Dual Geometry of Entanglement Entropy via Deep Learning
arXiv:2205.04445 · doi:10.1103/PhysRevD.106.106017
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.
17 pages, 9 figures
References in corpus (9)
- N=6 superconformal Chern-Simons-matter theories, M2-branes and their gravity duals
- Algebraic structures on parallel M2-branes
- Relative entropy and the Bekenstein bound
- Extracting Spacetimes using the AdS/CFT Conjecture: Part II
- AdS/Deep-Learning made easy: simple examples
- More of the Bulk from Extremal Area Variations
- Time Evolution of Entanglement Entropy in Holographic FLRW Cosmologies
- Time-dependent quantum correlations in two-dimensional expanding spacetime
- Holographic RG flow triggered by a classically marginal operator
Cited by in corpus (7)
- Deep learning bulk spacetime from boundary optical conductivity
- Holographic reconstruction of black hole spacetime: machine learning and entanglement entropy
- Disentangling the gravity dual of Yang-Mills theory
- Deep learning-based holography for T-linear resistivity
- The Algebraic Structure Underlying Pole-Skipping Points
- Holographic geometry/real-space entanglement correspondence and metric reconstruction
- Holographic entanglement entropy, Wilson loops, and neural networks