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Volume 31, Issue 5
A Symplectic Based Neural Network Algorithm for Quantum Controls under Uncertainty

Jingshi Li, Song Chen, Lijin Wang & Yanzhao Cao

DOI: 10.4208/cicp.OA-2021-0219

Commun. Comput. Phys., 31 (2022), pp. 1525-1545.

Published online: 2022-05

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  • 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.

  • Keywords

Quantum (noise) control, neural network, symplectic methods, norm-preservation.

  • AMS Subject Headings

81Q93, 81Q05, 65P10, 49M25

  • Copyright

COPYRIGHT: © Global Science Press

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  • TXT
@Article{CiCP-31-1525, author = {Li , JingshiChen , SongWang , Lijin and Cao , Yanzhao}, title = {A Symplectic Based Neural Network Algorithm for Quantum Controls under Uncertainty}, journal = {Communications in Computational Physics}, year = {2022}, volume = {31}, number = {5}, pages = {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.

}, issn = {1991-7120}, doi = {https://doi.org/10.4208/cicp.OA-2021-0219}, url = {http://global-sci.org/intro/article_detail/cicp/20513.html} }
TY - JOUR T1 - A Symplectic Based Neural Network Algorithm for Quantum Controls under Uncertainty AU - Li , Jingshi AU - Chen , Song AU - Wang , Lijin AU - Cao , Yanzhao JO - Communications in Computational Physics VL - 5 SP - 1525 EP - 1545 PY - 2022 DA - 2022/05 SN - 31 DO - http://doi.org/10.4208/cicp.OA-2021-0219 UR - https://global-sci.org/intro/article_detail/cicp/20513.html KW - Quantum (noise) control, neural network, symplectic methods, norm-preservation. AB -

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.

Li , JingshiChen , SongWang , Lijin and Cao , Yanzhao. (2022). A Symplectic Based Neural Network Algorithm for Quantum Controls under Uncertainty. Communications in Computational Physics. 31 (5). 1525-1545. doi:10.4208/cicp.OA-2021-0219
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