In this article we present two algorithms that provide optimal control pulses directly
applicable in experiment. These are, namely, the optimal control algorithm, in which we
incorporated spectral pressure during optimization, and complementarily a theoretical
variant of the learning loop based on an evolutionary algorithm. With these tools a
productive dialog between theory and experiments on optimal control is imaginable. The
implementation of these pulses is assured by calculating the optimal mask pattern, that …