Ensemble control of open quantum systems using differential evolution

Ensemble control of open quantum systems from one state to another is much more difficult than closed quantum systems due to their interactions with the environment. In this paper, a sampling-based learning control (SLC) method is applied to the optimal control design for inhomogeneous open quantum ensembles regarding the state-to-state transition task. A differential evolution (DE) algorithm is adopted for the training step of the SLC design to find the optimal control for the generalized system constructed by sampling members of the inhomogeneous quantum ensembles. Numerical results, including two-level, three-level and four-level open quantum ensembles, demonstrate the effectiveness of the proposed control approach for open quantum ensembles.