Generalized probabilistic theories and conic extensions of polytopes

Abstract

Generalized probabilistic theories (GPT) provide a general framework that includes classical and quantum theories. It is described by a cone C and its dual C*. We show that whether some one-way communication complexity problems can be solved within a GPT is equivalent to the recently introduced cone factorization of the corresponding communication matrix M. We also prove an analogue of Holevoʼs theorem: when the cone C is contained in , the classical capacity of the channel realized by sending GPT states and measuring them is bounded by . Polytopes and optimising functions over polytopes arise in many areas of discrete mathematics. A conic extension of a polytope is the intersection of a cone C with an affine subspace whose projection onto the original space yields the desired polytope. Extensions of polytopes can sometimes be much simpler geometric objects than the polytope itself. The existence of a conic extension of a polytope is equivalent to that of a cone factorization of the slack matrix of the polytope, on the same cone. We show that all polytopes whose vertices can be recognized by a polynomial size circuit, which includes as a special case the travelling salesman polytope and many other polytopes from combinatorial optimization, have small conic extension complexity when the cone is the completely positive cone. Using recent exponential lower bounds on the linear extension complexity of polytopes, this provides an exponential gap between the communication complexity of GPT based on the completely positive cone and classical communication complexity, and a conjectured exponential gap with quantum communication complexity. Our work thus relates the communication complexity of generalizations of quantum theory to questions of mainstream interest in the area of combinatorial optimization.