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Chin. Phys. B, 2025, Vol. 34(1): 017103    DOI: 10.1088/1674-1056/ad8cb9
CONDENSED MATTER: ELECTRONIC STRUCTURE, ELECTRICAL, MAGNETIC, AND OPTICAL PROPERTIES Prev   Next  

Classifying extended, localized and critical states in quasiperiodic lattices via unsupervised learning

Bohan Zheng(郑博涵)1,†, Siyu Zhu(朱思宇)2,†, Xingping Zhou(周兴平)2,‡, and Tong Liu(刘通)3,§
1 School of Computer Science and Technology, School of Software, Nanjing University of Posts and Telecommunications, Nanjing 210023, China;
2 Institute of Quantum Information and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;
3 Department of Applied Physics, School of Science, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Abstract  Classification of quantum phases is one of the most important areas of research in condensed matter physics. In this work, we obtain the phase diagram of one-dimensional quasiperiodic models via unsupervised learning. Firstly, we choose two advanced unsupervised learning algorithms, namely, density-based spatial clustering of applications with noise (DBSCAN) and ordering points to identify the clustering structure (OPTICS), to explore the distinct phases of the Aubry-André-Harper model and the quasiperiodic p-wave model. The unsupervised learning results match well with those obtained through traditional numerical diagonalization. Finally, we assess similarity across different algorithms and find that the highest degree of similarity between the results of unsupervised learning algorithms and those of traditional algorithms exceeds 98%. Our work sheds light on applications of unsupervised learning for phase classification.
Keywords:  quantum phase      quasiperiodic      machine learning  
Received:  31 May 2024      Revised:  19 October 2024      Accepted manuscript online:  30 October 2024
PACS:  71.23.Ft (Quasicrystals)  
  71.10.Fd (Lattice fermion models (Hubbard model, etc.))  
  71.23.An (Theories and models; localized states)  
Fund: Project supported by the Natural Science Foundation of Nanjing University of Posts and Telecommunications (Grant Nos. NY223109, NY220119, and NY221055), China Postdoctoral Science Foundation (Grant No. 2022M721693), and the National Natural Science Foundation of China (Grant No. 12404365).
Corresponding Authors:  Xingping Zhou, Tong Liu     E-mail:  zxp@njupt.edu.cn;t6tong@njupt.edu.cn

Cite this article: 

Bohan Zheng(郑博涵), Siyu Zhu(朱思宇), Xingping Zhou(周兴平), and Tong Liu(刘通) Classifying extended, localized and critical states in quasiperiodic lattices via unsupervised learning 2025 Chin. Phys. B 34 017103

[1] Anderson P W 1958 Phys. Rev. 109 1492
[2] Segev M, Silberberg Y and Christodoulides D N 2013 Nat. Photon. 7 197
[3] Lahini Y, Pugatch R, Pozzi F, Sorel M, Morandotti R, Davidson N and Silberberg Y 2009 Phys. Rev. Lett. 103 013901
[4] Roati G, D’Errico C, Fallani L, Fattori M, Fort C, Zaccanti M, Modugno G, Modugno M and Inguscio M 2008 Nature 453 895
[5] Abrahams E, Anderson P W, Licciardello D C and Ramakrishnan T V 1979 Phys. Rev. Lett. 42 673
[6] Mott N 1987 J. Phys. C 20 3075
[7] Soukoulis C M and Economou E N 1982 Phys. Rev. Lett. 48 1043
[8] Buchler H P, Blatter G and Zwerger W 2003 Phys. Rev. Lett. 90 130401
[9] Aubry S and André G 1980 Ann. Isr. Phys. Soc. 3 18
[10] Harper P G 1955 Proc. Phys. Soc. Sect. A 68 874
[11] Liu T, Guo H, Pu Y and Longhi S 2020 Phys. Rev. B 102 024205
[12] Xia X, Huang K, Wang S and Li X 2022 Phys. Rev. B 105 014207
[13] Cai X 2022 Phys. Rev. B 106 214207
[14] Zhou L and Han W 2021 Chin. Phys. B 30 100308
[15] Wang L, Wang Z B and Chen S 2024 Phys. Rev. B 110 L060201
[16] Liu Y, Jiang X P, Cao J and Chen S 2020 Phys. Rev. B 101 174205
[17] Xu Z, Xia X and Chen S 2021 Phys. Rev. B 104 224204
[18] Li S Z, Cheng E, Zhu S L and Li Z 2024 Phys. Rev. B 110 134203
[19] Li S Z and Li Z 2024 Phys. Rev. B 110 L041102
[20] Zhang D W, Chen Y L, Zhang G Q, Lang L J, Li Z and Zhu S L 2020 Phys. Rev. B 101 235150
[21] Jiang S L, Liu Y and Lang L J 2023 Chin. Phys. B 32 097204
[22] Wang Y, Xia X, Zhang L, Yao H, Chen S, You J, Zhou Q and Liu X J 2020 Phys. Rev. Lett. 125 196604
[23] Zhou X C, Wang Y, Poon T F J, Zhou Q and Liu X J 2023 Phys. Rev. Lett. 131 176401
[24] Biddle J and Das Sarma S 2010 Phys. Rev. Lett. 104 070601
[25] Longhi S 2019 Phys. Rev. B 100 125157
[26] Zhou L W 2024 Phys. Rev. B 109 024204
[27] Zeng Q B, Yang Y B and Xu Y 2020 Phys. Rev. B 101 020201
[28] Lin X, Chen X, Guo G C and Gong M 2023 Phys. Rev. B 108 174206
[29] Lin X and Gong M 2024 Phys. Rev. A 109 033310
[30] Jiang H, Lang L, Yang C, Zhu S L and Chen S 2019 Phys. Rev. B 100 054301
[31] Beveridge C, Hart K, Cristani C R, Li X, Barbierato E and Hsu Y T 2024 arXiv:2407.06253[cond-mat.dis-nn]
[32] Vanoni C and Vitale V 2024 Phys. Rev. B 110 024204
[33] Scheurer M S and Slager R J 2020 Phys. Rev. Lett. 124 226401
[34] Hetényi B and Cengiz S 2022 Phys. Rev. B 106 195151
[35] Liu T and Xia X 2024 Chin. Phys. Lett. 41 017102
[36] Jordan M I and Mitchell T M 2015 Science 349 255
[37] Carleo G, Cirac I, Cranmer K, Daudet L, Schuld M, Tishby N, VogtMaranto L and Zdeborová L 2019 Rev. Mod. Phys. 91 045002
[38] Ma W, Liu Z, Kudyshev Z A, Boltasseva A, Cai W and Liu Y 2021 Nat. Photon. 15 77
[39] Carrasquilla J and Torlai G 2021 PRX Quantum 2 040201
[40] Lu S, Gao X and Duan L M 2019 Phys. Rev. B 99 155136
[41] Schmitt M and Heyl M 2020 Phys. Rev. Lett. 125 100503
[42] Gubernatis J E and Lookman T 2018 Phys. Rev. Materials 2 120301
[43] Park S, Hwang Y and Yang B 2022 Phys. Rev. B 105 195115
[44] Zhang Y and Kim E A 2017 Phys. Rev. Lett. 118 216401
[45] Bai S C, Tang Y C and Ran S J 2022 Chin. Phys. Lett. 39 100701
[46] Zhang P, Shen H and Zhai H 2018 Phys. Rev. Lett. 120 066401
[47] Ellis K, Solar-Lezama A and Josh T 2015 Advances in Neural Information Processing Systems (Montreal: Neural Information Processing Systems Foundation) pp. 973-981
[48] Venderley J, Khemani V and Kim E A 2018 Phys. Rev. Lett. 120 257204
[49] Hsu Y T, Li X, Deng D L and Das Sarma S 2018 Phys. Rev. Lett. 121 245701
[50] Ch’Ng K, Carrasquilla J, Melko R G and Khatami E 2017 Phys. Rev. X 7 031038
[51] Hu W, Singh R R and Scalettar R T 2017 Phys. Rev. E 95 062122
[52] Ahmed A, Nelson A, Raina A and Sharma A 2023 Phys. Rev. B 108 155128
[53] Bai X D, Zhao J, Han Y Y, Zhao J C and Wang J G 2021 Phys. Rev. B 103 134203
[54] Yao H, Khoudli A, Bresque L and Sanchez-Palencia L 2019 Phys. Rev. Lett. 123 070405
[55] Ester M, Kriegel H P, Sander J and Xu X W 1996 Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96) (Portland: AAAI Press) pp. 226-231
[56] Ankerst M, Breunig M M, Kriegel H P and Sander J 1999 Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data (New York: Association for Computing Machinery) pp. 49-60
[57] Lu Z, Xu Z and Zhang Y B 2022 Ann. Phys. (Berlin) 534 2200203
[58] Wei X B, Wu L Q, Feng K W, Liu T and Zhang Y B 2024 Phys. Rev. A 109 023314
[59] Lin X, Guo G C and Gong M 2023 arXiv:2311.08643[cond-mat]
[60] Liu T, Cheng S, Zhang R, Ruan R and Jiang H 2022 Chin. Phys. B 31 027101
[61] Liu T, Xia X, Longhi S and Sanchez-Palencia L 2022 SciPost Phys. 12 027
[62] Jiang X P, Zeng W L, Hu Y Y and Pan L 2024 arXiv:2409.03591[condmat]
[63] Liu T, Cheng S, Guo H and Xianlong G 2021 Phys. Rev. B 103 104203
[64] Wang J, Liu X J, Xianlong G and Hu H 2016 Phys. Rev. B 93 104504
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