A Symplectic Based Neural Network Algorithm for Quantum Controls under Uncertainty (Communications in Computational Physics, 2022)
Robust quantum control with uncertainty plays a crucial role in practicalquantum technologies. This paper presents a method for solving a quantum controlproblem by combining neural network and symplectic finite difference methods. Theneural network approach provides a framework that is easy to establish and train. Atthe same time, the symplectic methods possess the norm-preserving property for thequantum system to produce a realistic solution in physics. We construct a generalhigh dimensional quantum optimal control problem to evaluate the proposed methodand an approach that combines a neural network with forward Euler’s method. Ouranalysis and numerical experiments confirm that the neural network-based symplecticmethod achieves significantly better accuracy and robustness against noises.
Keywords: Quantum (noise) control; neural network; symplectic methods; norm-preservation.