paper

Completely positive semidefinite rank

arXiv:1604.07199 · doi:10.1007/s10107-017-1198-4

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 upper bound is known on the cpsd-rank of a cpsd matrix. In fact, we show that the cpsd-rank can be exponential in terms of the size. Specifically, for any we construct a cpsd matrix of size whose cpsd-rank is . Our construction is based on Gram matrices of Lorentz cone vectors, which we show are cpsd. The proof relies crucially on the connection between the cpsd-rank and quantum behaviors. In particular, we use a known lower bound on the size of matrix representations of extremal quantum correlations which we apply to high-rank extreme points of the -dimensional elliptope. Lastly, we study cpsd-graphs, i.e., graphs with the property that every doubly nonnegative matrix whose support is given by is cpsd. We show that a graph is cpsd if and only if it has no odd cycle of length at least as a subgraph. This coincides with the characterization of cp-graphs.

29 pages including appendix. Comments welcome

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