Discovering quantum phenomena with Interpretable Machine Learning

TL;DR

This paper presents a framework using interpretable machine learning, specifically variational autoencoders combined with symbolic methods, to discover and interpret quantum phenomena from diverse datasets, revealing new physical insights.

Contribution

It introduces a novel approach that combines interpretable neural models with symbolic analysis to uncover physical laws from quantum data, demonstrated on multiple experimental and simulated datasets.

Findings

Learned representations reveal quantum phase space structures

Discovered a corner-ordering pattern in Rydberg arrays

Provided a general, open-source framework for quantum data analysis

Abstract

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.