Source code for qutip.control.optimresult

# -*- coding: utf-8 -*-
# @author: Alexander Pitchford
# @email1:
# @email2:
# @organization: Aberystwyth University
# @supervisor: Daniel Burgarth

Class containing the results of the pulse optimisation

import numpy as np

[docs]class OptimResult(object): """ Attributes give the result of the pulse optimisation attempt Attributes ---------- termination_reason : string Description of the reason for terminating the optimisation fidelity : float final (normalised) fidelity that was achieved initial_fid_err : float fidelity error before optimisation starting fid_err : float final fidelity error that was achieved goal_achieved : boolean True is the fidely error achieved was below the target grad_norm_final : float Final value of the sum of the squares of the (normalised) fidelity error gradients grad_norm_min_reached : float True if the optimisation terminated due to the minimum value of the gradient being reached num_iter : integer Number of iterations of the optimisation algorithm completed max_iter_exceeded : boolean True if the iteration limit was reached max_fid_func_exceeded : boolean True if the fidelity function call limit was reached wall_time : float time elapsed during the optimisation wall_time_limit_exceeded : boolean True if the wall time limit was reached time : array[num_tslots+1] of float Time are the start of each timeslot with the final value being the total evolution time initial_amps : array[num_tslots, n_ctrls] The amplitudes at the start of the optimisation final_amps : array[num_tslots, n_ctrls] The amplitudes at the end of the optimisation evo_full_final : Qobj The evolution operator from t=0 to t=T based on the final amps evo_full_initial : Qobj The evolution operator from t=0 to t=T based on the initial amps stats : Stats Object contaning the stats for the run (if any collected) optimizer : Optimizer Instance of the Optimizer used to generate the result """ def __init__(self): self.reset() def reset(self): = 0.0 self.initial_fid_err = np.Inf self.fid_err = np.Inf self.goal_achieved = False self.grad_norm_final = 0.0 self.grad_norm_min_reached = False self.num_iter = 0 self.max_iter_exceeded = False self.num_fid_func_calls = 0 self.max_fid_func_exceeded = False self.wall_time = 0.0 self.wall_time_limit_exceeded = False self.termination_reason = "not started yet" self.time = None self.initial_amps = None self.final_amps = None self.evo_full_final = None self.evo_full_initial = None self.stats = None self.optimizer = None