2019/05/08 by Wenqian Lian, Sheng-Tao Wang, Sirui Lu +11 · 2 citations
Physics and Astronomy · #cond-mat.dis-nn #cond-mat.mes-hall #quant-ph
paper · pdf · doi:10.1103/physrevlett.122.210503
published as Phys. Rev. Lett. 122, 210503 (2019) · Main text: 5 pages with 3 figures; supplemental materials: 8 pages with 4 figures and 2 tables; accepted at Physical Review Letters
arxiv created 2019/05/08 · arxiv updated 2019/06/04
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.