gaussian_pulse_stf

signals.gaussian_pulse_stf(amplitude, width, center_time, drag=None)

Create an Stf representing a Gaussian pulse.

Parameters

  • amplitude (float or complex or Tensor) – The amplitude of the Gaussian pulse, AA
  • width (float or Tensor) – The standard deviation of the Gaussian pulse, σ\sigma
  • center_time (float or Tensor) – The center of the Gaussian pulse, t0t_0
  • drag (float or Tensor or None , optional) – The DRAG parameter, β\beta

Returns

The sampleable Gaussian pulse.

Return type

Stf

SEE ALSO

Graph.signals.gaussian_pulse_pwc : Corresponding operation with Pwc output.

Graph.signals.sech_pulse_stf : Create an Stf representing a hyperbolic secant pulse.

Notes

The Gaussian pulse is defined as

Gaussian(t)=A(1−iβ(t−t0)σ2)exp⁡(−(t−t0)22σ2). \mathop{\mathrm{Gaussian}}(t) = A \left(1-\frac{i\beta (t-t_0)}{\sigma^2}\right) \exp \left(- \frac{(t-t_0)^2}{2\sigma^2} \right) .

Examples

Define a sampleable Gaussian pulse.

>>> gaussian = graph.signals.gaussian_pulse_stf(
...     amplitude=1.0, width=0.1, center_time=0.5
... )
>>> graph.discretize_stf(
...     gaussian, duration=1, segment_count=5, name="gaussian"
... )
<Pwc: name="gaussian", operation_name="discretize_stf", value_shape=(), batch_shape=()>
>>> result = bo.execute_graph(graph=graph, output_node_names="gaussian")
>>> result["output"]["gaussian"]
{'durations': array([0.2, 0.2, 0.2, 0.2, 0.2]),
'values': array([3.35462628e-04, 1.35335283e-01, 1.00000000e+00, 1.35335283e-01,
        3.35462628e-04]),
'time_dimension': 0}

Define a sampleable Gaussian with a DRAG correction.

>>> drag_gaussian = graph.signals.gaussian_pulse_stf(
...     amplitude=1.0, width=0.1, center_time=0.5, drag=0.2
... )
>>> graph.discretize_stf(
...     drag_gaussian, duration=1, segment_count=5, name="drag_gaussian"
... )
<Pwc: name="drag_gaussian", operation_name="discretize_stf", value_shape=(), batch_shape=()>
>>> result = bo.execute_graph(graph=graph, output_node_names="drag_gaussian")
>>> result["output"]["drag_gaussian"]
{'durations': array([0.2, 0.2, 0.2, 0.2, 0.2]),
'values': array([3.35462628e-04+0.0026837j , 1.35335283e-01+0.54134113j,
        1.00000000e+00+0.j        , 1.35335283e-01-0.54134113j,
        3.35462628e-04-0.0026837j ]),
'time_dimension': 0}

Define a sampleable Gaussian pulse with optimizable parameters.

>>> amplitude = graph.optimizable_scalar(
...     lower_bound=0, upper_bound=2.*np.pi, name="amplitude"
... )
>>> width = graph.optimizable_scalar(
...     lower_bound=0.1, upper_bound=0.5, name="width"
... )
>>> center_time = graph.optimizable_scalar(
...     lower_bound=0.2, upper_bound=0.8, name="center_time"
... )
>>> drag = graph.optimizable_scalar(
...     lower_bound=0, upper_bound=0.5, name="drag"
... )
>>> graph.signals.gaussian_pulse_stf(
...     amplitude=amplitude, width=width, center_time=center_time, drag=drag
... )
<Stf: operation_name="multiply", value_shape=(), batch_shape=()>

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