典型文献
Digraph states and their neural network representations
文献摘要：
With the rapid development of machine learning,artificial neural networks provide a powerful tool to represent or approximate many-body quantum states.It was proved that every graph state can be generated by a neural network.Here,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 NNQS.The obtained results will provide a theoretical foundation for solving the quantum many-body problem with machine learning method whenever the wave-function is known as an unknown digraph state or it can be approximated by digraph states.
文献关键词：
中图分类号：
作者姓名：
Ying Yang;Huaixin Cao
作者机构：
School of Mathematics and Information Technology,Yuncheng University,Yuncheng 044000,China;School of Mathematics and Statistics,Shaanxi Normal University,Xi'an 710119,China
文献出处：
引用格式：
[1]Ying Yang;Huaixin Cao-.Digraph states and their neural network representations)[J].中国物理B（英文版）,2022(06):206-214
A类：
Digraph,NNRs,NNQSs,NNR,NNQS
B类：
states,their,neural,representations,With,rapid,development,machine,learning,artificial,networks,provide,powerful,tool,many,body,quantum,It,was,proved,that,every,can,be,generated,by,Here,introduce,digraph,explore,Based,some,discussions,about,construct,explicitly,implying,obtained,results,will,theoretical,foundation,solving,problem,method,whenever,wave,function,unknown,approximated
AB值：
0.424835
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