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**
1 School of Physics, Sun Yat-sen University, Guangzhou 5102752 State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics, Tsinghua University, Beijing 1000843 Department of Physics, National University of Singapore, 117542, Singapore4 Institute of High Performance Computing, 138632, Singapore
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.
收稿日期: 2019-10-24
出版日期: 2019-12-23
引用本文:
. [J]. 中国物理快报, 2020, 37(1): 18401-.
链接本文:
https://cpl.iphy.ac.cn/CN/10.1088/0256-307X/37/1/018401
或
https://cpl.iphy.ac.cn/CN/Y2020/V37/I1/18401
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2020, Vol. 37 
