Machine learning Z2 quantum spin liquids with quasiparticle statistics

Yi Zhang, Roger G. Melko, Eun Ah Kim

Research output: Contribution to journalArticlepeer-review

98 Scopus citations


After decades of progress and effort, obtaining a phase diagram for a strongly correlated topological system still remains a challenge. Although in principle one could turn to Wilson loops and long-range entanglement, evaluating these nonlocal observables at many points in phase space can be prohibitively costly. With growing excitement over topological quantum computation comes the need for an efficient approach for obtaining topological phase diagrams. Here we turn to machine learning using quantum loop topography (QLT), a notion we have recently introduced. Specifically, we propose a construction of QLT that is sensitive to quasiparticle statistics. We then use mutual statistics between the spinons and visons to detect a Z2 quantum spin liquid in a multiparameter phase space. We successfully obtain the quantum phase boundary between the topological and trivial phases using a simple feed-forward neural network. Furthermore, we demonstrate advantages of our approach for the evaluation of phase diagrams relating to speed and storage. Such statistics-based machine learning of topological phases opens new efficient routes to studying topological phase diagrams in strongly correlated systems.

Original languageEnglish
Article number245119
JournalPhysical Review B
Issue number24
StatePublished - 13 Dec 2017

Bibliographical note

Publisher Copyright:
© 2017 American Physical Society.


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