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arXiv · 2104.03607

Identifying Topological Phase Transitions in Experiments Using Manifold Learning

Eran Lustig·Or Yair·Ronen Talmon·Mordechai Segev
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Abstract

We demonstrate the identification and classification 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.

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Eran Lustig, Or Yair, Ronen Talmon, Mordechai Segev. 2021-04-08. Identifying Topological Phase Transitions in Experiments Using Manifold Learning. https://doi.org/10.1103/physrevlett.125.127401

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