Noncontextual ontological models of operational probabilistic theories
Sina Soltani, Marco Erba, David Schmid, and John H. Selby

TL;DR
This paper extends the concept of noncontextual ontological models to operational probabilistic theories, proving that strongly causal theories admit noncontextual ontological representations independent of instrument structure.
Contribution
It adapts noncontextuality to OPTs with explicit instrument structures and proves noncontextuality for strongly causal quotiented OPTs, showing their representations depend only on transformations.
Findings
Noncontextual models can be adapted to OPTs with explicit instrument structures.
Strongly causal quotiented OPTs admit noncontextual ontological representations.
Ontological representations are determined solely by transformations, not instrument structure.
Abstract
An experiment or theory is classically explainable if it can be reproduced by some noncontextual ontological model. In this work, we adapt the notion of ontological models and generalized noncontextuality so it applies to the framework of operational probabilistic theories (OPTs). A defining feature of quotiented OPTs, which sets them apart from the closely related framework of generalized probabilistic theories (GPTs), is their explicit specification of the structure of instruments, these being generalizations of (including nondestructive measurements); in particular, one needs to explicitly declare which collections of transformations constitute a valid instrument. We are particularly interested in strongly causal OPTs, in which the choice of a future instrument can be conditioned on a past measurement outcome. This instrument structure might seem to…
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Taxonomy
TopicsStatistical and Computational Modeling · Economic and Technological Systems Analysis
