Unsupervised Learning of Rydberg Atom Array Phase Diagram with Siamese Neural Networks. (arXiv:2205.04051v2 [physics.comp-ph] UPDATED)

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.

arxiv learning networks neural networks physics unsupervised unsupervised learning