A Neural Network Approach to Sampling Based Learning Control for Quantum System with Uncertainty (Communications in Computational Physics, 2021)
Robust control design for quantum systems with uncertainty is a key taskfor developing practical quantum technology. In this paper, we apply neural networksto learn the control of a quantum system with uncertainty. By exploiting the auto differentiation function developed for neural network models, our method avoids themanual computation of the gradient of the cost function as required in traditionalmethods. We implement our method using two algorithms. One uses neural networks to learn both the states and the controls and one uses neural networks to learnonly the controls but solve the states by finite difference methods. Both algorithms incorporate the sampling-based learning process into the training of the networks. Theperformance of the algorithms is evaluated on a practical numerical example, followedby a detailed discussion about the advantage and trade-offs between our method andthe other numerical schemes.
Keywords: Optimal control; quantum computing; uncertainty quantification; deep neural network.