Beyond the Cabello-Severini-Winter framework: Making sense of contextuality without sharpness of measurements
Ravi Kunjwal

TL;DR
This paper introduces a generalized hypergraph-theoretic framework for Spekkens contextuality that relaxes traditional assumptions like outcome determinism and measurement sharpness, providing noise-robust inequalities beyond the CSW framework.
Contribution
It extends the CSW framework by incorporating preparation noncontextuality, relaxing outcome determinism, and removing the need for measurement sharpness, with new hypergraph invariants and operational quantities.
Findings
Developed a source events hypergraph and a new invariant, the weighted max-predictability.
Derived noise-robust noncontextuality inequalities involving three operational quantities.
Showed that CSW bounds are a special case when a certain correlation measure equals one.
Abstract
We develop a hypergraph-theoretic framework for Spekkens contextuality applied to Kochen-Specker (KS) type scenarios that goes beyond the Cabello-Severini-Winter (CSW) framework. To do this, we add new hypergraph-theoretic ingredients to the CSW framework. We then obtain noise-robust noncontextuality inequalities in this generalized framework by applying the assumption of (Spekkens) noncontextuality to both preparations and measurements. The resulting framework goes beyond the CSW framework, both conceptually and technically. On the conceptual level: 1) we relax the assumption of outcome determinism inherent to the Kochen-Specker theorem but retain measurement noncontextuality, besides introducing preparation noncontextuality, 2) we do not require the exclusivity principle as a fundamental constraint on measurement events, and 3) as a result, we do not need to presume that measurement…
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