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Machine Learning Topological Phases with a Solid-State Quantum Simulator.
- Source :
-
Physical Review Letters . 5/31/2019, Vol. 122 Issue 21, p1-1. 1p. - Publication Year :
- 2019
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *ARTIFICIAL neural networks
*MACHINE learning
*ANIMAL feeds
*TOPOLOGICAL insulators
Subjects
Details
- Language :
- English
- ISSN :
- 00319007
- Volume :
- 122
- Issue :
- 21
- Database :
- Academic Search Index
- Journal :
- Physical Review Letters
- Publication Type :
- Academic Journal
- Accession number :
- 136762996
- Full Text :
- https://doi.org/10.1103/PhysRevLett.122.210503