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: 30 May 2024

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

[1] Bennett C H, Brassard G, Crepeau C, Jozsa R, Peres A and Wootters We K 1993 Phys. Rev. Lett. 70 1895
[2] Peters N A, Barreiro J T, Goggin M E, Wei T C and Kwiat P G 2005 Phys. Rev. Lett. 94 150502
[3] Zhang Q, Goebel A, Wagenknecht C, Chen Y A, Zhao B, Yang T, Mair A, Schmiedmayer J and Pan J W 2006 Nat. Phys. 2 678
[4] Liu X, Hu J, Li Z F, Li X, Li P Y, Liang P J, Zhou Z Q, Li C F and Guo G C 2021 Nature 594 41
[5] Pironio S, Acin A, Massar S, de la Giroday A B, Matsukevich D N, Maunz P, Olmschenk S, Hayes D, Luo L, Manning T A and Monroe C 2010 Nature 464 1021
[6] Christensen B G, McCusker K T, Altepeter J B, Calkins B, Gerrits T, Lita A E, Miller A, Shalm L K, Zhang Y, Nam S W, Brunner N, Lim C C W, Gisin N and Kwiat P G 2013 Phys. Rev. Lett. 111 130406
[7] Liu W Z, Li M H, Ragy S, Zhao S R, Bai B, Liu Y, Brown P J, Zhang J, Colbeck R, Fan J, Zhang Q and Pan J W 2021 Nat. Phys. 17 448
[8] Kok P, Munro W J, Nemoto K, Ralph T C, Dowling J P and Milburn G J 2007 Rev. Mod. Phys. 79 135
[9] Ladd T D, Jelezko F, Laflamme R, Nakamura Y, Monroe C and O’Brien J L 2021 Nature 464 45
[10] Knill E, Laflamme R and Milburn G J 2001 Nature 409 46
[11] White A G, James D F V, Eberhard P H and Kwiat P G 1999 Phys. Rev. Lett. 83 3103
[12] James D F V, Kwiat P G, Munro W J and White A G 2001 Phys. Rev. A 64 052312
[13] Ma Y G, Pang L G, Wang R and Zhou K 2023 Chin. Phys. Lett. 40 122101
[14] Suresh R, Bishnoi H, Kuklin A V, Parikh A, Molokeev M, Harinarayanan R, Gharat S and Hiba P 2024 Front. Phys. 12
[15] Coelho C N, Kuusela A, Li S, Zhuang H, Ngadiuba J, Aarrestad T K, Loncar V, Pierini M, Pol A A and Summers S 2021 Nat. Mach. Intell. 3 675
[16] Kremer J, Stensbo-Smidt K, Gieseke F, Pedersen K S and Igel C 2017 IEEE Intelligent Systems 32 16
[17] Butler K T, Davies D W, Cartwright H, Isayev O and Walsh A 2017 Nature 559 547
[18] Jiang W, Lu Z and Deng D L 2022 Chin. Phys. Lett. 39 050303
[19] Gao J, Qiao L F, Jiao Z Q, Ma Y C, Hu C Q, Ren R J, Yang A L, Tang H, Yung M H and Jin X M 2018 Phys. Rev. Lett. 120 240501
[20] Ma Y C and Yung M H 2018 npj Quantum Inf. 4 34
[21] Yang M, Ren C, Ma Y, Xiao Y, Ye X J, Song L L, Xu J S, Yung M H, Li C F and Guo G C 2019 Phys. Rev. Lett. 123 190401
[22] Ren C and Chen C 2019 Phys. Rev. A 100 022314
[23] Zhang X, Luo M, Wen Z, Feng Q, Pang S, Luo W and Zhou X 2021 Phys. Rev. Lett. 127 130503
[24] Koutný D, Ginés L, Moczála-Dusanowska M, H′ ’ofling S, Schneider C, Predojević A and Ježek M 2023 Sci. Adv. 9 7131
[25] Carleo G and Troyer M 2017 Science 355 602-6
[26] Sun Y, Sun C W, Zhou W, Yang R, Duan J C, Gong Y X, Xu P and Zhu S N 2023 Chin. Phys. B 32 080308
[27] Fedrizzi A, Herbst T, Poppe A, Jennewein T and Zeilinger A 2007 Opt. Express 15 15377
[28] Kim T, Fiorentino M and Wong F N C 2006 Phys. Rev. A 73 012316
[29] Sun Q C, Jiang Y F, Bai B, Zhang W, Li H, Jiang X, Zhang J, You L, Chen X, Wang Z, Zhang Q, Fan J and Pan J W 2019 Nat. Photonics 13 687
[30] Gómez S, Mattar A, Machuca I, Gómez E S, Cavalcanti D, Farías O J, Acín A and Lima G 2019 Phys. Rev. A 99 032108
[31] Hu M J, Zhou Z Y, Hu X M, Li C F, Guo G C and Zhang Y S 2018 npj Quantum Inf. 4 63
[32] Gómez E S, Gómez S, González P, Canas G, Barra J F, Delgado A, Xavier G B, Cabello A, Kleinmann M, Vértesi T and Lima G 2016 Phys. Rev. Lett. 117 260401
[33] Huang Y, Li Y, Qi Z, Yang Y, Zheng Y and Chen X 2023 Quantum Front. 2 4
[34] Li J Y, Fang X X, Zhang T, Tabia G N M, Lu H and Liang Y C 2021 Phys. Rev. Res. 3 023045
[35] Liu H Y, Tian X H, Gu C, Fan P, Ni X, Yang R, Zhang J N, Hu M, Guo J, Cao X, Hu X, Zhao G, Lu Y Q, Gong Y X, Xie Z and Zhu S N 2020 Natl. Sci. Rev. 7 921
[36] Sun K, Hao Z Y, Wang Y, Li J K, Xu X Y, Xu J S, Han Y J, Li C F and Guo G C 2022 Light Sci. Appl. 11 203
[37] Wu T, Izaac J A, Li Z-X, Wang K, Chen Z-Z, Zhu S, Wang J B and Ma X-S 2020 Phys. Rev. Lett. 125 240501
[38] Hsu K Y, Li H Y and Psaltis D 1990 Proc. IEEE 78 1637
[39] Lewenstein M and Nowak A 1990 Phys. Rev. Lett. 62 225
[40] Srivastava N, Hinton G, Krizhevsky A, Sutskever I and Salakhutdinov R 2014 Mach. Learn. Res. 15 1929

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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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