Dual Geometry of Entanglement Entropy via Deep Learning

TL;DR

This paper explores reconstructing dual geometries from entanglement entropy data using deep learning, revealing insights into the holographic RG flow and IR physics beyond traditional methods.

Contribution

It introduces a deep learning approach to reconstruct holographic dual geometries solely from entanglement entropy data, linking quantum information to spacetime geometry.

Findings

Reconstructed geometry encodes RG flow information.

Entanglement data reveals IR thermodynamic properties.

Deep learning enhances holographic geometry reconstruction.

Abstract

For a given entanglement entropy of QFT, we investigate how to reconstruct its dual geometry by applying the Ryu-Takayanagi formula and the deep learning method. In the holographic setup, the radial direction of the dual geometry is identified with the energy scale of the dual QFT. Therefore, the holographic dual geometry can describe how the QFT changes along the RG flow. Intriguingly, we show that the reconstructed geometry only from the entanglement entropy data can give us more information about other physical properties like thermodynamic quantities in the IR region.