constant_pwc

Graph.constant_pwc(constant, duration, batch_dimension_count=0, *, name=None)

Create a piecewise-constant function of time that is constant over a specified duration.

Parameters

• constant (number or np.ndarray or Tensor) – The value $c$ of the function on the constant segment. To create a batch of $B_1 \times \ldots \times B_n$ piecewise-constant functions of shape $D_1 \times \ldots \times D_m$, provide this constant parameter as an object of shape $B_1\times\ldots\times B_n\times D_1\times\ldots\times D_m$.
• duration (float) – The duration $\tau$ for the resulting piecewise-constant function.
• batch_dimension_count (int , optional) – The number of batch dimensions, $n$ in constant. If provided, the first $n$ dimensions of constant are considered batch dimensions. Defaults to 0, which corresponds to no batch.
• name (str or None , optional) – The name of the node.

Returns

The constant function $f(t) = c$ (for $0\leq t\leq\tau$) (or a batch of constant functions, if you provide batch_dimension_count).

Return type

Pwc

SEE ALSO

Graph.constant_pwc_operator : Create constant Pwc operators.

Graph.constant_stf : Corresponding operation for Stfs.

Graph.pwc : Create piecewise-constant functions.

Notes

For more information on Pwc nodes see the Working with time-dependent functions in Boulder Opal topic.

Examples

Create a batched piecewise-constant function.

>>> constant = np.arange(12).reshape((2, 2, 3))
>>> graph.constant_pwc(
...     constant=constant, duration=0.1, batch_dimension_count=1, name="constant"
... )
<Pwc: name="constant", operation_name="constant_pwc", value_shape=(2, 3), batch_shape=(2,)>
>>> result = bo.execute_graph(graph=graph, output_node_names="constant")
>>> result["output"]["constant"]
{
    'durations': array([0.1]),
    'values': array([
        [[[ 0.,  1.,  2.], [ 3.,  4.,  5.]]],
        [[[ 6.,  7.,  8.], [ 9., 10., 11.]]]
    ]),
    'time_dimension': 1
}

See more examples in the Simulate the dynamics of a single qubit using computational graphs tutorial.