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

doi:10.1088/1674-1056/ad51f7

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.

Keywords: machine learning state estimation quantum state tomography polarization-entangled photon source

Received: 11 May 2024
Revised: 29 May 2024
Accepted manuscript online:

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

Fund: 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).

Corresponding Authors: Yan-Xiao Gong
E-mail: gongyanxiao@nju.edu.cn

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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 2024 Chin. Phys. B 33 080301

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

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