Journal
PHYSICAL REVIEW LETTERS
Volume 122, Issue 21, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevLett.122.210503
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Funding
- National key Research and Development Program of China [2016YFA0301902]
- Tsinghua University
- Ministry of Education of China
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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.
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