qml.batch_input

batch_input(tape, argnum)

Transform a circuit to support an initial batch dimension for gate inputs.

In a classical ML application one needs to batch the non-trainable inputs of the network. This function executes the same analogue for a quantum circuit: separate circuit executions are created for each input, which are then executed with the same trainable parameters.

The batch dimension is assumed to be the first rank of the non trainable tensor object. For a rank 1 feature space, the shape needs to be (Nt, x) where x indicates the dimension of the features and Nt being the number of examples within a batch. Based on arXiv:2202.10471.

Parameters

• tape (QNode or QuantumTape or Callable) – Input quantum circuit to batch

• argnum (Sequence[int] or int) – One or several index values indicating the position of the non-trainable batched parameters in the quantum tape.

Returns

The transformed circuit as described in qml.transform. Executing this circuit will provide the batched results.

Return type

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

See also

batch_params()

Example

from functools import partial
dev = qml.device("default.qubit", wires=2, shots=None)

@partial(qml.batch_input, argnum=1)
@qml.qnode(dev, diff_method="parameter-shift", interface="tf")
def circuit(inputs, weights):
    qml.RY(weights[0], wires=0)
    qml.AngleEmbedding(inputs, wires=range(2), rotation="Y")
    qml.RY(weights[1], wires=1)
    return qml.expval(qml.Z(1))

>>> x = tf.random.uniform((10, 2), 0, 1)
>>> w = tf.random.uniform((2,), 0, 1)
>>> circuit(x, w)
<tf.Tensor: shape=(10,), dtype=float64, numpy=
array([0.46230079, 0.73971315, 0.95666004, 0.5355225 , 0.66180948,
        0.44519553, 0.93874261, 0.9483197 , 0.78737918, 0.90866411])>

code/api/pennylane.batch_input

 

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