qiskit.aqua.components.optimizers.NFT¶

class NFT(maxiter=None, maxfev=1024, disp=False, reset_interval=32)[source]¶

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.

__init__(maxiter=None, maxfev=1024, disp=False, reset_interval=32)[source]¶

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.

Methods

`__init__`([maxiter, maxfev, disp, reset_interval])	Built out using scipy framework, for details, please refer to https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html.
`get_support_level`()	return support level dictionary
`gradient_num_diff`(x_center, f, epsilon[, …])	We compute the gradient with the numeric differentiation in the parallel way, around the point x_center.
`optimize`(num_vars, objective_function[, …])	Perform optimization.
`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.
`wrap_function`(function, args)	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

property bounds_support_level¶: Returns bounds support level

get_support_level()[source]¶: return support level dictionary

static gradient_num_diff(x_center, f, epsilon, max_evals_grouped=1)¶

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

Returns

the gradient computed

Return type

grad

property gradient_support_level¶: Returns gradient support level

property initial_point_support_level¶: Returns initial point support level

property is_bounds_ignored¶: Returns is bounds ignored

property is_bounds_required¶: Returns is bounds required

property is_bounds_supported¶: Returns is bounds supported

property is_gradient_ignored¶: Returns is gradient ignored

property is_gradient_required¶: Returns is gradient required

property is_gradient_supported¶: Returns is gradient supported

property is_initial_point_ignored¶: Returns is initial point ignored

property is_initial_point_required¶: Returns is initial point required

property is_initial_point_supported¶: Returns is initial point supported

optimize(num_vars, objective_function, gradient_function=None, variable_bounds=None, initial_point=None)[source]¶

Perform optimization.

Parameters

num_vars (int) – Number of parameters to be optimized.
objective_function (callable) – A function that computes the objective function.
gradient_function (callable) – A function that computes the gradient of the objective function, or None if not available.
variable_bounds (list[(float, float)]) – List of variable bounds, given as pairs (lower, upper). None means unbounded.
initial_point (numpy.ndarray[float]) – Initial point.

Returns

point, value, nfev: point: is a 1D numpy.ndarray[float] containing the solution value: is a float with the objective function value nfev: number of objective function calls made if available or None

Raises

ValueError – invalid input

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.

property setting¶: Return setting

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