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NFT

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

GitHub

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

get_support_level()

Return support level dictionary

gradient_num_diff

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

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

Minimize the scalar function.

Parameters

  • fun (Callable[[POINT], float]) – The scalar function to minimize.
  • x0 (POINT) – The initial point for the minimization.
  • jac (Callable[[POINT], POINT] | 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

set_max_evals_grouped(limit)

Set max evals grouped

set_options

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.

wrap_function

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

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