Reflected entropy in random tensor networks

Chris Akers, Thomas Faulkner, Simon Lin, Pratik Rath

Research output: Contribution to journalArticlepeer-review


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 Rényi 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 Rényi spectrum is not flat, in sharp contrast to the usual Rényi 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.

Original languageEnglish (US)
Article number162
JournalJournal of High Energy Physics
Issue number5
StatePublished - May 2022


  • AdS-CFT Correspondence
  • Gauge-Gravity Correspondence

ASJC Scopus subject areas

  • Nuclear and High Energy Physics


Dive into the research topics of 'Reflected entropy in random tensor networks'. Together they form a unique fingerprint.

Cite this