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

doi:10.1088/1674-1056/ad51f7

中国物理B ›› 2024, Vol. 33 ›› Issue (8): 80301-080301.doi: 10.1088/1674-1056/ad51f7

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

Menghui Mao(毛梦辉)†, Wei Zhou(周唯)†, Xinhui Li(李新慧), Ran Yang(杨然), Yan-Xiao Gong(龚彦晓)‡, and Shi-Ning Zhu(祝世宁)

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

收稿日期:2024-05-11 修回日期:2024-05-29 接受日期:2024-05-30 发布日期:2024-07-15
通讯作者: Yan-Xiao Gong E-mail:gongyanxiao@nju.edu.cn
基金资助:
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), and the National Natural Science Foundation of China (Grant No. 11974178).

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

Menghui Mao(毛梦辉)†, Wei Zhou(周唯)†, Xinhui Li(李新慧), Ran Yang(杨然), Yan-Xiao Gong(龚彦晓)‡, and Shi-Ning Zhu(祝世宁)

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

Received:2024-05-11 Revised:2024-05-29 Accepted:2024-05-30 Published:2024-07-15
Contact: Yan-Xiao Gong E-mail:gongyanxiao@nju.edu.cn
Supported by:
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), and the National Natural Science Foundation of China (Grant No. 11974178).

摘要/Abstract

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

Abstract: 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.

Key words: machine learning, state estimation, quantum state tomography, polarization-entangled photon source

中图分类号: (Entanglement and quantum nonlocality)

03.65.Ud

03.65.Wj (State reconstruction, quantum tomography) 02.60.-x (Numerical approximation and analysis) 42.50.Dv (Quantum state engineering and measurements)

Menghui Mao(毛梦辉), Wei Zhou(周唯), Xinhui Li(李新慧), Ran Yang(杨然), Yan-Xiao Gong(龚彦晓), and Shi-Ning Zhu(祝世宁). Machine-learning-assisted efficient reconstruction of the quantum states generated from the Sagnac polarization-entangled photon source[J]. 中国物理B, 2024, 33(8): 80301-080301.

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

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

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