| CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY |
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Predicting Quantum Many-Body Dynamics with Transferable Neural Networks |
| Ze-Wang Zhang1, Shuo Yang2, Yi-Hang Wu1, Chen-Xi Liu1, Yi-Min Han1, Ching-Hua Lee3,4, Zheng Sun1, Guang-Jie Li1, Xiao Zhang1** |
1School of Physics, Sun Yat-sen University, Guangzhou 510275
2State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics, Tsinghua University, Beijing 100084
3Department of Physics, National University of Singapore, 117542, Singapore
4Institute of High Performance Computing, 138632, Singapore
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| Cite this article: |
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Ze-Wang Zhang, Shuo Yang, Yi-Hang Wu et al 2020 Chin. Phys. Lett. 37 018401 |
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Abstract Advanced machine learning (ML) approaches such as transfer learning have seldom been applied to approximate quantum many-body systems. Here we demonstrate that a simple recurrent unit (SRU) based efficient and transferable sequence learning framework is capable of learning and accurately predicting the time evolution of the one-dimensional (1D) Ising model with simultaneous transverse and parallel magnetic fields, as quantitatively corroborated by relative entropy measurements between the predicted and exact state distributions. At a cost of constant computational complexity, a larger many-body state evolution is predicted in an autoregressive way from just one initial state, without any guidance or knowledge of any Hamiltonian. Our work paves the way for future applications of advanced ML methods in quantum many-body dynamics with knowledge only from a smaller system.
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Received: 24 October 2019
Published: 23 December 2019
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| PACS: |
84.35.+i
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(Neural networks)
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05.50.+q
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(Lattice theory and statistics)
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02.70.-c
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(Computational techniques; simulations)
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| Fund: Supported by the National Natural Science Foundation of China under Grant Nos 11874431 and 11804181, the National Key R&D Program of China under Grant No 2018YFA0306800, and the Guangdong Science and Technology Innovation Youth Talent Program under Grant Nos 2016TQ03X688 and 2018YFA0306504, and the Research Fund Program of the State Key Laboratory of Low-Dimensional Quantum Physics under Grant No ZZ201803. |
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| [1] | Bengio Y, Courville A and Vincent P 2013 IEEE Trans. Pattern Anal. Mach. Intell. 35 1798 | | [2] | Krizhevsky A, Sutskever I and Hinton G E 2017 Commun. ACM 60 84 | | [3] | Gatys L A, Ecker A S and Bethge M 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) p 2414 | | [4] | Van Den Oord A, Dieleman S and Zen H 2016 CoRR abs/1609.03499 | | [5] | Sun Z, Liu J and Zhang Z 2018 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) p 1864 | | [6] | Wu Y, Schuster M and Chen Z 2016 arXiv:1609.08144 | | [7] | Voulodimos A, Doulamis N, Doulamis A and Protopapadakis E 2018 Comput. Intell. Neurosci. 2018 | | [8] | Deng L, Hinton G and Kingsbury B 2013 2013 IEEE International Conference on Acoustics, Speech and Signal Processing p 8599 | | [9] | Van Nieuwenburg E P, Liu Y H and Huber S D 2017 Nat. Phys. 13 435 | | [10] | Cai Z and Liu J 2018 Phys. Rev. B 97 035116 | | [11] | Torlai G and Melko R G 2016 Phys. Rev. B 94 165134 | | [12] | Torlai G and Melko R G 2017 Phys. Rev. Lett. 119 030501 | | [13] | Deng D L, Li X and Sarma S D 2017 Phys. Rev. X 7 021021 | | [14] | Gray J, Banchi L, Bayat A and Bose S 2018 Phys. Rev. Lett. 121 150503 | | [15] | Ch'ng K, Carrasquilla J, Melko R G and Khatami E 2017 Phys. Rev. X 7 031038 | | [16] | Broecker P, Carrasquilla J, Melko R G and Trebst S 2017 Sci. Rep. 7 8823 | | [17] | Amin M H, Andriyash E, Rolfe J, Kulchytskyy B and Melko R 2018 Phys. Rev. X 8 021050 | | [18] | Zhang Y and Kim E A 2017 Phys. Rev. Lett. 118 216401 | | [19] | Carleo G and Troyer M 2017 Science 355 602 | | [20] | Pan S J and Yang Q 2009 IEEE Trans. Knowl. Data Eng. 22 1345 | | [21] | Torrey L and Shavlik J 2010 Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques (IGI Global) p 242 | | [22] | Gao X and Duan L M 2017 Nat. Commun. 8 662 | | [23] | Sharir O, Levine Y, Wies N, Carleo G and Shashua A 2019 arXiv:1902.04057 | | [24] | Carleo G, Nomura Y and Imada M 2018 Nat. Commun. 9 5322 | | [25] | Lei T, Zhang Y, Wang S I, Dai H and Artzi Y 2018 Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing p 4470 | | [26] | Graves A and Jaitly N 2014 International Conference on Machine Learning p 1764 | | [27] | Pan S J, Tsang I W, Kwok J T and Yang Q 2010 IEEE Trans. Neural Netw. 22 199 | | [28] | Glorot X, Bordes A and Bengio Y 2011 Proceedings of the 28th International Conference on Machine Learning (ICML-11) p 513 | | [29] | Sutskever I, Vinyals O and Le Q V 2014 Advances in Neural Information Processing Systems p 3104 | | [30] | Williams R J and Zipser D 1989 Neural Comput. 1 270 | | [31] | Kingma D P and Ba J 2014 arXiv:1412.6980 | | [32] | Vedral V 2002 Rev. Mod. Phys. 74 197 | | [33] | Hinton G, Vinyals O and Dean J 2015 arXiv:1503.02531 | | [34] | Kingma D P, Salimans T and Jozefowicz R 2016 Advances in Neural Information Processing Systems p 4743 |
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