Bayesian Update Step Using Adiabatic Quantum Computing

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dc.contributor.authorBunk, Norman
dc.contributor.authorGovaers, Felix
dc.contributor.editorKais, Sabre
dc.contributor.editorSkala, Václav
dc.date.accessioned2025-10-08T14:18:51Z
dc.date.available2025-10-08T14:18:51Z
dc.date.issued2025
dc.description.abstract-translatedWe propose a quantum algorithm for the filtering step in Bayesian state estimation, based on adiabatic quantum computing (AQC). The approach embeds the likelihood function into the energy landscape of an Ising Hamiltonian, enabling the posterior distribution to emerge through quantum annealing. Unlike existing gate-based methods, our formulation avoids complex conditional operations and scales more naturally with the size of the state space. Numerical simulations confirm that the resulting distributions closely match the correct posterior, even with approximate parameter settings. This demonstrates the feasibility of AQC as a tool for probabilistic inference in high-dimensional filtering problems.en
dc.format6 s.cs
dc.identifier.doihttp://www.doi.org/10.24132/CSRN.2025-A23
dc.identifier.issn2464-4617
dc.identifier.issn2464-4625
dc.identifier.urihttp://hdl.handle.net/11025/62926
dc.language.isoenen
dc.publisherVaclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.rights.accessopenAccessen
dc.subjectadiabatické kvantové výpočtycs
dc.subjectfúze senzorových datcs
dc.subjectBayesova větacs
dc.subject.translatedadiabatic quantum computingen
dc.subject.translatedsensor data fusionen
dc.subject.translatedBayes theoremen
dc.titleBayesian Update Step Using Adiabatic Quantum Computingen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.type.statusPeer revieweden
dc.type.versionpublishedVersionen
local.files.count2*
local.files.size2406637*
local.has.filesyes*

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