论文标题
位线程,爱因斯坦的方程式和批量位置
Bit threads, Einstein's equations and bulk locality
论文作者
论文摘要
在全息图的背景下,可以通过i)极端表面或ii)纠缠熵来研究,或者ii)位线,即,divergenceless vector vector字段具有由Planck长度设置的NORM BONDS。在本文中,我们根据后一种方法开发了一种新的度量重建方法,并显示了与现有方法相比的优势。我们首先研究真空状态周围的一般线性扰动。通用线程配置结果是以高度非局部的方式编码有关度量的信息,但是,我们表明,对于具有本地模块化汉密尔顿的边界区域,总是有一个典型的选择,用于利用散装本地性的扰动线程配置。为此,我们以差异形式表达了位线程形式主义,以使其显然是背景独立的。我们表明,iyer-wald形式主义为规范局部扰动提供了自然候选者,可用于根据特定线性差异操作员的反转来重述度量重建问题。我们详细检查了球形区域的反转问题,并在这种情况下为反操作员提供明确的表达式。我们认为,超越了线性顺序,认为必须倒置的操作员自然会按顺序增加。但是,反转可以在扰动中的不同顺序递归完成。最后,我们通过将反演问题作为特定优化问题进行措辞来评论一种非扰动度量的替代方法。
In the context of holography, entanglement entropy can be studied either by i) extremal surfaces or ii) bit threads, i.e., divergenceless vector fields with a norm bound set by the Planck length. In this paper we develop a new method for metric reconstruction based on the latter approach and show the advantages over existing ones. We start by studying general linear perturbations around the vacuum state. Generic thread configurations turn out to encode the information about the metric in a highly nonlocal way, however, we show that for boundary regions with a local modular Hamiltonian there is always a canonical choice for the perturbed thread configurations that exploits bulk locality. To do so, we express the bit thread formalism in terms of differential forms so that it becomes manifestly background independent. We show that the Iyer-Wald formalism provides a natural candidate for a canonical local perturbation, which can be used to recast the problem of metric reconstruction in terms of the inversion of a particular linear differential operator. We examine in detail the inversion problem for the case of spherical regions and give explicit expressions for the inverse operator in this case. Going beyond linear order, we argue that the operator that must be inverted naturally increases in order. However, the inversion can be done recursively at different orders in the perturbation. Finally, we comment on an alternative way of reconstructing the metric non-perturbatively by phrasing the inversion problem as a particular optimization problem.