qml.gradients.batch_jvp

batch_jvp(tapes, tangents, gradient_fn, reduction='append', gradient_kwargs=None)

Generate the gradient tapes and processing function required to compute the Jacobian vector products of a batch of tapes.

Parameters

• tapes (Sequence[QuantumTape]) – sequence of quantum tapes to differentiate

• tangents (Sequence[tensor_like]) – Sequence of gradient-output vectors dy. Must be the same length as tapes. Each dy tensor should have shape matching the output shape of the corresponding tape.

• gradient_fn (callable) – the gradient transform to use to differentiate the tapes

• reduction (str) – Determines how the Jacobian-vector products are returned. If append, then the output of the function will be of the form List[tensor_like], with each element corresponding to the JVP of each input tape. If extend, then the output JVPs will be concatenated.

• gradient_kwargs (dict) – dictionary of keyword arguments to pass when determining the gradients of tapes

Returns

list of Jacobian vector products. None elements corresponds to tapes with no trainable parameters.

Return type

List[tensor_like or None]

Example

import jax
x = jax.numpy.array([[0.1, 0.2, 0.3],
                     [0.4, 0.5, 0.6]])

ops = [
    qml.RX(x[0, 0], wires=0),
    qml.RY(x[0, 1], wires=1),
    qml.RZ(x[0, 2], wires=0),
    qml.CNOT(wires=[0, 1]),
    qml.RX(x[1, 0], wires=1),
    qml.RY(x[1, 1], wires=0),
    qml.RZ(x[1, 2], wires=1)
]
measurements1 = [qml.expval(qml.Z(0)), qml.probs(wires=1)]
tape1 = qml.tape.QuantumTape(ops, measurements1)

measurements2 = [qml.expval(qml.Z(0) @ qml.Z(1))]
tape2 = qml.tape.QuantumTape(ops, measurements2)

tapes = [tape1, tape2]

Both tapes share the same circuit ansatz, but have different measurement outputs.

We can use the batch_jvp function to compute the Jacobian vector product, given a list of tangents tangent:

>>> tangent_0 = [jax.numpy.array(1.0), jax.numpy.array(1.0), jax.numpy.array(1.0), jax.numpy.array(1.0), jax.numpy.array(1.0), jax.numpy.array(1.0)]
>>> tangent_1 = [jax.numpy.array(1.0), jax.numpy.array(1.0), jax.numpy.array(1.0), jax.numpy.array(1.0), jax.numpy.array(1.0), jax.numpy.array(1.0)]
>>> tangents = [tangent_0, tangent_1]

Note that each tangents has shape matching the parameter dimension of the tape.

Executing the JVP tapes, and applying the processing function:

>>> jvp_tapes, fn = qml.gradients.batch_jvp(tapes, tangents, qml.gradients.param_shift)

>>> dev = qml.device("default.qubit", wires=2)
>>> jvps = fn(dev.execute(jvp_tapes))
>>> jvps
((Array(-0.62073976, dtype=float32), Array([-0.3259707 ,  0.32597077], dtype=float32)), Array(-0.6900841, dtype=float32))

We have two JVPs; one per tape. Each one corresponds to the shape of the output of their respective tape.

code/api/pennylane.gradients.batch_jvp

 

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