Every valid linear holographic entropy inequality with integer coefficients has a contraction map, making the contraction-map proof method complete.
Holographic Entropy Relations Repackaged
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
abstract
We explore the structure of holographic entropy relations (associated with 'information quantities' given by a linear combination of entanglement entropies of spatial sub-partitions of a CFT state with geometric bulk dual). Such entropy relations can be recast in multiple ways, some of which have significant advantages. Motivated by the already-noted simplification of entropy relations when recast in terms of multipartite information, we explore additional simplifications when recast in a new basis, which we dub the K-basis, constructed from perfect tensor structures. For the fundamental information quantities such a recasting is surprisingly compact, in part due to the interesting fact that entropy vectors associated to perfect tensors are in fact extreme rays in the holographic entropy cone (as well as the full quantum entropy cone). More importantly, we prove that all holographic entropy inequalities have positive coefficients when expressed in the K-basis, underlying the key advantage over the entropy basis or the multipartite information basis.
representative citing papers
Reinforcement learning finds explicit graph realizations for three of six previously unresolved extreme rays of the N=6 holographic entropy cone and supplies evidence that the other three lie outside it.
The upper bound of holographic conditional mutual information equals twice the entanglement of state-constrained purification and diverges in the many-interval limit, revealing abundant multipartite entanglement.
The authors derive upper bounds for large families of holographic entanglement entropy combinations by fixing the connectivity of entanglement wedges and classifying gap regions.
Derives conditions for TEE probes, generalizes cyclic and multi-information quantities, and verifies holographic entropy inequalities for gapped topological states.
A tree tensor network for distilling SU(N)_1 Chern-Simons boundary states is proposed, but its key fusion-matrix identification is asserted without derivation.
citing papers explorer
-
Topological entanglement entropy meets holographic entropy inequalities
Derives conditions for TEE probes, generalizes cyclic and multi-information quantities, and verifies holographic entropy inequalities for gapped topological states.