Learning-based Quantum Robust Control: Algorithm, Applications and Experiments

Robust control design for quantum systems has been recognized as a key task in quantum information technology, molecular chemistry and atomic physics. In this paper, an improved differential evolution algorithm, referred to as msMSDE, is proposed to search robust fields for various quantum control problems. In msMSDE, multiple samples are used for fitness evaluation and a mixed strategy is employed for the mutation operation. In particular, the msMSDE algorithm is applied to the control problems of (i) open inhomogeneous quantum ensembles and (ii) the consensus goal of a quantum network with uncertainties. Numerical results are presented to demonstrate the excellent performance of the improved machine learning algorithm for these two classes of quantum robust control problems. Furthermore, msMSDE is experimentally implemented on femtosecond laser control applications to optimize two-photon absorption and control fragmentation of the molecule CH2BrI. Experimental results demonstrate excellent performance of msMSDE in searching for effective femtosecond laser pulses for various tasks.