Quantum-inspired Ising machine using sparsified spin connectivity

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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Quantum-inspired Ising machine using sparsified spin connectivity

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