Unsupervised learning of Rydberg atom array phase diagram with Siamese neural networks

Unsupervised learning of Rydberg atom array phase diagram with Siamese neural networks

We introduce an unsupervised machine learning method based on Siamese neural networks (SNNs) to detect phase boundaries. This method is applied to Monte-Carlo simulations of Ising-type systems and Rydberg atom arrays. In both cases the SNN reveals phase boundaries consistent with prior research. The...

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Bibliographic Details
Main Authors: Zakaria Patel, Ejaaz Merali, Sebastian J Wetzel
Format: Article
Language:English
Published: IOP Publishing 2022-01-01
Series:New Journal of Physics
Subjects:
artificial neural networks
phase transitions
Ising model
Rydberg array
Online Access:https://doi.org/10.1088/1367-2630/ac9c7a

Internet

https://doi.org/10.1088/1367-2630/ac9c7a

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