Digraph states and their neural network representations

作     者：Ying Yang Huaixin Cao 杨莹;曹怀信

作者机构：School of Mathematics and Information TechnologyYuncheng UniversityYuncheng 044000China School of Mathematics and StatisticsShaanxi Normal UniversityXi’an 710119China 

出 版 物：《Chinese Physics B》 (中国物理B（英文版）)

年 卷 期：2022年第31卷第6期

页      面：183-191页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 081104[工学-模式识别与智能系统] 08[工学] 070104[理学-应用数学] 0835[工学-软件工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.12001480 and 11871318) the Applied Basic Research Program of Shanxi Province(Grant Nos.201901D211461 and 201901D211462) the Scientific and Technologial Innovation Programs of Higher Education Institutions in Shanxi(Grant No.2020L0554) the Excellent Doctoral Research Project of Shanxi Province(Grant No.QZX-2020001) the PhD Start-up Project of Yuncheng University(Grant No.YQ-2019021) 

主　　题：digraph state neural network quantum state representation 

摘      要：With the rapid development of machine learning,artificial neural networks provide a powerful tool to represent or approximate many-body quantum *** was proved that every graph state can be generated by a neural ***,we introduce digraph states and explore their neural network representations(NNRs).Based on some discussions about digraph states and neural network quantum states(NNQSs),we construct explicitly an NNR for any digraph state,implying every digraph state is an *** obtained results will provide a theoretical foundation for solving the quantum manybody problem with machine learning method whenever the wave-function is known as an unknown digraph state or it can be approximated by digraph states.