aiqbench analysis
This paper introduces an efficient algorithm for constructing holographic tree graph models that realize specific entropy vectors, advancing the toolkit for correlation hypergraphs in coarse- and fine-graining subsystems. The work also proposes methods to detect the unrealizability of entropy vectors without relying on known holographic entropy inequalities, marking a step toward generalizing these techniques to arbitrary models.
On the construction of graph models realizing given entropy vectors
arXiv:2512.18702v2 Announce Type: replace-cross 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.
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.
Replaced example in sec 3.2, fixed typos, 62 pages, 17 figures, published version
arXiv:2512.18702 [hep-th]
(or arXiv:2512.18702v2 [hep-th] for this version)
https://doi.org/10.48550/arXiv.2512.18702 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 194 words
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