Quadratic Unconstrained Binary Optimization for Sparse Magnetoencephalography Source Localization

[2608.14738] Quadratic Unconstrained Binary Optimization for Sparse Magnetoencephalography Source Localization Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Signal Processing arXiv:2608.14738 (eess) [Submitted on 13 Aug 2026] Title: Quadratic Unconstrained Binary Optimization for Sparse Magnetoencephalography Source Localization Authors: Arim Ryou , Kiwoong Kim View a PDF of the paper titled Quadratic Unconstrained Binary Optimization for Sparse Magnetoencephalography Source Localization, by Arim Ryou and 1 other authors View PDF HTML (experimental) Abstract: Magnetoencephalography (MEG) source localization is an ill-posed inverse problem because distinct cortical source configurations can produce similar sensor-level fields. We formulate sparse multi-source localization as a quadratic unconstrained binary optimization (QUBO) problem combined with residual-aware candidate screening. Candidate source-location groups are generated from the sensor-space residual, fixed sensor-space templates are estimated for the resulting candidates, and active templates are jointly selected using data-fit, pairwise template interactions, and soft-cardinality terms. We evaluate the method using classical simulated annealing in controlled synthetic MEG simulations, primarily under a two-source condition, and compare it with MNE, dSPM, MxNE, LCMV, and RAP-MUSIC. Across 100 main-benchmark trials, QUBO achieved a mean cardinality-aware localization error of 8.45 mm, compared with 22.35 mm for MxNE, the best-performing baseline according to this metric, corresponding to a 62.2% reduction. The composite metric adds a 50 mm penalty per unit of source-count mismatch before normalization by the true source count. Because MxNE returned only one source in 35 trials, the reported reduction reflects both spatial localization and source-count performance. In separate sensitivity experiments, QUBO remained competitive across the tested sensor-noise and source-count conditions, although RAP-MUSIC performed comparably to or better than QUBO in some low-noise and three-source settings. The present experiments use classical simulated annealing and do not evaluate quantum hardware or claim quantum advantage. The resulting binary quadratic objective admits a direct Ising representation, enabling future evaluation on quantum-annealing and hybrid backends. Comments: 16 pages, 4 figures Subjects: Signal Processing (eess.SP) Cite as: arXiv:2608.14738 [eess.SP] (or arXiv:2608.14738v1 [eess.SP] for this version) https://doi.org/10.48550/arXiv.2608.14738 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Arim Ryou [ view email ] [v1] Thu, 13 Aug 2026 10:04:37 UTC (119 KB) Full-text links: Access Paper: View a PDF of the paper titled Quadratic Unconstrained Binary Optimization for Sparse Magnetoencephalography Source Localization, by Arim Ryou and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: eess.SP < prev | next > new | recent | 2026-08 Change to browse by: eess References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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