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Supervised Machine Learning Topological States of One-Dimensional Non-Hermitian Systems |
| Zhuo Cheng1 and Zhenhua Yu1,2* |
1Guangdong Provincial Key Laboratory of Quantum Metrology and Sensing, School of Physics and Astronomy, Sun Yat-Sen University, Zhuhai 519082, China
2State Key Laboratory of Optoelectronic Meterials and Technologies, Sun Yat-Sen University, Guangzhou 510275, China
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| Cite this article: |
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Zhuo Cheng and Zhenhua Yu 2021 Chin. Phys. Lett. 38 070302 |
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Abstract We apply supervised machine learning to study the topological states of one-dimensional non-Hermitian systems. Unlike Hermitian systems, the winding number of such non-Hermitian systems can take half integers. We focus on a non-Hermitian model, an extension of the Su–Schrieffer–Heeger model. The non-Hermitian model maintains the chiral symmetry. We find that trained neuron networks can reproduce the topological phase diagram of our model with high accuracy. This successful reproduction goes beyond the parameter space used in the training process. Through analyzing the intermediate output of the networks, we attribute the success of the networks to their mastery of computation of the winding number. Our work may motivate further investigation on the machine learning of non-Hermitian systems.
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Received: 19 March 2021
Published: 03 July 2021
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| PACS: |
03.65.Vf
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(Phases: geometric; dynamic or topological)
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05.70.Fh
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(Phase transitions: general studies)
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64.70.Tg
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(Quantum phase transitions)
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| Fund: Supported by the Key Area Research and Development Program of Guangdong Province (Grant No. 2019B030330001), the National Natural Science Foundation of China (Grant Nos. 11474179, 11722438, 91736103, and 12074440), and Guangdong Project (Grant No. 2017GC010613). |
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