On the completeness of contraction map proof method for holographic entropy inequalities

A bstract The contraction map proof method is the commonly used method to prove holographic entropy inequalities. Existence of a contraction map corresponding to a holographic entropy inequality is a sufficient condition for its validity. But is it also necessary? In this note, we answer that question in affirmative for all linear holographic entropy inequalities with rational coefficients. We show that the pre-image of a non-contraction map is not a hypercube, but a proper cubical subgraph, and show that this manifests as alterations to the geodesic structure in the bulk, which leads to the violation of inequalities by holographic geometries obeying the RT formula.

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Publication Details

Journal
Journal of High Energy Physics
Published
2025-12-18
DOI
https://doi.org/10.1007/jhep12(2025)140
Primary Topic
Geometry and complex manifolds
Type
article
Field-Weighted Citation Impact
0.00

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article

On the completeness of contraction map proof method for holographic entropy inequalities

Ning Bao, Keiichiro Furuya, Joydeep Naskar
Journal of High Energy Physics
Geometry and complex manifolds
article

On the completeness of contraction map proof method for holographic entropy inequalities

Ning Bao, Keiichiro Furuya, Joydeep Naskar
article en

Abstract

A bstract The contraction map proof method is the commonly used method to prove holographic entropy inequalities. Existence of a contraction map corresponding to a holographic entropy inequality is a sufficient condition for its validity. But is it also necessary? In this note, we answer that question in affirmative for all linear holographic entropy inequalities with rational coefficients. We show that the pre-image of a non-contraction map is not a hypercube, but a proper cubical subgraph, and show that this manifests as alterations to the geodesic structure in the bulk, which leads to the violation of inequalities by holographic geometries obeying the RT formula.

Journal of High Energy PhysicsVol. 2025(12)
Northeastern University (US), Brookhaven National Laboratory (US), The NSF AI Institute for Artificial Intelligence and Fundamental Interactions (US), Universidad del Noreste (MX)
U.S. Department of Energy, Advanced Scientific Computing Research, Brookhaven National Laboratory
Openalex Percentile: Top 54%
Geometry and complex manifolds
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