arXiv:1905.03255 · Physical Review Letters

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, Dong-Ling Deng, Luming Duan

Published May 31, 2019• 67 citations

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Abstract

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.

DOI: 10.1103/physrevlett.122.210503

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