Quantum learning control using differential evolution with equally-mixed strategies

Learning control has been recognized as a powerful approach in quantum information technology. In this paper, we extend the application of differential evolution (DE) to design optimal control for various quantum systems. Various DE methods are introduced and analyzed, and EMSDE featuring in equally mixed strategies is employed for quantum control. Two classes of quantum control problems, including control of four-level open quantum ensembles and quantum superconducting systems, are investigated to demonstrate the performance of EMSDE for learning control of quantum systems. Numerical results verify the effectiveness of the EMSDE method for various quantum systems and show the potential for complex quantum control problems.