Finding Short-Range Parity-Time Phase-Transition Points with a Neural Network
Songju Lei1, Dong Bai2*, Zhongzhou Ren2,3*, and Mengjiao Lyu4,5
1School of Physics, Nanjing University, Nanjing 210093, China 2School of Physics Science and Engineering, Tongji University, Shanghai 200092, China 3Key Laboratory of Advanced Micro-Structure Materials (Ministry of Education), Shanghai 200092, China 4College of Science, Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing 210016, China 5Key Laboratory of Aerospace Information Materials and Physics (NUAA), MIIT, Nanjing 211106, China
Abstract：The non-Hermitian $PT$-symmetric system can live in either unbroken or broken $PT$-symmetric phase. The separation point of the unbroken and broken $PT$-symmetric phases is called the $PT$-phase-transition point. Conventionally, given an arbitrary non-Hermitian $PT$-symmetric Hamiltonian, one has to solve the corresponding Schrödinger equation explicitly in order to determine which phase it is actually in. Here, we propose to use artificial neural network (ANN) to determine the $PT$-phase-transition points for non-Hermitian $PT$-symmetric systems with short-range potentials. The numerical results given by ANN agree well with the literature, which shows the reliability of our new method.
收稿日期: 2021-01-07
出版日期: 2021-05-02
引用本文:
. [J]. 中国物理快报, 2021, 38(5): 51101-.
Songju Lei, Dong Bai, Zhongzhou Ren, and Mengjiao Lyu. Finding Short-Range Parity-Time Phase-Transition Points with a Neural Network. Chin. Phys. Lett., 2021, 38(5): 51101-.
2021, Vol. 38 
