Quantum Robust Control for Time-Varying Noises Based on Adversarial Learning

Time-varying noises are one of the reasons that make it difficult for quantum systems to complete control tasks. How to quantify the influence of time-varying noises on control results and how to design a control law that can resist time-varying noises are two important problems. In this paper, the adversarial learning is introduced into quantum control and the loss function under the worst-case noise is used as a way to quantify the impact of time-varying noises on control performance. We utilize the Gradient Ascent Pulse Engineering (GRAPE) technique to search the worst-case noise and meanwhile offer a strategy to improve the robustness of the control law. Simulation experiments on a two-qubit system and a four-qubit system show that the found noises indeed can act as worst-case noises. Furthermore, the optimized control laws demonstrate good robustness to time-varying noises in state preparation tasks.