(13.06.) Deep learning and holographic QCD
Koji Hashimoto (Osaka)
13.06.2019 at 16:15
We present a deep neural network representation of the AdS/CFT correspondence, and demonstrate the emergence of the bulk metric function via the learning process for given data sets of response in boundary quantum field theories. The emergent radial direction of the bulk is identified with the depth of the layers, and the network itself is interpreted as a bulk geometry. Our network provides a data-driven holographic modeling of strongly coupled systems. As an explicit application, we use a lattice QCD data of the chiral condensate at a finite temperature as our training data, the deep learning finds an emergent bulk metric to have both a black hole horizon and a finite-height IR wall, so shares both the confining and deconfining phases. This is consistent with the cross-over thermal phase transition of QCD and also with quantum gravity corrections of the geometry. Our method suggests a way to formulate a quantum gravity via solving the inverse problem of the AdS/CFT.
Arnold Sommerfeld Center