Mapping out phase diagrams with generative classifiers

TL;DR

This paper introduces a generative classifier-based framework for automatically mapping phase diagrams in many-body physics, leveraging probabilistic models to improve accuracy and reduce human intervention.

Contribution

It proposes using generative classifiers rooted in physical probabilistic models for phase diagram mapping, enhancing automation and interpretability over traditional discriminative methods.

Findings

Successfully applied to classical equilibrium systems.

Effective in quantum ground state phase classification.

Requires minimal human supervision.

Abstract

One of the central tasks in many-body physics is the determination of phase diagrams. However, mapping out a phase diagram generally requires a great deal of human intuition and understanding. To automate this process, one can frame it as a classification task. Typically, classification problems are tackled using discriminative classifiers that explicitly model the probability of the labels for a given sample. Here we show that phase-classification problems are naturally suitable to be solved using generative classifiers based on probabilistic models of the measurement statistics underlying the physical system. Such a generative approach benefits from modeling concepts native to the realm of statistical and quantum physics, as well as recent advances in machine learning. This leads to a powerful framework for the autonomous determination of phase diagrams with little to no human supervision that we showcase in applications to classical equilibrium systems and quantum ground states.