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This paper advances foundational quantum theory by unifying contextual limitations in the Döring-Isham topos framework with Bayesian probability updates, offering a novel topological approach to modeling predictive shifts as new data arrives. By applying these mathematical insights directly to quantum computing architectures, the work bridges abstract category theory with practical algorithmic evolution, potentially refining how quantum systems handle probabilistic inference.
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Nakayama's reduction of quantum topos and Bayesian quantum computing

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arXiv:2609.34200v1 Announce Type: new Abstract: Nakayama found the way to limit the contexts in the D\"oring-Isham topos quantum theory and unified such limitation and the definition of probability as a single choice of topology. We explain how this unification supports the Bayesian perspective where the probability changes as data is obtained. We describe this change as a local shift of the applicability of a predictive theory and apply it to quantum computing.

Abstract:Nakayama found the way to limit the contexts in the Döring-Isham topos quantum theory and unified such limitation and the definition of probability as a single choice of topology. We explain how this unification supports the Bayesian perspective where the probability changes as data is obtained. We describe this change as a local shift of the applicability of a predictive theory and apply it to quantum computing.

18 pages. Submitted Apr 7, 2026; no AI disclosures. Companion paper: arXiv:2609.30406 (geometric approach)

Quantum Physics (quant-ph); Mathematical Physics (math-ph); Category Theory (math.CT)

arXiv:2609.34200 [quant-ph]

(or arXiv:2609.34200v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2609.34200 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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