Combinatorial optimization with coherent Ising machine

Hiroki Takesue

Shoko Utsunomiya, Shuhei Tamate, Yoshitaka Haribara +6 more

Zhi‐guo Nie, Ruo‐xing Guo, Chen‐Rui Fan +4 more

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.

Hiroki Takesue, Takahiro Inagaki, Toshimori Honjo

Mikio Hasegawa, Hirotake Ito, Hiroki Takesue +1 more

Recently, new optimization machines based on non-silicon physical systems, such as quantum annealing machines, have been developed, and their commercialization has been started. These machines solve the problems by searching the state of the Ising spins, which minimizes the Ising Hamiltonian. Such a property of minimization of the Ising Hamiltonian can be applied to various combinatorial optimization problems. In this paper, we introduce the coherent Ising machine (CIM), which can solve the problems in a milli-second order, and has higher performance than the quantum annealing machines especially on the problems with dense mutual connections in the corresponding Ising model. We explain how a target problem can be implemented on the CIM, based on the optimization scheme using the mutually connected neural networks. We apply the CIM to traveling salesman problems as an example benchmark, and show experimental results of the real machine of the CIM. We also apply the CIM to several combinatorial optimization problems in wireless communication systems, such as channel assignment problems. The CIM's ultra-fast optimization may enable a real-time optimization of various communication systems even in a dynamic communication environment.

Yunpyo Hong, Jaejoon Gill, Daeyeun Kim +3 more