Sliding Mode Control‐Like Accelerated Coherent Ising Machine

Abstract Coherent Ising Machine (CIM) emerge as powerful tools for solving large‐scale combinatorial optimization problems by mapping them to the ground state search of the Ising model. Traditional CIM models face two major challenges when addressing large‐scale problems: slowness in convergence and susceptibility to local minima. To address these limitations, the Sliding Mode Control‐Like Coherent Ising Machine (SMCL‐CIM) integrates sliding mode control principles into the feedback mechanism of the CIM, inspired by classical dynamic control methods. Experimental results on random graphs and G‐set benchmarks demonstrate that for the max‐cut problem, SMCL‐CIM achieves an approximately 79. 93% reduction in solution time while improving solution accuracy by 11.4%–15.3% under the same simulation conditions. This scheme provides an efficient and scalable approach to combinatorial optimization, thereby facilitating the broader application of CIM.