Unsupervised identification of topological phase transitions using predictive models
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Author / Producer
Date
2020-04-07
Publication Type
Journal Article
ETH Bibliography
yes
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Abstract
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.
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Publication status
published
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Editor
Book title
Journal / series
Volume
22
Pages / Article No.
45003
Publisher
IOP Publishing
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Topological phase transitions; Unsupervised learning; Quantum phase transitions; Topological order; Ising guage theory; Toric code
Organisational unit
08714 - Gruppe Huber
Notes
Funding
771503 - Topological Mechanical Metamaterials (EC)