Machine learning in phase transition analysis of lattice quantum gravity

2025/10/02 by Jan Ambjorn, Ambjorn, Jan, Zbigniew Drogosz +9
#hep-lat #hep-th

paper · pdf · doi:10.48550/arxiv.2510.02159

Abstract

Using numerical data coming from Monte Carlo simulations of four-dimensional Causal Dynamical Triangulations, we study how automated machine learning algorithms can be used to recognize transitions between different phases of quantum geometries observed in lattice quantum gravity. We tested seven supervised and seven unsupervised machine learning models and found that most of them were very successful in that task, even outperforming standard methods based on order parameters.

Citations

• Is Lattice Quantum Gravity Asymptotically Safe? Making contact between Causal Dynamical Triangulations and the Functional Renormalization Group
• CDT Quantum Toroidal Spacetimes: An Overview
• Renormalization in quantum theories of geometry

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