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

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外文摘要：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.

外文关键词：

machine learningstate estimationquantum state tomographypolarization-entangled photon source

作者：

毛梦辉、周唯、李新慧、杨然、龚彦晓、祝世宁

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作者单位：

National Laboratory of Solid State Microstructures and School of Physics,Nanjing University,Nanjing 210093,China

基金：

National Key Research and Development Program of ChinaLeadingedge technology Program of Jiangsu Natural Science FoundationNational Natural Science Foundation of China

项目编号：

2019YFA0705000BK2019200111974178

出版年：

2024

DOI：

10.1088/1674-1056/ad51f7

中国物理B(英文版)

中国物理学会和中国科学院物理研究所

中国物理B(英文版)

CSTPCDEI

影响因子：0.995

ISSN：1674-1056

年,卷(期)：2024.33(8)