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arXiv 2302.02446hep-thquant-ph
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走向CFT中的随机张量网络与全息码

Toward random tensor networks and holographic codes in CFT

Jeevan Chandra, Thomas Hartman

AI总结:

本文提出在满足本征态热化的全息CFT中,用随机张量网络近似OPE并构造可系统修正的全息纠错码,证明其在视界外等距、视界内非等距,揭示了视界处Virasoro恒等块近似的失效机制。

摘要:

在满足本征态热化假说的全息共形场论(CFT)中,存在一个区域,其中算子乘积展开(OPE)可以用随机张量网络来近似。该张量网络的几何对应于全息对偶中的一个空间切片,张量将径向方向离散化。在任意维度下的球对称态以及二维CFT中更一般的态中,这导致了一个以OPE数据定义的全息纠错码,该码可以在随机张量近似之外被系统地修正。研究表明,对于视界外的轻算子,该码是等距的;而在视界内则是非等距的,这与关于体空间重构的一般论证所预期的一致。视界处的转变源于具有复杂内部结构的态中Virasoro恒等块近似的微妙失效。

英文摘要:

In holographic CFTs satisfying eigenstate thermalization, there is a regime where the operator product expansion can be approximated by a random tensor network. The geometry of the tensor network corresponds to a spatial slice in the holographic dual, with the tensors discretizing the radial direction. In spherically symmetric states in any dimension and more general states in 2d CFT, this leads to a holographic error-correcting code, defined in terms of OPE data, that can be systematically corrected beyond the random tensor approximation. The code is shown to be isometric for light operators outside the horizon, and non-isometric inside, as expected from general arguments about bulk reconstruction. The transition at the horizon occurs due to a subtle breakdown of the Virasoro identity block approximation in states with a complex interior.

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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