SNOBFIT¶

class SNOBFIT(maxiter=1000, maxfail=10, maxmp=None, verbose=False)[source]¶

Bases: qiskit.algorithms.optimizers.optimizer.Optimizer

Stable Noisy Optimization by Branch and FIT algorithm.

SnobFit is used for the optimization of derivative-free, noisy objective functions providing robust and fast solutions of problems with continuous variables varying within bound.

Uses skquant.opt installed with pip install scikit-quant. For further detail, please refer to https://github.com/scikit-quant/scikit-quant and https://qat4chem.lbl.gov/software.

Parameters

maxiter (int) – Maximum number of function evaluations.
maxmp (Optional[int]) – Maximum number of model points requested for the local fit. Default = 2 * number of parameters + 6 set to this value when None.
maxfail (int) – Maximum number of failures to improve the solution. Stops the algorithm after maxfail is reached.
verbose (bool) – Provide verbose (debugging) output.

Raises

MissingOptionalLibraryError – scikit-quant or SQSnobFit not installed
QiskitError – If NumPy 1.24.0 or above is installed. See https://github.com/scikit-quant/scikit-quant/issues/24 for more details.

Methods

`get_support_level`	Returns support level dictionary.
`gradient_num_diff`	We compute the gradient with the numeric differentiation in the parallel way, around the point x_center.
`minimize`	Minimize the scalar function.
`print_options`	Print algorithm-specific options.
`set_max_evals_grouped`	Set max evals grouped
`set_options`	Sets or updates values in the options dictionary.
`wrap_function`	Wrap the function to implicitly inject the args at the call of the function.

Attributes

bounds_support_level¶: Returns bounds support level

gradient_support_level¶: Returns gradient support level

initial_point_support_level¶: Returns initial point support level

is_bounds_ignored¶: Returns is bounds ignored

is_bounds_required¶: Returns is bounds required

is_bounds_supported¶: Returns is bounds supported

is_gradient_ignored¶: Returns is gradient ignored

is_gradient_required¶: Returns is gradient required

is_gradient_supported¶: Returns is gradient supported

is_initial_point_ignored¶: Returns is initial point ignored

is_initial_point_required¶: Returns is initial point required

is_initial_point_supported¶: Returns is initial point supported

setting¶: Return setting

settings¶

Return type: Dict[str, Any]