Neural-Network Quantum State of Transverse-Field Ising Model  

作　　者：Han-Qing Shi Xiao-Yue Sun Ding-Fang Zeng 石汉青;孙小岳;曾定方(Theoretical Physics Division, College of Applied Sciences, Beijing University of Technology)

机构地区：[1]Theoretical Physics Division, College of Applied Sciences, Beijing University of Technology

出　　处：《Communications in Theoretical Physics》2019年第11期1379-1387,共9页理论物理通讯（英文版）

基　　金：Supported by the Natural Science Foundation of China under Grant No.11875082

摘　　要：Along the way initiated by Carleo and Troyer [G. Carleo and M. Troyer, Science 355(2017) 602], we construct the neural-network quantum state of transverse-field Ising model(TFIM) by an unsupervised machine learning method. Such a wave function is a map from the spin-configuration space to the complex number field determined by an array of network parameters. To get the ground state of the system, values of the network parameters are calculated by a Stochastic Reconfiguration(SR) method. We provide for this SR method an understanding from action principle and information geometry aspects. With this quantum state, we calculate key observables of the system, the energy,correlation function, correlation length, magnetic moment, and susceptibility. As innovations, we provide a high e?ciency method and use it to calculate entanglement entropy(EE) of the system and get results consistent with previous work very well.

关 键 词：neural network QUANTUM state Stochastic RECONFIGURATION method transverse field ISING model QUANTUM phase transition 

分 类 号：O41[理学—理论物理]