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

TL;DR

This paper presents an unsupervised Siamese Neural Network approach to detect phase boundaries in Monte Carlo simulations of Ising systems and Rydberg atom arrays, revealing known phase boundaries and enabling exploration of new phases.

Contribution

The paper introduces a novel unsupervised learning method using Siamese Neural Networks for phase boundary detection in complex quantum systems.

Findings

Successfully detects phase boundaries consistent with prior research

Applies to Rydberg atom arrays and Ising models

Enables exploration of unknown phases of matter

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.