Chin. Phys. Lett.  2020, Vol. 37 Issue (1): 018401    DOI: 10.1088/0256-307X/37/1/018401
CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY |
Predicting Quantum Many-Body Dynamics with Transferable Neural Networks
Ze-Wang Zhang1, Shuo Yang2, Yi-Hang Wu1, Chen-Xi Liu1, Yi-Min Han1, Ching-Hua Lee3,4, Zheng Sun1, Guang-Jie Li1, Xiao Zhang1**
1School of Physics, Sun Yat-sen University, Guangzhou 510275
2State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics, Tsinghua University, Beijing 100084
3Department of Physics, National University of Singapore, 117542, Singapore
4Institute of High Performance Computing, 138632, Singapore
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Ze-Wang Zhang, Shuo Yang, Yi-Hang Wu et al  2020 Chin. Phys. Lett. 37 018401
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Abstract Advanced machine learning (ML) approaches such as transfer learning have seldom been applied to approximate quantum many-body systems. Here we demonstrate that a simple recurrent unit (SRU) based efficient and transferable sequence learning framework is capable of learning and accurately predicting the time evolution of the one-dimensional (1D) Ising model with simultaneous transverse and parallel magnetic fields, as quantitatively corroborated by relative entropy measurements between the predicted and exact state distributions. At a cost of constant computational complexity, a larger many-body state evolution is predicted in an autoregressive way from just one initial state, without any guidance or knowledge of any Hamiltonian. Our work paves the way for future applications of advanced ML methods in quantum many-body dynamics with knowledge only from a smaller system.
Received: 24 October 2019      Published: 23 December 2019
PACS:  84.35.+i (Neural networks)  
  05.50.+q (Lattice theory and statistics)  
  02.70.-c (Computational techniques; simulations)  
Fund: Supported by the National Natural Science Foundation of China under Grant Nos 11874431 and 11804181, the National Key R&D Program of China under Grant No 2018YFA0306800, and the Guangdong Science and Technology Innovation Youth Talent Program under Grant Nos 2016TQ03X688 and 2018YFA0306504, and the Research Fund Program of the State Key Laboratory of Low-Dimensional Quantum Physics under Grant No ZZ201803.
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https://cpl.iphy.ac.cn/10.1088/0256-307X/37/1/018401       OR      https://cpl.iphy.ac.cn/Y2020/V37/I1/018401
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Ze-Wang Zhang
Shuo Yang
Yi-Hang Wu
Chen-Xi Liu
Yi-Min Han
Ching-Hua Lee
Zheng Sun
Guang-Jie Li
Xiao Zhang
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