A Symplectic Based Neural Network Algorithm for Quantum Controls under Uncertainty

作     者：Jingshi Li Song Chen Lijin Wang Yanzhao Cao 

作者机构：Department of Mathematics and StatisticsNanjing University of Information Science and TechnologyNanjingJiangsu210044P.R.China Department of Mathematics&StatisticsUniversity of Wisconsin–La CrosseLa CrosseWI54601USA School of Mathematical SciencesUniversity of Chinese Academy of SciencesBeijing100049P.R.China Department of Mathematics&StatisticsAuburn UniversityAuburnAL36830USA 

出 版 物：《Communications in Computational Physics》 (计算物理通讯（英文）)

年 卷 期：2022年第31卷第5期

页      面：1525-1545页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：supported by the National Natural Science Foundation of China(No.11971458) supported by U.S.Department of Energy under the grant number DE-SC0022253 

主　　题：Quantum(noise)control neural network symplectic methods norm-preservation 

摘      要：Robust quantum control with uncertainty plays a crucial role in practical quantum *** paper presents a method for solving a quantum control problem by combining neural network and symplecticfinite difference *** neural network approach provides a framework that is easy to establish and *** the same time,the symplectic methods possess the norm-preserving property for the quantum system to produce a realistic solution in *** construct a general high dimensional quantum optimal control problem to evaluate the proposed method and an approach that combines a neural network with forward Euler’s *** analysis and numerical experiments confirm that the neural network-based symplectic method achieves significantly better accuracy and robustness against noises.