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This research presents a quantum-inspired Ising machine that mimics thermal spin dynamics using digital logic circuits and sparsified connectivity. By optimizing the search for ground states in combinatorial problems, the method solves significantly larger instances (up to 1600 spins) than standard simulated annealing baselines while achieving a six-fold speedup on FPGA hardware. The study offers practical pathways for enhancing classical optimization algorithms without relying on full-scale quantum hardware.
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Quantum-inspired Ising machine using sparsified spin connectivity

arXiv3 min read

arXiv:2604.04606v2 Announce Type: replace-cross Abstract: Combinatorial optimization problems become computationally intractable as these NP-hard problems scale. We previously proposed extraction-type majority voting logic (E-MVL), a quantum-inspired algorithm using digital logic circuits. E-MVL mimics the thermal spin dynamics of simulated annealing (SA) through controlled sparsification of spin interactions for efficient ground-state search. This study investigates the performance potential of E-MVL through systematic optimization and comprehensive benchmarking against SA. The target problem is the Sherrington-Kirkpatrick (SK) model with bimodal and Gaussian coupling distributions. Through equilibrium state analysis, we demonstrate that the sparsity control mechanism provides a consistent search of the solution space regardless of the problem's coupling distribution (bimodal, Gaussian) or size. E-MVL not only achieves the best performance among all tested algorithms--solving exact solutions up to 1600 spins where the best SA baseline is limited to 400 spins--but also provides insights that significantly improve SA's own temperature scheduling. These results establish E-MVL's dual contribution as both an efficient optimizer and a practical methodology for enhancing SA performance. Moreover, FPGA implementation achieved an approximately 6-fold faster solution speed than SA.

Abstract:Combinatorial optimization problems become computationally intractable as these NP-hard problems scale. We previously proposed extraction-type majority voting logic (E-MVL), a quantum-inspired algorithm using digital logic circuits. E-MVL mimics the thermal spin dynamics of simulated annealing (SA) through controlled sparsification of spin interactions for efficient ground-state search. This study investigates the performance potential of E-MVL through systematic optimization and comprehensive benchmarking against SA. The target problem is the Sherrington-Kirkpatrick (SK) model with bimodal and Gaussian coupling distributions. Through equilibrium state analysis, we demonstrate that the sparsity control mechanism provides a consistent search of the solution space regardless of the problem's coupling distribution (bimodal, Gaussian) or size. E-MVL not only achieves the best performance among all tested algorithms--solving exact solutions up to 1600 spins where the best SA baseline is limited to 400 spins--but also provides insights that significantly improve SA's own temperature scheduling. These results establish E-MVL's dual contribution as both an efficient optimizer and a practical methodology for enhancing SA performance. Moreover, FPGA implementation achieved an approximately 6-fold faster solution speed than SA.

23 pages, 13 figures. Corresponding author: Jun-ichi Shirakashi

Emerging Technologies (cs.ET); Quantum Physics (quant-ph)

arXiv:2604.04606 [cs.ET]

(or arXiv:2604.04606v2 [cs.ET] for this version)

https://doi.org/10.48550/arXiv.2604.04606 Focus to learn more arXiv-issued DOI via DataCite

Sci. Rep. 16 (2026) 17900

https://doi.org/10.1038/s41598-026-45226-5 Focus to learn more DOI(s) linking to related resources

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