Abstract:We develop a reinforcement learning algorithm to study the holographic entropy cone. Given a target entropy vector, our algorithm searches for a graph realization whose min-cut entropies match the target vector. If the target vector does not admit such a graph realization, it must lie outside the cone, in which case the algorithm finds a graph whose corresponding entropy vector most nearly approximates the target and allows us to probe the location of the facets. For the $\sf N=3$ cone, we confirm that our algorithm successfully rediscovers monogamy of mutual information beginning with a target vector outside the holographic entropy cone. We then apply the algorithm to the $\sf N=6$ cone, analyzing the 6 "mystery" extreme rays of the subadditivity cone from arXiv:2412.15364 that satisfy all known holographic entropy inequalities yet lacked graph realizations. We found realizations for 3 of them, proving they are genuine extreme rays of the holographic entropy cone, while providing evidence that the remaining 3 are not realizable, implying unknown holographic inequalities exist for $\sf N=6$.
| Comments: | 39 pages, 10 figures, 2 tables; v2: minor clarifications, version appearing in JHEP |
| Subjects: | High Energy Physics - Theory (hep-th); Machine Learning (cs.LG); Quantum Physics (quant-ph) |
| Report number: | CALT-TH 2026-005 |
| Cite as: | arXiv:2601.19979 [hep-th] |
| (or arXiv:2601.19979v2 [hep-th] for this version) | |
| https://doi.org/10.48550/arXiv.2601.19979 arXiv-issued DOI via DataCite |
Submission history
From: Jaeha Lee [view email]
[v1]
Tue, 27 Jan 2026 19:00:01 UTC (4,270 KB)
[v2]
Wed, 13 May 2026 04:09:47 UTC (4,273 KB)