Discovering quantum phenomena with Interpretable Machine Learning
This work provides a general, automated pipeline for extracting interpretable physical laws from quantum data, benefiting researchers in quantum physics and materials science.
The authors developed an interpretable machine learning framework using variational autoencoders and symbolic methods to automatically discover physical order parameters and previously unreported quantum phenomena (e.g., corner-ordering in Rydberg arrays) from diverse unlabeled quantum datasets.
Interpretable machine learning techniques are becoming essential tools for extracting physical insights from complex quantum data. We build on recent advances in variational autoencoders to demonstrate that such models can learn physically meaningful and interpretable representations from a broad class of unlabeled quantum datasets. From raw measurement data alone, the learned representation reveals rich information about the underlying structure of quantum phase spaces. We further augment the learning pipeline with symbolic methods, enabling the discovery of compact analytical descriptors that serve as order parameters for the distinct regimes emerging in the learned representations. We demonstrate the framework on experimental Rydberg-atom snapshots, classical shadows of the cluster Ising model, and hybrid discrete-continuous fermionic data, revealing previously unreported phenomena such as a corner-ordering pattern in the Rydberg arrays. These results establish a general framework for the automated and interpretable discovery of physical laws from diverse quantum datasets. All methods are available through qdisc, an open-source Python library designed to make these tools accessible to the broader community.