NFT

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

Bases: 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 (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.

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

Methods

get_support_level()

Return support level dictionary

static gradient_num_diff(x_center, f, epsilon, max_evals_grouped=None)

We compute the gradient with the numeric differentiation in the parallel way, around the point x_center.

Parameters:

• x_center (ndarray) – point around which we compute the gradient

• f (func) – the function of which the gradient is to be computed.

• epsilon (float) – the epsilon used in the numeric differentiation.

• max_evals_grouped (int) – max evals grouped, defaults to 1 (i.e. no batching).

Returns:

the gradient computed

Return type:

grad

minimize(fun, x0, jac=None, bounds=None)

Minimize the scalar function.

Parameters:

• fun (Callable[[float | ndarray], float]) – The scalar function to minimize.

• x0 (float | ndarray) – The initial point for the minimization.

• jac (Callable[[float | ndarray], float | ndarray] | None) – The gradient of the scalar function fun.

• bounds (list[tuple[float, float]] | None) – Bounds for the variables of fun. This argument might be ignored if the optimizer does not support bounds.

Returns:

The result of the optimization, containing e.g. the result as attribute x.

Return type:

OptimizerResult

print_options()

Print algorithm-specific options.

set_max_evals_grouped(limit)

Set max evals grouped

set_options(**kwargs)

Sets or updates values in the options dictionary.

The options dictionary may be used internally by a given optimizer to pass additional optional values for the underlying optimizer/optimization function used. The options dictionary may be initially populated with a set of key/values when the given optimizer is constructed.

Parameters:

kwargs (dict) – options, given as name=value.

static wrap_function(function, args)

Wrap the function to implicitly inject the args at the call of the function.

Parameters:

• function (func) – the target function

• args (tuple) – the args to be injected

Returns:

wrapper

Return type:

function_wrapper