论文标题
通过深度学习的纠缠熵的双几何形状
Dual Geometry of Entanglement Entropy via Deep Learning
论文作者
论文摘要
对于给定的QFT纠缠熵，我们研究了如何通过应用Ryu-Takayanagi公式和深度学习方法来重建其双重几何形状。在全息图设置中，双重几何的径向方向是用双QFT的能量尺度鉴定的。因此，全息双几何形状可以描述QFT沿RG流动的变化。有趣的是，我们表明，仅从纠缠熵数据中重建的几何形状才能为我们提供有关IR区域中其他物理特性（例如热力学数量）的更多信息。
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.
