NFT

class NFT(maxiter=None, maxfev=1024, disp=False, reset_interval=32)

Nakanishi-Fujii-Todo algorithm.

See https://arxiv.org/abs/1903.12166

Built out using scipy framework, for details, please refer to https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html.

Parameters

• maxiter (Optional[int]) – 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.

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.

Attributes

NFT.bounds_support_level	Returns bounds support level
NFT.gradient_support_level	Returns gradient support level
NFT.initial_point_support_level	Returns initial point support level
NFT.is_bounds_ignored	Returns is bounds ignored
NFT.is_bounds_required	Returns is bounds required
NFT.is_bounds_supported	Returns is bounds supported
NFT.is_gradient_ignored	Returns is gradient ignored
NFT.is_gradient_required	Returns is gradient required
NFT.is_gradient_supported	Returns is gradient supported
NFT.is_initial_point_ignored	Returns is initial point ignored
NFT.is_initial_point_required	Returns is initial point required
NFT.is_initial_point_supported	Returns is initial point supported
NFT.setting	Return setting

Methods

NFT.get_support_level()	return support level dictionary
NFT.gradient_num_diff(x_center, f, epsilon)	We compute the gradient with the numeric differentiation in the parallel way, around the point x_center.
NFT.optimize(num_vars, objective_function[, …])	Perform optimization.
NFT.print_options()	Print algorithm-specific options.
NFT.set_max_evals_grouped(limit)	Set max evals grouped
NFT.set_options(**kwargs)	Sets or updates values in the options dictionary.
NFT.wrap_function(function, args)	Wrap the function to implicitly inject the args at the call of the function.