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

arXiv: https://arxiv.org/abs/2205.04051 · HTML: https://mlsanity.com/paper/2205.04051/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 4 references that are in the map.
- First posted on arXiv 9 May 2022.

## Built on by (most cited first, 6 of 6)

- [Interpretable representation learning of quantum data enabled by probabilistic variational autoencoders](https://mlsanity.com/paper/2506.11982/) (2025)
- [SLIQ: Quantum Image Similarity Networks on Noisy Quantum Computers](https://mlsanity.com/paper/2309.15259/) (2023)
- [Discovering quantum phenomena with Interpretable Machine Learning](https://mlsanity.com/paper/2604.16015/) (2026)
- [Siamese Neural Network for Label-Efficient Critical Phenomena Prediction in 3D Percolation Models](https://mlsanity.com/paper/2507.14159/) (2025)
- [TetrisCNN for interpretable detection of phases of matter from experimental quantum simulator data](https://mlsanity.com/paper/2609.20693/) (2026)
- [Autonomous phase discovery](https://mlsanity.com/paper/2609.33802/) (2026)

## Abstract (arXiv)

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.