Completely positive semidefinite rank
Anupam Prakash, Jamie Sikora, Antonios Varvitsiotis, Zhaohui Wei

TL;DR
This paper introduces the concept of completely positive semidefinite (cpsd) matrices, explores their properties and rank bounds, and characterizes cpsd-graphs, linking quantum information theory with matrix theory.
Contribution
It defines cpsd-rank, demonstrates its potential exponential growth, and characterizes cpsd-graphs, connecting quantum behaviors with graph theory and matrix factorizations.
Findings
Cpsd-rank can be exponential in matrix size.
Construction of cpsd matrices using Lorentz cone vectors.
Cpsd-graphs are characterized by absence of certain odd cycles.
Abstract
An matrix is called completely positive semidefinite (cpsd) if there exist Hermitian positive semidefinite matrices (for some ) such that for all . The cpsd-rank of a cpsd matrix is the smallest for which such a representation is possible. In this work we initiate the study of the cpsd-rank which we motivate twofold. First, the cpsd-rank is a natural non-commutative analogue of the completely positive rank of a completely positive matrix. Second, we show that the cpsd-rank is physically motivated as it can be used to upper and lower bound the size of a quantum system needed to generate a quantum behavior. In this work we present several properties of the cpsd-rank. Unlike the completely positive rank which is at most quadratic in the size of the matrix, no general…
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