Topological methods for studying contextuality: $N$-cycle scenarios and beyond

Abstract: Simplicial distributions are combinatorial models describing distributions on spaces of measurements and outcomes that generalize non-signaling distributions on contextuality scenarios. This paper studies simplicial distributions on $2$-dimensional measurement spaces by introducing new topological methods. Two key ingredients are a geometric interpretation of Fourier--Motzkin elimination and a technique based on collapsing of measurement spaces. Using the first one, we provide a new proof of Fine's theorem characterizing non-contextual distributions on $N$-cycle scenarios. Our approach goes beyond these scenarios and can describe non-contextual distributions on scenarios obtained by gluing cycle scenarios of various sizes. The second technique is used for detecting contextual vertices and deriving new Bell inequalities. Combined with these methods, we explore a monoid structure on simplicial distributions.