Decoding conformal field theories: from supervised to unsupervised learning

TL;DR

This paper demonstrates how machine learning techniques, both supervised and unsupervised, can classify and analyze conformal field theories and critical points in strongly correlated systems using spectral and entanglement data.

Contribution

The study introduces novel machine learning applications for classifying conformal field theories and identifying critical points from spectral and entanglement data, including the use of autoencoders for hidden variable discovery.

Findings

Machine learning accurately predicts critical points from energy spectra.

Supervised learning identifies conformal field theories from Rényi entropies.

Autoencoders find variables correlated with the central charge.

Abstract

We use machine learning to classify rational two-dimensional conformal field theories. We first use the energy spectra of these minimal models to train a supervised learning algorithm. We find that the machine is able to correctly predict the nature and the value of critical points of several strongly correlated spin models using only their energy spectra. This is in contrast to previous works that use machine learning to classify different phases of matter, but do not reveal the nature of the critical point between phases. Given that the ground-state entanglement Hamiltonian of certain topological phases of matter is also described by conformal field theories, we use supervised learning on R\'{e}yni entropies and find that the machine is able to identify which conformal field theory describes the entanglement Hamiltonian with only the lowest few R\'{e}yni entropies to a high degree of accuracy. Finally, using autoencoders, an unsupervised learning algorithm, we find a hidden variable that has a direct correlation with the central charge and discuss prospects for using machine learning to investigate other conformal field theories, including higher-dimensional ones. Our results highlight that machine learning can be used to find and characterize critical points and also hint at the intriguing possibility to use machine learning to learn about more complex conformal field theories.