On the construction of graph models realizing given entropy vectors
Veronika E. Hubeny, Massimiliano Rota
TL;DR
The paper develops a constructive framework for realizing given entropy vectors with holographic graph models, focusing on simple tree topologies under a chordality (KC-based) condition. It introduces a comprehensive correlation-hypergraph toolkit, including coarse- and fine-graining, and reinterprets KC-PMIs through the MI-poset and Gamma-partitions, enabling efficient candidate-model construction. A central contribution is an explicit algorithm for building simple-tree holographic graphs when the line graph is chordal, with reductions to strictly positive entries and $|Q^igcap|=0$; it also outlines strategies for non-chordal cases via fine-graining and unrealizability criteria, aiming to illuminate the structure of the holographic entropy cone and test conjectures at larger N. The work provides a structured path toward systematically constructing holographic models for entropy vectors and discusses how to detect unrealizability independent of holographic entropy inequalities, with broader implications for the geometry-entropy correspondence in gauge-gravity duality.
Abstract
We present an efficient algorithm for the construction of a holographic simple tree graph model that realizes a given entropy vector, subject to a specific ``chordality'' condition first introduced in arXiv:2412.18018. We further develop the toolkit of the correlation hypergraph, particularly in relation to coarse-graining and fine-graining of subsystems. We then use these techniques to take the first steps towards the generalization of this new algorithm to arbitrary (not necessarily simple) holographic tree graph models, and the ``detection'' of unrealizability of an entropy vector independently from the knowledge of holographic entropy inequalities.
