Identifying Topological Phase Transitions in Experiments Using Manifold Learning

We demonstrate the identification of topological phase transitions from experimental data using diffusion maps: a nonlocal unsupervised machine learning method. We analyze experimental data from an optical system undergoing a topological phase transition and demonstrate the ability of this approach...

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Bibliographic Details
Published in:Physical review letters 2020-09, Vol.125 (12), p.1-127401, Article 127401
Main Authors: Lustig, Eran, Yair, Or, Talmon, Ronen, Segev, Mordechai
Format: Article
Language:English
Subjects:
Machine learning
Manifolds (mathematics)
Phase transitions
Topology
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Summary:We demonstrate the identification of topological phase transitions from experimental data using diffusion maps: a nonlocal unsupervised machine learning method. We analyze experimental data from an optical system undergoing a topological phase transition and demonstrate the ability of this approach to identify topological phase transitions even when the data originates from a small part of the system, and does not even include edge states.
ISSN:0031-9007
1079-7114
DOI:10.1103/PhysRevLett.125.127401