Robust quantum control with uncertainty plays a crucial role in practical quantum technologies.This paper presents a method for solving a quantum control problem by combining neural network and symplecticfinite differ...Robust quantum control with uncertainty plays a crucial role in practical quantum technologies.This paper presents a method for solving a quantum control problem by combining neural network and symplecticfinite difference methods.The neural network approach provides a framework that is easy to establish and train.At the same time,the symplectic methods possess the norm-preserving property for the quantum system to produce a realistic solution in physics.We 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 method.Our analysis and numerical experiments confirm that the neural network-based symplectic method achieves significantly better accuracy and robustness against noises.展开更多
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.
文摘Robust quantum control with uncertainty plays a crucial role in practical quantum technologies.This paper presents a method for solving a quantum control problem by combining neural network and symplecticfinite difference methods.The neural network approach provides a framework that is easy to establish and train.At the same time,the symplectic methods possess the norm-preserving property for the quantum system to produce a realistic solution in physics.We 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 method.Our analysis and numerical experiments confirm that the neural network-based symplectic method achieves significantly better accuracy and robustness against noises.
