paraqeet.optimizers.scipy_optimizer.ScipyOptimizer#

class paraqeet.optimizers.scipy_optimizer.ScipyOptimizer(measure, optimization_map)[source]#

Bases: Optimizer

Minimize the outcome of a measuremnt with the scipy optimization package.

Parameters:

measure (Measurement) – Implementation of the Measurement class that measures the observable to be maximised.
optimization_map (OptimizationMap) – An optimization map containing all parameters that can be optimized.

__init__(measure, optimization_map)[source]#

Parameters:

measure (NormalizableMeasurement)
optimization_map (OptimizationMap)

Return type:

None

Methods

`__init__`(measure, optimization_map)
`optimize`(times)	Optimize the system via the Scipy optimizer.
`set_options`(opts)	Set the options for the system.
`update_option`(key, val)	Updates one option for the system.

Attributes

`callback`	Returns the callback function.
`logger`	Returns the current logger that is being used by this optimizer, or None if no logger was set yet.
`method`	Returns the currently selected optimization method.
`optimization_map`	Return the optimization map that this optimizer uses.

property callback: Callable | None#: Returns the callback function.

property logger: Logger | None#: Returns the current logger that is being used by this optimizer, or None if no logger was set yet.

property method: str#: Returns the currently selected optimization method.

property optimization_map: OptimizationMap#

Return the optimization map that this optimizer uses.

Parameters that can be optimized need to be added to this map.

Returns:: Returns the optimization map that this optimizer uses.
Return type:: paraqeet.optimization_map

optimize(times)[source]#

Optimize the system via the Scipy optimizer.

Performs the actual optimization.

Since the search parameters are dimensionless and bound by [-1, 1], we set the bounds of the scipy minimize module to -1, and 1 explicitly in each search dimension.

Note - If input `times` is a float, then the start time of propagation is implicitly assumed to be zero. For an array of times, the first time point is the start time.

Returns:: The result of the optimization.
Return type:: OptimizationResult
Parameters:: times (Array | float)

set_options(opts)[source]#

Set the options for the system.

Parameters:: opts (dict)

update_option(key, val)[source]#: Updates one option for the system.