NFT

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

Bases: qiskit.algorithms.optimizers.scipy_optimizer.SciPyOptimizer

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.

• options (Optional[dict]) – 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.
optimize	Perform optimization.
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]