BayesianOptimizer#

class BayesianOptimizer(measure_func, optimization_map, initial_samples=10, iterations=100)[source]#

Bases: Optimizer

Minimize the outcome of a measurement using Bayesian optimization [13].

This is useful if the evaluation of the measurement is costly. This class is mostly a wrapper around the implementing package, see BayesianOptimization.

__init__(measure_func, optimization_map, initial_samples=10, iterations=100)[source]#

Parameters:

measure_func (Callable[[Array], Float]) – Function implementing measurement of observables to be minimized.
optimization_map (OptimizationMap) – All optimizable parameters via the optimization map.
initial_samples (int) – Number of initial samples explored before the search for the maximum starts.
iterations (int) – Number of iterations where the method attempts to find the maximum value.

Methods

`__init__`(measure_func, optimization_map[, ...])
`optimize`(times)	Optimize the system via the Bayesian optimizer.

Attributes

`initial_samples`	Get the initial samples fed to the system.
`iterations`	Get the iterations of the system.
`logger`	Returns the current logger that is being used by this optimizer, or None if no logger was set yet.
`optimization_map`	Return the optimization map that this optimizer uses.

property initial_samples: int#: Get the initial samples fed to the system.

property iterations: int#: Get the iterations of the system.

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

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:: The optimization map that this optimizer uses.

optimize(times)[source]#

Optimize the system via the Bayesian optimizer.

Performs the actual optimization.

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.

Parameters:: times (Array | float) – Array of times or a float (assumed start time zero).
Returns:: Result of optimization (status, value, iterations, and the raw result).
Return type:: OptimizationResult