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arXiv 2205.04445hep-thgr-qcmath-phmath.MP
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通过深度学习实现纠缠熵的对偶几何

Dual Geometry of Entanglement Entropy via Deep Learning

Chanyong Park, Chi-Ok Hwang, Kyungchan Cho, Se-Jin Kim

摘要:

对于给定的量子场论的纠缠熵,我们研究了如何利用Ryu-Takayanagi公式和深度学习方法重建其对偶几何。在全息设置中,对偶几何的径向方向与对偶QFT的能量标度相一致。因此,全息对偶几何可以描述QFT沿RG流的变化。有趣的是,我们表明,重建的几何结构,只有从纠缠熵的数据可以给我们更多的信息,其他的物理性质,如热力学量在红外区域。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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