State Classification via a Random-Walk-Based Quantum Neural Network  

作　　者：Lu-Ji Wang Jia-Yi Lin Shengjun Wu 王露吉;林嘉懿;吴盛俊(Institute for Brain Sciences and Kuang Yaming Honors School,Nanjing University,Nanjing 210023,China;School of Physics,Nanjing University,Nanjing 210093,China)

机构地区：[1]Institute for Brain Sciences and Kuang Yaming Honors School,Nanjing University,Nanjing 210023,China [2]School of Physics,Nanjing University,Nanjing 210093,China

出　　处：《Chinese Physics Letters》2022年第5期4-9,共6页中国物理快报（英文版）

基　　金：supported by the National Key R&D Program of China (Grant No. 2017YFA0303703);the National Natural Science Foundation of China (Grant No. 12175104)

摘　　要：In quantum information technology,crucial information is regularly encoded in different quantum states.To extract information,the identification of one state from the others is inevitable.However,if the states are nonorthogonal and unknown,this task will become awesomely tricky,especially when our resources are also limited.Here,we introduce the quantum stochastic neural network(QSNN),and show its capability to accomplish the binary discrimination of quantum states.After a handful of optimizing iterations,the QSNN achieves a success probability close to the theoretical optimum,no matter whether the states are pure or mixed.Other than binary discrimination,the QSNN is also applied to classify an unknown set of states into two types:entangled ones and separable ones.After training with four samples,it can classify a number of states with acceptable accuracy.Our results suggest that the QSNN has the great potential to process unknown quantum states in quantum information.

关 键 词：QUANTUM classify SEPARABLE 

分 类 号：O413[理学—理论物理] TP183[理学—物理]