Quantum-inspired Ising machine using sparsified spin connectivity

TL;DR

This paper presents a quantum-inspired algorithm, E-MVL, that efficiently solves large-scale combinatorial optimization problems by mimicking thermal spin dynamics, outperforming simulated annealing in speed and solution quality.

Contribution

The study introduces E-MVL, a novel quantum-inspired digital logic algorithm that enhances optimization performance and provides a practical FPGA implementation for faster solutions.

Findings

E-MVL solves problems up to 1600 spins, outperforming SA limited to 400 spins.

E-MVL achieves approximately 6-fold faster solution speed than SA on FPGA.

Sparsity control in E-MVL provides consistent solution space exploration across different problem distributions.

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.