Unsupervised identification of topological phase transitions using predictive models

Greplova, Eliska and Valenti, Agnes and Boschung, Gregor and Schäfer, Frank and Lörch, Niels and Huber, Sebastian D.. (2020) Unsupervised identification of topological phase transitions using predictive models. New Journal of Physics, 22. 045003.

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Official URL: https://edoc.unibas.ch/80353/

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Machine-learning driven models have proven to be powerful tools for the identification of phases of matter. In particular, unsupervised methods hold the promise to help discover new phases of matter without the need for any prior theoretical knowledge. While for phases characterized by a broken symmetry, the use of unsupervised methods has proven to be successful, topological phases without a local order parameter seem to be much harder to identify without supervision. Here, we use an unsupervised approach to identify boundaries of the topological phases. We train artificial neural nets to relate configurational data or measurement outcomes to quantities like temperature or tuning parameters in the Hamiltonian. The accuracy of these predictive models can then serve as an indicator for phase transitions. We successfully illustrate this approach on both the classical Ising gauge theory as well as on the quantum ground state of a generalized toric code.
Faculties and Departments:05 Faculty of Science > Departement Physik > Physik > Theoretische Physik (Bruder)
UniBasel Contributors:Schäfer, Frank
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:IOP Publishing
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:14 Apr 2021 12:10
Deposited On:14 Apr 2021 12:10

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