Year: 2022
Author: Jingshi Li, Song Chen, Lijin Wang, Yanzhao Cao
Communications in Computational Physics, Vol. 31 (2022), Iss. 5 : pp. 1525–1545
Abstract
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 symplectic finite 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.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/cicp.OA-2021-0219
Communications in Computational Physics, Vol. 31 (2022), Iss. 5 : pp. 1525–1545
Published online: 2022-01
AMS Subject Headings: Global Science Press
Copyright: COPYRIGHT: © Global Science Press
Pages: 21
Keywords: Quantum (noise) control neural network symplectic methods norm-preservation.