Machine-learning-assisted efficient reconstruction of the quantum states generated from the Sagnac polarization-entangled photon source  

作　　者：毛梦辉 周唯 李新慧 杨然 龚彦晓 祝世宁 Menghui Mao;Wei Zhou;Xinhui Li;Ran Yang;Yan-Xiao Gong;Shi-Ning Zhu(National Laboratory of Solid State Microstructures and School of Physics,Nanjing University,Nanjing 210093,China Author notes)

机构地区：[1]National Laboratory of Solid State Microstructures and School of Physics,Nanjing University,Nanjing 210093,China Author notes

出　　处：《Chinese Physics B》2024年第8期50-54,共5页中国物理B（英文版）

基　　金：Project supported by the National Key Research and Development Program of China (Grant No.2019YFA0705000);Leading-edge technology Program of Jiangsu Natural Science Foundation (Grant No.BK20192001);the National Natural Science Foundation of China (Grant No.11974178)。

摘　　要：Neural networks are becoming ubiquitous in various areas of physics as a successful machine learning(ML)technique for addressing different tasks.Based on ML technique,we propose and experimentally demonstrate an efficient method for state reconstruction of the widely used Sagnac polarization-entangled photon source.By properly modeling the target states,a multi-output fully connected neural network is well trained using only six of the sixteen measurement bases in standard tomography technique,and hence our method reduces the resource consumption without loss of accuracy.We demonstrate the ability of the neural network to predict state parameters with a high precision by using both simulated and experimental data.Explicitly,the mean absolute error for all the parameters is below 0.05 for the simulated data and a mean fidelity of 0.99 is achieved for experimentally generated states.Our method could be generalized to estimate other kinds of states,as well as other quantum information tasks.

关 键 词：machine learning state estimation quantum state tomography polarization-entangled photon source 

分 类 号：O413[理学—理论物理] TP18[理学—物理]