Deep Learning and Holographic QCD

TL;DR

This paper demonstrates how deep learning can be used to holographically model QCD by deriving bulk geometries from lattice data, capturing key features like phase transitions and quark potentials.

Contribution

It introduces a data-driven holographic approach to QCD using deep learning to determine emergent bulk metrics from lattice data, revealing phase structures and confinement properties.

Findings

Emergent bulk metric exhibits both black hole horizon and IR wall.

Quark-antiquark potential shows linear confinement and Debye screening.

The method provides a novel way to model QCD holographically.

Abstract

We apply the relation between deep learning (DL) and the AdS/CFT correspondence to a holographic model of QCD. Using a lattice QCD data of the chiral condensate at a finite temperature as our training data, the deep learning procedure holographically determines an emergent bulk metric as neural network weights. The emergent bulk metric is found to have both a black hole horizon and a finite-height IR wall, so shares both the confining and deconfining phases, signaling the cross-over thermal phase transition of QCD. In fact, a quark antiquark potential holographically calculated by the emergent bulk metric turns out to possess both the linear confining part and the Debye screening part, as is often observed in lattice QCD. From this we argue the discrepancy between the chiral symmetry breaking and the quark confinement in the holographic QCD. The DL method is shown to provide a novel data-driven holographic modeling of QCD, and sheds light on the mechanism of emergence of the bulk geometries in the AdS/CFT correspondence.