paraqeet.signal.generator.Generator

class paraqeet.signal.generator.Generator

Bases: Optimizable, Differentiable

Signal generation stack.

Contrary to most quantum simulators, paraqeet includes a detailed simulation of the control stack. Each component in the stack and its functions are simulated individually and combined here.

Example: A local oscillator and arbitrary waveform generator signal are put through via a mixer device to produce an effective modulated signal.

__init__()

Methods

__init__()
get_gradient_at_timestep(time)	Return array with the gradient of the signal value for one time step.
get_parameters()	Return all parameters of this class that can be optimized.
get_value(times)	Return array with scalar signal value for each time step.
get_value_and_gradient(times)	Calculate the gradient of the model.
set_all_optimizable_parameters(all_params)	Set all optimizable parameters in the optimization.
set_optimizable_parameters(params)	Set which parameters associated with the object shall be considered during optimization.

Attributes

all_optimizable_parameters	Get the optimizable parameters
name	Get the name of the parameter.
optimizable_parameters	Get the optimizable parameters

property all_optimizable_parameters: list[Quantity]

Get the optimizable parameters

Returns:

The list of all the optimizable parameters considered in the optimization

Return type:

list[Quantity]

abstract get_gradient_at_timestep(time)

Return array with the gradient of the signal value for one time step.

The result has the shape (p,) where ‘p’ is the parameter index.

Parameters:

time (Array) – One time stamp.

Return type:

Array

abstract get_parameters()

Return all parameters of this class that can be optimized.

Raises:

NotImplementedError – Subclasses derived from this class must implement this method.

Return type:

list[Quantity]

abstract get_value(times)

Return array with scalar signal value for each time step.

Parameters:

times (Array) – One-dimensional vector of timestamps.

Returns:

Returns the scalar signal vector.

Return type:

Array

abstract get_value_and_gradient(times)

Calculate the gradient of the model.

Returns:

The value and the gradient of the model.

Return type:

tuple[Array, Array] | tuple[float, Array]

Parameters:

times (Array)

property name: str | None

Get the name of the parameter.

Returns:

Name of the parameter.

Return type:

str | None

property optimizable_parameters: list[Quantity]

Get the optimizable parameters

Returns:

The list of optimizable parameters associated with the object.

Return type:

list[Quantity]

set_all_optimizable_parameters(all_params)

Set all optimizable parameters in the optimization.

Parameters:

• params (List[Quantity]) – List of optimizable parameters to be set.

• all_params (list[Quantity])

Return type:

None

set_optimizable_parameters(params)

Set which parameters associated with the object shall be considered during optimization.

All quantities that are not in the response of get_parameters will be filtered out. This function is called by the optimizer before gradient based optimization to tell the layers which gradients to compute.

Parameters:

params (list[Quantity]) – List of optimizable parameters to be set.

Return type:

None