Deep Learning and Holographic QCD (1809.10536v1)

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.

Overview of "Deep Learning and Holographic QCD"

The paper "Deep Learning and Holographic QCD," authored by Koji Hashimoto et al., explores an innovative intersection between deep learning methodologies and gauge/gravity duality, particularly applying the AdS/CFT correspondence to quantum chromodynamics (QCD). The research seeks to unravel the complexities of holographic QCD by leveraging the capabilities of deep learning to address the inverse problem inherent in model-building: determining bulk gravity metrics from high-dimensional data reflective of boundary field theory phenomena.

Deep Learning and Holographic Modelling

The authors utilize a lattice QCD dataset that encapsulates information about the chiral condensate at finite temperatures. In this approach, deep learning creates a bridge by treating the bulk metric in AdS space as the weights of a neural network. Such a framework allows for deriving bulk geometry from boundary conditions of quantum field theories, addressing the issue typically circumvented by traditional phenomenological models that require pre-assumed bulk geometries.

Emergent Metric and Phase Transition

The paper presents results wherein the emergent metric obtained through deep learning reveals an unexpected structure — it possesses a finite-height infrared (IR) wall alongside a black hole horizon in the bulk space. This unique structure elucidates both confining and deconfining phases simultaneously, signaling the cross-over nature of thermal phase transitions in real-world QCD. The generated bulk metric, therefore, encodes both phases, enabling the holographic modeling to predict quark antiquark potential that blends linear confinement with Debye screening, as observed in lattice QCD data.

Implications for Chiral Symmetry and Quark Confinement

The authors explore the discrepancies between chiral symmetry breaking and quark confinement in holographic QCD, proposing that the bulk metric's structure could inherently give rise to both phenomena, although not all properties are realized simultaneously due to specific conditions (such as temperature). Their findings raise poignant discussions on the relationship, or lack thereof, between these two fundamental aspects of QCD.

Speculations on AI and Holographic Duality

The research posits significant implications for future theoretical and practical AI applications, suggesting the deep learning methodologies herein can offer profound insights into gravitational degrees of freedom and spatial reconstructions of bulk space through data. This could further illuminate the validity and multiplicities of the AdS/CFT correspondence in strongly coupled systems beyond QCD.

Conclusion

The paper demonstrates how machine learning can aid in modeling complex phenomena in theoretical physics, offering a new vantage point into holography and bulk geometry emergence from quantum field theories. The novel findings of Hashimoto et al. invite further exploration into machine learning's role in understanding non-perturbative aspects of QCD and related gauge theories, prompting discussions on how this approach might address broader challenges in theoretical physics and computational sciences.