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arXiv 1905.03255cond-mat.dis-nncond-mat.mes-hallquant-ph
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Machine Learning Topological Phases with a Solid-state Quantum Simulator

Wenqian Lian, Sheng-Tao Wang, Sirui Lu, Yuanyuan Huang, Fei Wang, Xinxing Yuan, Wengang Zhang, Xiaolong Ouyang, Xin Wang, Xianzhi Huang, Li He, Xiuying Chang, D… 展开作者

Wenqian Lian, Sheng-Tao Wang, Sirui Lu, Yuanyuan Huang, Fei Wang, Xinxing Yuan, Wengang Zhang, Xiaolong Ouyang, Xin Wang, Xianzhi Huang, Li He, Xiuying Chang, Dong-Ling Deng, Lu-Ming Duan

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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