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.
- Received 22 November 2018
- Revised 5 May 2019
DOI:https://doi.org/10.1103/PhysRevLett.122.210503
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