1])
    qml.AmplitudeEmbedding([0,1], wires = 2)
    qml.AmplitudeEmbedding([0,1], wires = 3)
    return qml.state()

The circuit before compilation will not work because of using two amplitude embedding.

Using the transformation we can join the different amplitude embedding into a single one:

>>> optimized_qfunc = qml.transforms.merge_amplitude_embedding(qfunc)
>>> optimized_qnode = qml.QNode(optimized_qfunc, dev)
>>> print(qml.draw(optimized_qnode)())
0: ─╭●───┤  State
1: ─╰X───┤  State
2: ─╭|Ψ⟩─┤  State
3: ─╰|Ψ⟩─┤  State

code/api/pennylane.transforms.merge_amplitude_embedding

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qml.transforms.merge_amplitude_embedding¶

Usage Details

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