Reflected entropy in random tensor networks

TL;DR

This paper explores the reflected entropy in random tensor networks, establishing a duality with holographic theories, analyzing non-perturbative effects, and proposing a new analytic continuation method that aligns with holographic expectations.

Contribution

It introduces an analytical approach to reflected entropy in random tensor networks, including non-perturbative effects and a new continuation prescription, aligning results with holographic duality.

Findings

Reflected entanglement spectrum matches numerical results.

The spectrum is not flat, indicating complex entanglement structure.

Superselection sectors organize contributions to the spectrum.

Abstract

In holographic theories, the reflected entropy has been shown to be dual to the area of the entanglement wedge cross section. We study the same problem in random tensor networks demonstrating an equivalent duality. For a single random tensor we analyze the important non-perturbative effects that smooth out the discontinuity in the reflected entropy across the Page phase transition. By summing over all such effects, we obtain the reflected entanglement spectrum analytically, which agrees well with numerical studies. This motivates a prescription for the analytic continuation required in computing the reflected entropy and its R\'enyi generalization which resolves an order of limits issue previously identified in the literature. We apply this prescription to hyperbolic tensor networks and find answers consistent with holographic expectations. In particular, the random tensor network has the same non-trivial tripartite entanglement structure expected from holographic states. We furthermore show that the reflected R\'enyi spectrum is not flat, in sharp contrast to the usual R\'enyi spectrum of these networks. We argue that the various distinct contributions to the reflected entanglement spectrum can be organized into approximate superselection sectors. We interpret this as resulting from an effective description of the canonically purified state as a superposition of distinct tensor network states. Each network is constructed by doubling and gluing various candidate entanglement wedges of the original network. The superselection sectors are labelled by the different cross-sectional areas of these candidate entanglement wedges.