Entropy and Information Causality in General Probabilistic Theories
Howard Barnum, Jonathan Barrett, Lisa Orloff Clark, Matthew Leifer,, Robert Spekkens, Nicholas Stepanik, Alex Wilce, Robin Wilke

TL;DR
This paper explores entropy concepts in general probabilistic theories, revealing conditions under which information causality holds and how entropy properties relate to non-classical theories like PR boxes.
Contribution
It introduces the notion of monoentropic theories where measurement and mixing entropies coincide, and links entropy properties to information causality violations.
Findings
Measurement entropy is concave and subadditive in classical and quantum theories.
Mixing entropy can be non-concave in non-simplicial polytope state spaces.
Monoentropic theories with strong subadditivity satisfy information causality.
Abstract
We investigate the concept of entropy in probabilistic theories more general than quantum mechanics, with particular reference to the notion of information causality recently proposed by Pawlowski et. al. (arXiv:0905.2992). We consider two entropic quantities, which we term measurement and mixing entropy. In classical and quantum theory, they are equal, being given by the Shannon and von Neumann entropies respectively; in general, however, they are very different. In particular, while measurement entropy is easily seen to be concave, mixing entropy need not be. In fact, as we show, mixing entropy is not concave whenever the state space is a non-simplicial polytope. Thus, the condition that measurement and mixing entropies coincide is a strong constraint on possible theories. We call theories with this property monoentropic. Measurement entropy is subadditive, but not in general strongly…
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