Closed loop learning control to suppress the effects of quantum decoherence

This paper explores the use of laboratory closed loop learning control to suppress the effects of decoherence in quantum dynamics. Simulations of the process are performed in multilevel quantum systems strongly interacting with the environment. A genetic algorithm is used to find an optimal control field which seeks out transition pathways to achieve a minimum influence of decoherence upon the system at a target time. The simulations suggest that decoherence may be optimally managed in the laboratory through closed loop operations with a suitable cost that is sensitive to the coherence of the dynamics. The case studies of dimension N=4 and N=10 with strong system–environment coupling indicate that the additional complexity with increasing system dimension can make it more difficult to manage decoherence.