quri_parts.algo.optimizer.nft module#

class quri_parts.algo.optimizer.nft.OptimizerStateNFT(params: 'npt.NDArray[np.float_]', cost: float = 0.0, status: quri_parts.algo.optimizer.interface.OptimizerStatus = <OptimizerStatus.SUCCESS: 1>, niter: int = 0, funcalls: int = 0, gradcalls: int = 0)#

Bases: OptimizerState

params: npt.NDArray[np.float_]#

Current parameter values.

class quri_parts.algo.optimizer.nft.NFTBase(randomize: bool = False, ftol: float | None = 1e-05)#

Bases: Optimizer

get_init_state(init_params: npt.NDArray[np.float_]) → OptimizerStateNFT#

Returns an initial state for optimization.

step(state: OptimizerState, cost_function: Callable[[npt.NDArray[np.float_]], float], grad_function: Callable[[npt.NDArray[np.float_]], npt.NDArray[np.float_]] | None = None) → OptimizerStateNFT#

Run a single optimization step and returns a new state.

class quri_parts.algo.optimizer.nft.NFT(randomize: bool = False, reset_interval: int | None = 32, eps: float = 1e-32, ftol: float | None = 1e-05)#

Bases: NFTBase

Nakanishi-Fujii-Todo optimization algorithm proposed in [1]. The algorithms optimizes the cost function sequentially with respect to the parameters.

Parameters:

  • randomize – If True, the order of the parameters over which the cost function is optimized will be random. If False, the order will be the same as the order of the array of initial parameters.

  • reset_interval – minimum value of the cost function is evaluated by direct function-call only once in reset_interval times.

  • eps – a small number used for avoiding zero division.

  • ftol – If not None, judge convergence by cost function tolerance. See ftol() for details.

Ref:
[1]: Ken M. Nakanishi, Keisuke Fujii, and Synge Todo,

Sequential minimal optimization for quantum-classical hybrid algorithms, Phys. Rev. Research 2, 043158 (2020).

class quri_parts.algo.optimizer.nft.NFTfit(randomize: bool = False, n_points: int = 3, ftol: float | None = 1e-05)#

Bases: NFTBase

Basically the same as Nakanishi-Fujii-Todo optimization algorithm [1]. The difference from NFT class is that NFTfit uses SciPy fitting function curve_fit for parameters update.

Parameters:

  • randomize – If True, the order of the parameters over which the cost function is optimized will be random. If False, the order will be the same as the order of the array of initial parameters.

  • n_point – Number of values of cost function for function fitting using SciPy fitting function curve_fit.

  • ftol – If not None, judge convergence by cost function tolerace. See ftol() for details.

Ref:
[1]: Ken M. Nakanishi, Keisuke Fujii, and Synge Todo,

Sequential minimal optimization for quantum-classical hybrid algorithms, Phys. Rev. Research 2, 043158 (2020).