arXiv subjects

Arim Ryou

Publications and source records attributed to Arim Ryou.

4 recordsLinked to original sources

arXiv2026

Quadratic Unconstrained Binary Optimization for Sparse Magnetoencephalography Source Localization

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.

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arXiv2025

Hybrid Quantum Annealing Approach for High-Dimensional and Multi-Criteria Constrained Quadratic Optimization in Arctic Ship Routing

The opening of Arctic sea routes presents unprecedented opportunities for global trade but poses significant operational and computational challenges due to the dynamic nature of sea ice conditions. This study formulates a multi criteria Arctic route optimization problem that integrates Copernicus Marine Environment Monitoring Service (CMEMS) variables into a Constrained Quadratic Model (CQM) and solves it using D Wave's hybrid quantum classical solver. We benchmark the feasibility and scalability of this approach against classical Mixed Integer Quadratic Programming (MIQP) solvers such as Gurobi and CPLEX. Results show that the CQM formulation achieves feasible solutions with stable runtimes as quadratic density increases, demonstrating 10 to 100 times faster convergence and reduced computational time compared with classical solvers, while also improving route smoothness by approximately 10 percent and reducing total length by approximately 1 percent. This reflects the effectiveness of the hybrid quantum annealing approach for Arctic routing problems.

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arXiv2025

Quantum prime factorization algorithms using binary carry propagation

The RSA cryptosystem, which relies on the computational difficulty of prime factorization, faces growing challenges with the advancement of quantum computing. In this study, we propose a quantum annealing based approach to integer factorization using both high order unconstrained binary optimization (HUBO) and constrained quadratic model (CQM) formulations. We begin by modeling binary multiplication with explicit carry propagation, translating this into a HUBO representation and subsequently reducing it to a quadratic unconstrained binary optimization form compatible with current quantum solvers. To address scalability limitations, we implement a CQM approach with constraint relaxation and global product consistency. While the HUBO model successfully factors small semiprimes, it exhibits exponential memory growth, making it impractical for inputs larger than 10 bits. In contrast, the CQM model achieves accurate factorization of semiprimes up to 60 bits including N = 1152921423002469787 demonstrating significantly improved scalability. Experimental results further show that applying global product constraints enhances factorization accuracy and consistency across all tested instances. This work highlights both the promise and current limitations of quantum-assisted factorization and establishes a foundation for evaluating RSA security in the emerging quantum era.

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arXiv2025

Quantum compressed sensing tomographic reconstruction algorithm

Computed tomography (CT) is a non-destructive technique for observing internal images and has proven highly valuable in medical diagnostics. Recent advances in quantum computing have begun to influence tomographic reconstruction techniques. The quantum tomographic reconstruction algorithm is less affected by artifacts or noise than classical algorithms by using the square function of the difference between pixels obtained by projecting CT images in quantum superposition states and pixels obtained from experimental data. In particular, by using quantum linear systems, a fast quadratic unconstrained binary optimization (QUBO) model formulation for quantum tomographic reconstruction is possible. In this paper, we formulate the QUBO model for quantum compressed sensing tomographic reconstruction, which is a linear combination of the QUBO model for quantum tomographic reconstruction and the QUBO model for total variation in quantum superposition-state CT images. In our experiments, we used sinograms obtained by using the Radon transform of Shepp-Logan images and body CT images. We evaluate the performance of the new algorithm by reconstructing CT images using a hybrid solver with the QUBO model computed from each sinogram. The new algorithm was able to obtain a solution within 5 projection images for 30 by 30 image samples and within 6 projection images for 60 by 60 image samples, reconstructing error-free CT images. We anticipate that quantum compressed sensing tomographic reconstruction algorithms could significantly reduce the total radiation dose when quantum computing performance advances.

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