Deep learning and AdS/QCD

Abstract

We propose a deep learning method to build an AdS/QCD model from the data of hadron spectra. A major problem of generic AdS/QCD models is that a large ambiguity is allowed for the bulk gravity metric with which QCD observables are holographically calculated. We adopt the experimentally measured spectra of ρ and a 2 mesons as training data, and perform a supervised machine learning which determines concretely a bulk metric and a dilaton profile of an AdS/QCD model. Our deep learning (DL) architecture is based on the AdS/DL correspondence [K. Hashimoto, S. Sugishita, A. Tanaka, and A. Tomiya, Phys. Rev. D 98, 046019 (2018)] where the deep neural network is identified with the emergent bulk spacetime.

Holographic QCD and magnetic fields

“…Machine learning techniques, e.g. the ones developed for a simpler holographic study in [164], could be useful in this study.…”

Holographic Wilsonian renormalization of a heavy quark moving through a strongly coupled plasma

“…It would be interesting to combine the holographic Wilsonian approach with other phenomenological approaches trying to fit QCD lattice data or experiments. Among these, we have the traditional holographic QCD models where the gravitational action is adjusted [49][50][51][52][53] or, more recently, the application of machine learning [54][55][56][57] and Monte Carlo techniques [58] to constrain the background geometry. In both cases, the holographic description of UV physics is expected to be problematic due to the asymptotic freedom of QCD.…”

Bulk reconstruction of metrics inside black holes by complexity

“…Nevertheless, various methods have been developed to reconstruct bulk spacetime metrics by the data of dual quantum field theories (QFTs). 1 Successful methods include the holographic renormalization [5], the reconstruction using bulk geodesics and light cones [6][7][8][9][10][11][12], the reconstruction [13][14][15][16][17][18][19][20][21][22][23][24][25] using holographic entanglement entropy [26,27], 2 the inversion formula [37] of the holographic Wilson loops [38][39][40][41], and the machine learning holography [42][43][44][45][46][47]. 3 However, all of these methods do not reconstruct the static black hole 1 Here we focus on only the bulk reconstruction of spacetime metrics.…”

“…2 Related methods include the one [28][29][30][31] using tensor networks [32,33] through the entanglement properties and the one [34] using bit threads [35,36]. 3 The holographic bulk spacetime is identified with neural networks [42][43][44][45][46][47][48][49][50][51], and the spacetimes are emergent. See [52] for a review of data science approach to string theory, and also see [53] for applications of machine learning to material sciences.…”