Entanglement generation in uncertain quantum systems using sampling-based learning control

In this paper, we develop a control algorithm to generate entanglement in a quantum system with uncertainties. The system under consideration is an uncertain system of two two-level atoms interacting with each other through a dipole-dipole interaction. The sampling-based learning control (SLC) strategy is employed to find a control law. An SLC strategy contains two steps of training and evaluation. In the training step, we obtain several samples to construct an augmented system by sampling the uncertainties according to a possible distribution of the uncertainty parameters and learn an optimal control law by maximizing the performance index. In the evaluation step, we apply the obtained control law from the training step to additional samples through randomly sampling the uncertainties. Numerical results are presented showing the success of the SLC method in control design for generating entanglement.