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Complexity of Contextuality

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arxiv 2506.09133 v1 pith:A4GOV4RI submitted 2025-06-10 quant-ph cs.CCcs.CG

Complexity of Contextuality

classification quant-ph cs.CCcs.CG
keywords modelontologicalcomplexitysmallestnoncontextualdecidingdimensioncomputational
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Generalized contextuality is a hallmark of nonclassical theories like quantum mechanics. Yet, three fundamental computational problems concerning its decidability and complexity remain open. First, determining the complexity of deciding if a theory admits a noncontextual ontological model; Second, determining the complexity of deciding if such a model is possible for a specific dimension $k$; Third, efficiently computing the smallest such model when it exists, given that finding the smallest ontological model is NP-hard. We address the second problem by presenting an algorithm derived from a geometric formulation and its reduction to the intermediate simplex problem in computational geometry. We find that the complexity of deciding the existence of a noncontextual ontological model of dimension $k$ is at least exponential in the dimension of the theory and at most exponential in $k$. This, in turn, implies that computing the smallest noncontextual ontological model is inefficient in general. Finally, we demonstrate the fundamental difference between finding the smallest noncontextual ontological model and the smallest ontological model using an explicit example wherein the respective minimum ontic sizes are five and four.

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  1. Linear Algebra of Generalized Contextuality in All Prepare-Transform-Measure Scenarios

    quant-ph 2026-07 conditional novelty 6.0

    A rank-based linear-algebra criterion and decision algorithm certify generalized (non)contextuality in prepare-transform-measure scenarios with any number of sequential transformations.