Genuine Multi-Entropy in the Toric Code
Abstract
We study genuine multi-entropy as a diagnostic of multipartite entanglement in the toric code, which provides a controlled setting for probing multipartite structures in topologically ordered states. Our main question is whether genuine multi-entropy captures information that is not reducible to conventional lower-party entropic data, such as topological entanglement entropy. We first analyze toric-code ground states that admit a stabilizer-state description, where the relevant quantities can be evaluated exactly. In this sector, genuine multi-entropy reflects the topological structure and symmetries of the toric code, while exhibiting highly constrained relations to lower-party multi-entropies. We conjecture that, for stabilizer states and , the -partite genuine multi-entropy at replica index collapses to a linear combination of multi-entropies involving at most parties. We establish this pattern explicitly for in the toric code stabilizer sector: for , the genuine multi-entropy is proportional to the tripartite information and, for the Kitaev--Preskill partition, contains no independent genuine four-partite information beyond that captured by the topological entanglement entropy. At , however, this reduction breaks down: the genuine multi-entropy is no longer proportional to , but remains a topological invariant of the toric-code stabilizer ground states. For generic non-stabilizer superpositions within the ground-state manifold and for coherent superpositions of local excitations, the low- reduction also fails. These results show that genuine multi-entropy probes multipartite entanglement structure beyond the tripartite information, and hence beyond the topological entanglement entropy in the Kitaev--Preskill partition, whereas for stabilizer states at low replica index it reduces to lower-partite entropic data.
Cite
@article{arxiv.2607.06050,
title = {Genuine Multi-Entropy in the Toric Code},
author = {Sriram Akella and Norihiro Iizuka and Akihiro Miyata},
journal= {arXiv preprint arXiv:2607.06050},
year = {2026}
}
Comments
17 pages, 19 figures. Comments are welcome!