Dynamical learning of non-Markovian quantum dynamics

doi:10.1088/1674-1056/ac2490

中国物理B ›› 2022, Vol. 31 ›› Issue (1): 10314-010314.doi: 10.1088/1674-1056/ac2490

Dynamical learning of non-Markovian quantum dynamics

Jintao Yang(杨锦涛)^1,2,†, Junpeng Cao(曹俊鹏)^1,2,3,4,‡, and Wen-Li Yang(杨文力)^4,5,6,7,§

1 Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China;
2 School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China;
3 Songshan Lake Materials Laboratory, Dongguan 523808, China;
4 Peng Huanwu Center for Fundamental Theory, Xi'an 710127, China;
5 Institute of Modern Physics, Northwest University, Xi'an 710127, China;
6 School of Physical Sciences, Northwest University, Xi'an 710127, China;
7 Shaanxi Key Laboratory for Theoretical Physics Frontiers, Xi'an 710127, China

收稿日期:2021-06-03 修回日期:2021-07-19 接受日期:2021-09-08 出版日期:2021-12-03 发布日期:2021-12-31
通讯作者: Jintao Yang, Junpeng Cao, and Wen-Li Yang E-mail:jintao_yang@iphy.ac.cn;junpengcao@iphy.ac.cn;wlyang@nwu.edu.cn
基金资助:
Project supported by the National Program for Basic Research of the Ministry of Science and Technology of China (Grant Nos. 2016YFA0300600 and 2016YFA0302104), the National Natural Science Foundation of China (Grant Nos. 12074410, 12047502, 11934015, 11975183, 11947301, 11774397, 11775178, and 11775177), the Major Basic Research Program of the Natural Science of Shaanxi Province, China (Grant No. 2017ZDJC-32), the Australian Research Council (Grant No. DP 190101529), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB33000000), and the Double First-Class University Construction Project of Northwest University.

Dynamical learning of non-Markovian quantum dynamics

Jintao Yang(杨锦涛)^1,2,†, Junpeng Cao(曹俊鹏)^1,2,3,4,‡, and Wen-Li Yang(杨文力)^4,5,6,7,§

1 Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China;
2 School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China;
3 Songshan Lake Materials Laboratory, Dongguan 523808, China;
4 Peng Huanwu Center for Fundamental Theory, Xi'an 710127, China;
5 Institute of Modern Physics, Northwest University, Xi'an 710127, China;
6 School of Physical Sciences, Northwest University, Xi'an 710127, China;
7 Shaanxi Key Laboratory for Theoretical Physics Frontiers, Xi'an 710127, China

Received:2021-06-03 Revised:2021-07-19 Accepted:2021-09-08 Online:2021-12-03 Published:2021-12-31
Contact: Jintao Yang, Junpeng Cao, and Wen-Li Yang E-mail:jintao_yang@iphy.ac.cn;junpengcao@iphy.ac.cn;wlyang@nwu.edu.cn
Supported by:
Project supported by the National Program for Basic Research of the Ministry of Science and Technology of China (Grant Nos. 2016YFA0300600 and 2016YFA0302104), the National Natural Science Foundation of China (Grant Nos. 12074410, 12047502, 11934015, 11975183, 11947301, 11774397, 11775178, and 11775177), the Major Basic Research Program of the Natural Science of Shaanxi Province, China (Grant No. 2017ZDJC-32), the Australian Research Council (Grant No. DP 190101529), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB33000000), and the Double First-Class University Construction Project of Northwest University.

摘要/Abstract

摘要： We study the non-Markovian dynamics of an open quantum system with machine learning. The observable physical quantities and their evolutions are generated by using the neural network. After the pre-training is completed, we fix the weights in the subsequent processes thus do not need the further gradient feedback. We find that the dynamical properties of physical quantities obtained by the dynamical learning are better than those obtained by the learning of Hamiltonian and time evolution operator. The dynamical learning can be applied to other quantum many-body systems, non-equilibrium statistics and random processes.

关键词: machine learning, quantum dynamics, open quantum system

Abstract: We study the non-Markovian dynamics of an open quantum system with machine learning. The observable physical quantities and their evolutions are generated by using the neural network. After the pre-training is completed, we fix the weights in the subsequent processes thus do not need the further gradient feedback. We find that the dynamical properties of physical quantities obtained by the dynamical learning are better than those obtained by the learning of Hamiltonian and time evolution operator. The dynamical learning can be applied to other quantum many-body systems, non-equilibrium statistics and random processes.

Key words: machine learning, quantum dynamics, open quantum system

中图分类号: (Decoherence; open systems; quantum statistical methods)

03.65.Yz

47.10.Fg (Dynamical systems methods) 02.50.Ga (Markov processes)

Jintao Yang(杨锦涛), Junpeng Cao(曹俊鹏), and Wen-Li Yang(杨文力). Dynamical learning of non-Markovian quantum dynamics[J]. 中国物理B, 2022, 31(1): 10314-010314.

Jintao Yang(杨锦涛), Junpeng Cao(曹俊鹏), and Wen-Li Yang(杨文力). Dynamical learning of non-Markovian quantum dynamics[J]. Chin. Phys. B, 2022, 31(1): 10314-010314.

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Dynamical learning of non-Markovian quantum dynamics

Dynamical learning of non-Markovian quantum dynamics

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