Emergent Mind
Unsupervised Learning of Rydberg Atom Array Phase Diagram with Siamese Neural Networks
(2205.04051)
Published May 9, 2022
in
physics.comp-ph
,
cond-mat.quant-gas
,
cs.LG
,
and
quant-ph
Abstract
We introduce an unsupervised machine learning method based on Siamese Neural Networks (SNN) 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 combination of leveraging the power of feed-forward neural networks, unsupervised learning and the ability to learn about multiple phases without knowing about their existence provides a powerful method to explore new and unknown phases of matter.
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