Machine Learning Topological Phases with a Solid-State Quantum Simulator
We report an experimental demonstration of a machine learning approach to identify exotic topological phases, with a focus on the three-dimensional chiral topological insulators. We show that the convolutional neural networks-a class of deep feed-forward artificial neural networks with widespread applications in machine learning-can be trained to successfully identify different topological phases protected by chiral symmetry from experimental raw data generated with a solid-state quantum simulator. Our results explicitly showcase the exceptional power of machine learning in the experimental detection of topological phases, which paves a way to study rich topological phenomena with the machine learning toolbox.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2019 |
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| Erschienen: |
2019 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:122 |
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| Enthalten in: |
Physical review letters - 122(2019), 21 vom: 31. Mai, Seite 210503 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Lian, Wenqian [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Revised 09.07.2019 published: Print Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1103/PhysRevLett.122.210503 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM298966972 |
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| 520 | |a We report an experimental demonstration of a machine learning approach to identify exotic topological phases, with a focus on the three-dimensional chiral topological insulators. We show that the convolutional neural networks-a class of deep feed-forward artificial neural networks with widespread applications in machine learning-can be trained to successfully identify different topological phases protected by chiral symmetry from experimental raw data generated with a solid-state quantum simulator. Our results explicitly showcase the exceptional power of machine learning in the experimental detection of topological phases, which paves a way to study rich topological phenomena with the machine learning toolbox | ||
| 650 | 4 | |a Journal Article | |
| 700 | 1 | |a Wang, Sheng-Tao |e verfasserin |4 aut | |
| 700 | 1 | |a Lu, Sirui |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Yuanyuan |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Fei |e verfasserin |4 aut | |
| 700 | 1 | |a Yuan, Xinxing |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Wengang |e verfasserin |4 aut | |
| 700 | 1 | |a Ouyang, Xiaolong |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Xin |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Xianzhi |e verfasserin |4 aut | |
| 700 | 1 | |a He, Li |e verfasserin |4 aut | |
| 700 | 1 | |a Chang, Xiuying |e verfasserin |4 aut | |
| 700 | 1 | |a Deng, Dong-Ling |e verfasserin |4 aut | |
| 700 | 1 | |a Duan, Luming |e verfasserin |4 aut | |
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