📝 SummarXiv

Abstract

The coherent Ising machine (CIM) is a quantum-inspired computing platform that leverages optical parametric oscillation dynamics to solve combinatorial optimization problems by searching for the ground state of an Ising Hamiltonian. Conventional CIM implementations face challenges in handling non-uniform coupling strengths and maintaining amplitude stability during computation. In this paper, a new global mean-amplitude feedback-enhanced spiking neural network CIM (GFSNN-CIM) is introduced with a physics-driven amplitude stabilization mechanism to dynamically balance nonlinear gain saturation and coupling effects. This modification enhances synchronization in the optical pulse network, leading to more robust convergence under varying interaction strengths. Experimental validation on Max-Cut problems demonstrates that the GFSNN-CIM achieves up to a 27% improvement in solution success rates compared to conventional spiking neural network CIM, with scalability improving as problem complexity increases. Further application to the traffic assignment problem (TAP) confirms the method's generality; the GFSNN-CIM achieves near-continuous accuracy (deviations < 0.035%) even at coarse discretization, while large-scale tests on Beijing's road network (481 spins) validate its real-world applicability. These advances establish a physics-consistent optimization framework, where optical pulse dynamics directly encode combinatorial problems, paving the way for scalable, high-performance CIM implementations in complex optimization tasks.