Bulletin of the American Physical Society
APS March Meeting 2021
Volume 66, Number 1
Monday–Friday, March 15–19, 2021; Virtual; Time Zone: Central Daylight Time, USA
Session L21: Machine Learning for Quantum Matter V
8:00 AM–10:48 AM,
Wednesday, March 17, 2021
Sponsoring Units: DCOMP GDS DMP
Chair: Emine Kucukbenli, Harvard University
Abstract: L21.00006 : Topological quantum phase transitions retrieved through unsupervised machine learning*
9:48 AM–10:00 AM
(RIKEN; and University of Michigan)
Here we show with several prototypical and relevant models that topological quantum phase transitions can indeed be automatically retrieved, with unsupervised machine learning, and requiring only a very limited number of hyperparameters. Inspired by the non-Euclidean structure of the data set as well as the concept of manifold learning, we argue that the widely used choice of a Euclidean distance may in general be suboptimal to discover topological transitions in momentum space. On the other hand, we can show that the Chebyshev distance sharpens the characteristic features of topological transitions, and thus decisively supports the retrieval of the critical points. Implications and demonstrations for learning in real space will also be provided.
Reference: Y. Che, C. Gneiting, T. Liu, F. Nori, arXiv:2002.02363 (to appear in Physical Review B).
*This work was supported in part by NTT, JST, CREST, JSPS, ARO, AOARD, FQXi.
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