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qml.workflow.construct_batch

construct_batch(qnode, level='user')[source]

Construct the batch of tapes and post processing for a designated stage in the transform program.

Warning

Using level=None is deprecated and will be removed in a future release. Please use level='device' to include all transforms.

Parameters:

  • qnode (QNode) – the qnode we want to get the tapes and post-processing for.

  • level (None, str, int, slice) – An indication of what transforms to apply before drawing. Check get_transform_program() for more information on the allowed values and usage details of this argument.

Returns:

A function with the same call signature as the initial quantum function. This function returns a batch (tuple) of tapes and postprocessing function.

Return type:

Callable

See also

pennylane.workflow.get_transform_program() to inspect the contents of the transform program for a specified level.

Suppose we have a QNode with several user transforms.

from pennylane.workflow import construct_batch

@qml.transforms.undo_swaps
@qml.transforms.merge_rotations
@qml.transforms.cancel_inverses
@qml.qnode(qml.device('default.qubit'), diff_method="parameter-shift", gradient_kwargs = {"shifts": np.pi/4})
def circuit(x):
    qml.RandomLayers(qml.numpy.array([[1.0, 2.0]]), wires=(0,1))
    qml.RX(x, wires=0)
    qml.RX(-x, wires=0)
    qml.SWAP((0,1))
    qml.X(0)
    qml.X(0)
    return qml.expval(qml.X(0) + qml.Y(0))

We can inspect what the device will execute with:

>>> batch, fn = construct_batch(circuit, level="device")(1.23)
>>> batch[0].circuit
[RY(1.0, wires=[1]),
 RX(2.0, wires=[0]),
 expval(X(0) + Y(0))]

These tapes can be natively executed by the device. However, with non-backprop devices the parameters will need to be converted to NumPy with convert_to_numpy_parameters().

>>> fn(dev.execute(batch))
(np.float64(-0.9092974268256817),)

Or what the parameter shift gradient transform will be applied to:

>>> batch, fn = construct_batch(circuit, level="gradient")(1.23)
>>> batch[0].circuit
[RY(tensor(1., requires_grad=True), wires=[1]),
 RX(tensor(2., requires_grad=True), wires=[0]),
 expval(X(0) + Y(0))]

We can inspect what was directly captured from the qfunc with level=0.

>>> batch, fn = construct_batch(circuit, level=0)(1.23)
>>> batch[0].circuit
[RandomLayers(tensor([[1., 2.]], requires_grad=True), wires=[0, 1]),
 RX(1.23, wires=[0]),
 RX(-1.23, wires=[0]),
 SWAP(wires=[0, 1]),
 X(0),
 X(0),
 expval(X(0) + Y(0))]

And iterate though stages in the transform program with different integers. If we request level=1, the cancel_inverses transform has been applied.

>>> batch, fn = construct_batch(circuit, level=1)(1.23)
>>> batch[0].circuit
[RandomLayers(tensor([[1., 2.]], requires_grad=True), wires=[0, 1]),
 RX(1.23, wires=[0]),
 RX(-1.23, wires=[0]),
 SWAP(wires=[0, 1]),
 expval(X(0) + Y(0))]

We can also slice into a subset of the transform program. slice(1, None) would skip the first user transform cancel_inverses:

>>> batch, fn = construct_batch(circuit, level=slice(1,None))(1.23)
>>> batch[0].circuit
[RY(tensor(1., requires_grad=True), wires=[1]),
 RX(tensor(2., requires_grad=True), wires=[0]),
 X(0),
 X(0),
 expval(X(0) + Y(0))]
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