krotov.optimize module

Summary

Data:

optimize_pulses Use Krotov’s method to optimize towards the given objectives

__all__: optimize_pulses

Reference

krotov.optimize.optimize_pulses(objectives, pulse_options, tlist, *, propagator, chi_constructor, mu=None, sigma=None, iter_start=0, iter_stop=5000, check_convergence=None, state_dependent_constraint=None, info_hook=None, modify_params_after_iter=None, storage='array', parallel_map=None, store_all_pulses=False)[source]

Use Krotov’s method to optimize towards the given objectives

Optimize all time-dependent controls found in the Hamiltonians of the given objectives.

Parameters:
  • objectives (list[Objective]) – List of objectives
  • pulse_options (dict) – Mapping of time-dependent controls found in the Hamiltonians of the objectives to PulseOptions instances. There must be a mapping for each control. As numpy arrays are unhashable and thus cannot be used as dict keys, the options for a control that is an array must set using pulse_options[id(control)] = ...
  • tlist (numpy.ndarray) – Array of time grid values, cf. mesolve()
  • propagator (callable) – Function that propagates the state backward or forwards in time by a single time step, between two points in tlist
  • chi_constructor (callable) – Function that calculates the boundary condition for the backward propagation. This is where the final-time functional (indirectly) enters the optimization.
  • mu (None or callable) – Function that calculates the derivative \(\frac{\partial H}{\partial\epsilon}\) for an equation of motion \(\dot{\phi}(t) = -i H[\phi(t)]\) of an abstract operator \(H\) and an abstract state \(\phi\). If None, defaults to krotov.mu.derivative_wrt_pulse(), which covers the standard Schrödinger and master equations. See krotov.mu for a full explanation of the role of mu in the optimization, and the required function signature.
  • sigma (None or callable) – Function that calculates the second-order Krotov term. If None, the first-order Krotov method is used.
  • iter_start (int) – The formal iteration number at which to start the optimization
  • iter_stop (int) – The iteration number after which to end the optimization, whether or not convergence has been reached
  • check_convergence (None or callable) – Function that determines whether the optimization has converged. If None, the optimization will only end when iter_stop is reached. See krotov.convergence for details.
  • state_dependent_constraint (None or callable) – Function that evaluates a state-dependent constraint. If None, optimize without any state-dependent constraint. Currently not implemented.
  • info_hook (None or callable) – Function that is called after each iteration of the optimization, for the purpose of analysis. Any value returned by info_hook (e.g. an evaluated functional J_T) will be stored, for each iteration, in the info_vals attribute of the returned Result. The info_hook must have the same signature as krotov.info_hooks.print_debug_information(). It should not modify its arguments except for shared_data in any way.
  • modify_params_after_iter (None or callable) – Function that is called after each iteration, which may modify its arguments for certain advanced use cases, such as dynamically adjusting lambda_vals, or applying spectral filters to the optimized_pulses. It has the same interface as info_hook but should not return anything. The modify_params_after_iter function is called immediately before info_hook, and can transfer arbitrary data to any subsequent info_hook via the shared_data argument.
  • storage (callable) – Storage constructor for the storage of propagated states. Must accept an integer parameter N and return an empty array of length N. The default value ‘array’ is equivalent to functools.partial(numpy.empty, dtype=object).
  • parallel_map (callable or None) – Parallel function evaluator. The argument must have the same specification as qutip.parallel.serial_map(), which is used when None is passed. Alternatives are qutip.parallel.parallel_map() or qutip.ipynbtools.parallel_map().
  • store_all_pulses (bool) – Whether or not to store the optimized pulses from all iterations in Result.
Returns:

The result of the optimization.
Return type:

Result
Raises:

ValueError – If any controls are not real-valued, or if any update shape is not a real-valued function in the range [0, 1].