Unsupervised Learning of Rydberg Atom Array Phase Diagram with Siamese Neural Networks
This provides a tool for physicists to explore unknown phases of matter without prior knowledge, though it is incremental as it builds on existing neural network techniques.
The researchers tackled the problem of detecting phase boundaries in complex systems like Rydberg atom arrays by introducing an unsupervised method based on Siamese Neural Networks, which successfully identified phase boundaries consistent with prior research in Monte-Carlo simulations of Ising-type systems and Rydberg atom arrays.
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.