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Variational data encoding and correlations in quantum-enhanced machine learning |
| Ming-Hao Wang(王明浩)1,† and Hua Lü(吕桦)2,‡ |
1 School of Physics, Hubei University, Wuhan 430062, China;
2 School of Science, Hubei University of Technology, Wuhan 430068, China |
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Abstract Leveraging the extraordinary phenomena of quantum superposition and quantum correlation, quantum computing offers unprecedented potential for addressing challenges beyond the reach of classical computers. This paper tackles two pivotal challenges in the realm of quantum computing: firstly, the development of an effective encoding protocol for translating classical data into quantum states, a critical step for any quantum computation. Different encoding strategies can significantly influence quantum computer performance. Secondly, we address the need to counteract the inevitable noise that can hinder quantum acceleration. Our primary contribution is the introduction of a novel variational data encoding method, grounded in quantum regression algorithm models. By adapting the learning concept from machine learning, we render data encoding a learnable process. This allowed us to study the role of quantum correlation in data encoding. Through numerical simulations of various regression tasks, we demonstrate the efficacy of our variational data encoding, particularly post-learning from instructional data. Moreover, we delve into the role of quantum correlation in enhancing task performance, especially in noisy environments. Our findings underscore the critical role of quantum correlation in not only bolstering performance but also in mitigating noise interference, thus advancing the frontier of quantum computing.
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Received: 16 March 2024
Revised: 23 June 2024
Accepted manuscript online: 27 June 2024
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PACS:
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03.67.-a
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(Quantum information)
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03.67.Ac
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(Quantum algorithms, protocols, and simulations)
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03.65.Ud
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(Entanglement and quantum nonlocality)
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| Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 12105090 and 12175057). |
Corresponding Authors:
Ming-Hao Wang, Hua Lu
E-mail: wangmh@hubu.edu.cn;lvhuahg@163.com
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Cite this article:
Ming-Hao Wang(王明浩) and Hua Lü(吕桦) Variational data encoding and correlations in quantum-enhanced machine learning 2024 Chin. Phys. B 33 090307
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[1] Nielsen M A and Chuang I L 2010 Quantum computation and quantum information: 10th anniversary edition (Cambridge University Press)
[2] Shor P W 1997 SIAM J. Comput. 26 1484
[3] Harrow A W, Hassidim A and Lloyd S 2009 Phys. Rev. Lett. 103 150502
[4] Preskill J 2018 Quantum 2 79
[5] Bharti K, Cervera-Lierta A, Kyaw T H, Haug T,Alperin-Lea S, Anand A, Degroote M, Heimonen H, Kottmann J S, Menke T, Mok W K, Sim S, Kwek L and Aspuru-Guzik A 2022 Rev. Mod. Phys. 94 015004
[6] Callison A and Chancellor N 2022 Phys. Rev. A 106 010101
[7] Cerezo M, Arrasmith A, Babbush R, Benjamin S C, Endo S, Fujii K, McClean J R, Mitarai K, Yuan X, Cincio L and Coles P J 2021 Nat. Rev. Phys. 3 625
[8] Zhang S X, Wan Z Q, Lee C K, Hsieh C Y, Zhang S and Yao H 2022 Phys. Rev. Lett. 128 120502
[9] Jones T, Endo S, McArdle S, Yuan X and Benjamin S C 2019 Phys. Rev. A 99 062304
[10] Liu H L, Wu Y S, Wan L C, Pan S J, Qin S J, Gao F and Wen Q Y 2021 Phys. Rev. A 104 022418
[11] Xu X, Sun J, Endo S, Li Y, Benjamin S C and Yuan X 2021 Sci. Bull. 66 2181
[12] Kandala A, Mezzacapo A, Temme K, Takita M, Brink M, Chow J M and Gambetta J M 2017 Nature 549 242
[13] Chalumuri A, Kune R and Manoj B S 2021 Quantum Inf. Process. 20 119
[14] Jordan M I and Mitchell T M 2015 Science 349 255
[15] Benedetti M, Lloyd E, Sack S and Fiorentini M 2019 Quantum Sci. Technol. 4 043001
[16] Du Y, Hsieh M H, Liu T and Tao D 2020 Phys. Rev. Res. 2 033125
[17] Nghiem N A, Chen S Y C and Wei T C 2021Phys. Rev. Res. 3 033056
[18] LaRose R and Coyle B 2020 Phys. Rev. A 102 032420
[19] Schuld M, Bocharov A, Svore K M and Wiebe N 2020 Phys. Rev. A 101 032308
[20] Dallaire-Demers P L and Killoran N 2018 Phys. Rev. A 98 012324
[21] Lloyd S and Weedbrook C 2018 Phys. Rev. Lett. 121 040502
[22] Gao X, Anschuetz E R, Wang S T, Cirac J I and Lukin M D 2022 Phys. Rev. X12 021037
[23] Biamonte J, Wittek P, Pancotti N, Rebentrost P, Wiebe N and Lloyd S 2017 Nature 549 195
[24] Dunjko V, Taylor J M and Briegel H J. 2016 Phys. Rev. Lett. 117 130501
[25] Dunjko V and Briegel H J 2018 Rep. Prog. Phys 81 074001
[26] Ma Y G, Pang L G, Wang R and Zhou K. 2023 Chin. Phys. Lett. 40 122101
[27] Bai S C, Tang Y C and Ran S J 2022 Chin. Phys. Lett. 39 100701
[28] Xie J and Dong Y 2023 Chin. Phys. B 32 120302
[29] Wang X Y, Dong C and Liu X 2024 Chin. Phys. Lett. 41 031201
[30] Havlíček V, Córcoles A D, Temme K, Harrow A W, Kandala A, Chow J M and Gambetta J M 2019 Nature 567 209
[31] Schuld M and Killoran N 2019 Phys. Rev. Lett. 122 040504
[32] Giovannetti, V, Lloyd S and Maccone L 2008 Phys. Rev. Lett. 16 160501
[33] Mitarai K, Negoro M, Kitagawa M and Fujii K 2018 Phys. Rev. A 98 032309
[34] Pérez-Salinas A, Cervera-Lierta A, Gil-Fuster E and Latorre J I 2020 Quantum 4 226
[35] Schuld M, Sweke R and Meyer J J 2021 Phys. Rev. A 103 032430
[36] Jerbi S, Fiderer L J, Nautrup H P, Kübler J M, Briegel H J and Dunjko V 2023 Nat. Commun. 14 517
[37] Hofmann T, Schölkopf B and Smola A J 2008 Ann. Statist. 36 1171
[38] Lloyd S, Schuld M, Ijaz A, Izaac J and Killoran N 2020 arXiv: 2001.03622[quant-ph]
[39] Hubregtsen T, Wierichs D, Gil-Fuster E, Derks P J H S, Faehrmann P K and Meyer J J 2022 Phys. Rev. A 106 042431
[40] Buhrman H, Cleve R, Watrous J and Wolf R. 2001 Phys. Rev. Lett. 87 167902
[41] Garcia-Escartin J C and Chamorro-Posada P 2013 Phys. Rev. A 87 052330
[42] Zhao T H, Wang M H and Zhou B 2021 Quantum Inf. Process. 20 1
[43] Tang H L, Shkolnikov V O, Barron G S, Grimsley H R, Mayhall N J, Barnes E and Economou S E 2021 PRX Quantum 2 020310
[44] Du Y, Huang T, You S, et al. 2022 npj Quantum Inf. 8 62
[45] Guo X, Muta T and Zhao J 2024 arXiv: 2405.05021[quant-ph]
[46] Schuld M, Bergholm V, Gogolin C, Izaac J and Killoran N 2019 Phys. Rev. A 99 032331
[47] Kingma D and Ba J 2014 arXiv: 1412.6980[cs.LG]
[48] Bergholm V, Izaac J, Schuld M, et al. 2018 arXiv: 1811.04968[quantph].
[49] Antipov A V, Kiktenko E O and Fedorov A K 2022 PLOS One 17 1
[50] Horodecki R, Horodecki P, Horodecki M and Horodecki K 2009 Rev. Mod. Phys. 81 865-
[51] Vedral V 2014 Nat. Phys. 10 256
[52] Erhard M, Krenn M and Zeilinger A 2020 Nat. Rev. Phys. 2 365
[53] Köhnke S, Agudelo E, Schünemann M, Schlettwein O, Vogel W, Sperling J and Hage B 2021 Phys. Rev. Lett. 126 170404
[54] Modi K, Brodutch A, Cable H, Paterek T and Vedral V 2012 Rev. Mod. Phys. 84 1655
[55] Adesso G, Bromley T R and Cianciaruso M 2016 J. Phys. A: Math. Theor. 49 473001
[56] Fontana E, Fitzpatrick N, Ramo D M, Duncan R and Rungger I 2021 Phys. Rev. A 104 022403 |
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