NFT¶

class NFT(maxiter=None, maxfev=1024, disp=False, reset_interval=32, options=None, **kwargs)[código fonte]¶

Nakanishi-Fujii-Todo algorithm.

Parâmetros

maxiter (int | None) – Maximum number of iterations to perform.
maxfev (int) – Maximum number of function evaluations to perform.
disp (bool) – disp
reset_interval (int) – The minimum estimates directly once in reset_interval times.
options (dict | None) – A dictionary of solver options.
kwargs – additional kwargs for scipy.optimize.minimize.

Notes

In this optimization method, the optimization function have to satisfy three conditions written in 1.

References

1: K. M. Nakanishi, K. Fujii, and S. Todo. 2019. Sequential minimal optimization for quantum-classical hybrid algorithms. arXiv preprint arXiv:1903.12166.

Methods

`get_support_level`	Return 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