Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash 90 tok/s
Gemini 2.5 Pro 51 tok/s Pro
GPT-5 Medium 24 tok/s
GPT-5 High 27 tok/s Pro
GPT-4o 100 tok/s
GPT OSS 120B 478 tok/s Pro
Kimi K2 217 tok/s Pro
2000 character limit reached

Space-time random tensor networks and holographic duality (1801.05289v1)

Published 16 Jan 2018 in hep-th

Abstract: In this paper we propose a space-time random tensor network approach for understanding holographic duality. Using tensor networks with random link projections, we define boundary theories with interesting holographic properties, such as the Renyi entropies satisfying the covariant Hubeny-Rangamani-Takayanagi formula, and operator correspondence with local reconstruction properties. We also investigate the unitarity of boundary theory in spacetime geometries with Lorenzian signature. Compared with the spatial random tensor networks, the space-time generalization does not require a particular time slicing, and provides a more covariant family of microscopic models that may help us to understand holographic duality.

Citations (22)
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Summary

We haven't generated a summary for this paper yet.

Ai Generate Text Spark Streamline Icon: https://streamlinehq.com

Paper Prompts

Sign up for free to create and run prompts on this paper using GPT-5.

Dice Question Streamline Icon: https://streamlinehq.com

Follow-up Questions

We haven't generated follow-up questions for this paper yet.

Authors (2)