arXiv:1905.11066 · Communications in Theoretical Physics

Neural-Network Quantum State of Transverse-Field Ising Model*

Han-Qing 汉青 Shi 石, Xiao-Yue 小岳 Sun 孙, Ding-Fang 定方 Zeng 曾

Published Nov 1, 2019• 5 citations

Abstract

Abstract 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 efficiency method and use it to calculate entanglement entropy (EE) of the system and get results consistent with previous work very well .

DOI: 10.1088/0253-6102/71/11/1379

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