qiskit.algorithms.optimizers.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 inreset_intervaltimes.
Notes
In this optimization method, the optimization function have to satisfy three conditions written in K. M. Nakanishi, K. Fujii, and S. Todo. 2019. Sequential minimal optimization for quantum-classical hybrid algorithms. arXiv preprint arXiv:1903.12166.
__init__
__init__(maxiter=None, maxfev=1024, disp=False, reset_interval=32)
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 inreset_intervaltimes.
Notes
In this optimization method, the optimization function have to satisfy three conditions written in 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 |
bounds_support_level
Returns bounds support level
get_support_level
get_support_level()
return support level dictionary
gradient_num_diff
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
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
optimize
optimize(num_vars, objective_function, gradient_function=None, variable_bounds=None, initial_point=None)
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_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.
setting
Return setting
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