NFT
qiskit.algorithms.optimizers.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 (opens in a new tab)
Built out using scipy framework, for details, please refer to https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html (opens in a new tab).
Parameters
- maxiter (int (opens in a new tab) | None) – Maximum number of iterations to perform.
- maxfev (int (opens in a new tab)) – Maximum number of function evaluations to perform.
- disp (bool (opens in a new tab)) – disp
- reset_interval (int (opens in a new tab)) – The minimum estimates directly once in
reset_intervaltimes. - options (dict (opens in a new tab) | 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 (opens in a new tab)) – the epsilon used in the numeric differentiation.
- max_evals_grouped (int (opens in a new tab)) – 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 (opens in a new tab)]) – 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 (opens in a new tab)[tuple (opens in a new tab)[float (opens in a new tab), float (opens in a new tab)]] | 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
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 (opens in a new tab)) – 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 (opens in a new tab)) – the args to be injected
Returns
wrapper
Return type
function_wrapper