Bounding the separable rank via polynomial optimization
Sander Gribling, Monique Laurent, Andries Steenkamp

TL;DR
This paper develops a hierarchy of semidefinite programming bounds to estimate the separable rank of bipartite quantum states, extending to multipartite and real cases, and offers new insights into the structure of separable states.
Contribution
It introduces a moment-based hierarchy of semidefinite bounds for the separable rank, utilizing polynomial positivity constraints, and extends these methods to multipartite and real separable states.
Findings
Hierarchy converges to a convexified separable rank parameter
Method extends to multipartite and real cases
Provides new proofs for convergence of the DPS hierarchy
Abstract
We investigate questions related to the set consisting of the linear maps acting on that can be written as a convex combination of rank one matrices of the form . Such maps are known in quantum information theory as the separable bipartite states, while nonseparable states are called entangled. In particular we introduce bounds for the separable rank , defined as the smallest number of rank one states entering the decomposition of a separable state . Our approach relies on the moment method and yields a hierarchy of semidefinite-based lower bounds, that converges to a parameter , a natural convexification of the combinatorial parameter . A distinguishing feature is exploiting the positivity constraint…
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Taxonomy
TopicsQuantum Information and Cryptography · Quantum Computing Algorithms and Architecture · Quantum Mechanics and Applications
