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qml.batch_params

batch_params(tape, all_operations=False)[source]

Transform a QNode to support an initial batch dimension for operation parameters.

Note

This transform will create multiple circuits inside the QNode, one per batch dimension. As a result, it is both simulator and hardware compatible. When using a simulator device, however, this means that a separate simulation will be performed per batch dimension.

Warning

Currently, not all templates have been updated to support a batch dimension. If you run into an error attempting to use a template with this transform, please open a GitHub issue detailing the error.

Parameters:

  • tape (QNode or QuantumTape or Callable) – a quantum circuit to add a batch dimension to

  • all_operations (bool) – If True, a batch dimension will be added to all operations in the QNode, rather than just trainable QNode parameters.

Returns:

The transformed circuit as described in qml.transform. Executing this circuit will provide the batched results, with the first dimension treated as the batch dimension.

Return type:

qnode (QNode) or quantum function (Callable) or tuple[List[QuantumTape], function]

Example

Consider the following circuit:

dev = qml.device("default.qubit", wires=3)

@qml.batch_params
@qml.qnode(dev)
def circuit(x, weights):
    qml.RX(x, wires=0)
    qml.RY(0.2, wires=1)
    qml.templates.StronglyEntanglingLayers(weights, wires=[0, 1, 2])
    return qml.expval(qml.Hadamard(0))

The qml.batch_params decorator allows us to pass arguments x and weights that have a batch dimension. For example,

>>> batch_size = 3
>>> x = pnp.linspace(0.1, 0.5, batch_size)
>>> rng = np.random.default_rng(seed=1234)
>>> weights = pnp.array(rng.random((batch_size, 10, 3, 3)))

If we evaluate the QNode with these inputs, we will get an output of shape (batch_size,):

>>> circuit(x, weights)
tensor([ 0.008...,  0.273..., -0.24...], requires_grad=True)

QNodes with a batch dimension remain fully differentiable:

>>> def cost_fn(x, weights): return qml.math.sum(circuit(x, weights))
>>> cost_fn(x, weights)
tensor(0.037..., requires_grad=True)
>>> qml.grad(cost_fn)(x, weights)[0]
array([-0.302...,  0.0632...  0.0081...])

If we pass the all_operations argument, we can specify that all operation parameters in the transformed QNode, regardless of whether they are QNode input parameters, have a batch dimension:

@qml.batch_params(all_operations=True)
@qml.qnode(dev)
def circuit(x, weights):
    qml.RX(x, wires=0)
    qml.RY([0.2, 0.2, 0.2], wires=1)
    qml.templates.StronglyEntanglingLayers(weights, wires=[0, 1, 2])
    return qml.expval(qml.Hadamard(0))

>>> def cost_fn(x, weights): return qml.math.sum(circuit(x, weights))
>>> weights.requires_grad = False
>>> cost_fn(x, weights)
tensor(0.037..., requires_grad=True)
>>> qml.grad(cost_fn)(x, weights)[0]
np.float64(-0.302...)
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