2026-07-24T08:33:51Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:23534 2025-10-02T12:58:01Z ddc:004 open_access

Bayesian Inference in Quantum Programs Gehnen, Christina Unruh, Dominique Katoen, Joost-Pieter Quantum Program Logics Weakest Preconditions Bayesian Inference Program Semantics Conditioning is a key feature in probabilistic programming to enable modeling the influence of data (also known as observations) to the probability distribution described by such programs. Determining the posterior distribution is also known as Bayesian inference. This paper equips a quantum while-language with conditioning, defines its denotational and operational semantics over infinite-dimensional Hilbert spaces, and shows their equivalence. We provide sufficient conditions for the existence of weakest (liberal) precondition-transformers and derive inductive characterizations of these transformers. It is shown how w(l)p-transformers can be used to assess the effect of Bayesian inference on (possibly diverging) quantum programs. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Christina Gehnen and Dominique Unruh and Joost-Pieter Katoen 2025 Is Part Of LIPIcs, Volume 334, 52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.ICALP.2025.157 urn:nbn:de:0030-drops-235345 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2025.157 eng https://creativecommons.org/licenses/by/4.0/legalcode