Unsupervised Learning of Rydberg Atom Array Phase Diagram with Siamese Neural Networks

Zakaria Patel; Ejaaz Merali; Sebastian J. Wetzel

doi:10.1088/1367-2630/ac9c7a

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arXiv · 2205.04051

Unsupervised Learning of Rydberg Atom Array Phase Diagram with Siamese Neural Networks

Zakaria Patel·Ejaaz Merali·Sebastian J. Wetzel

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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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physics.comp-ph cond-mat.quant-gas cs.LG quant-ph

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BibTeX RIS

Zakaria Patel, Ejaaz Merali, Sebastian J. Wetzel. 2022-05-09. Unsupervised Learning of Rydberg Atom Array Phase Diagram with Siamese Neural Networks. https://doi.org/10.1088/1367-2630/ac9c7a

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