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The Japan Society of Applied Physics

Ground state search of Ising model with coherent Ising machine

Takahiro Inagaki, Ryan Hamerly, Toshimori Honjo, Kensuke Inaba, Koji Enbutsu, Takeshi Umeki, Ryouichi Kasahara, Y. Yamamoto, Hiroki Takesue

Published Jul 21, 2017• 0 citations
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Today, world-wide efforts are being undertaken to explore non-von Neumann computation methods to overcome "the end of Moore's law". A promising approach is the coherent Ising machine (CIM), which utilizes a network of degenerate optical parametric oscillators for solving ground-state-search problems of the Ising model. Since its proposal in 2013 and the first proof-of-principle experiment in 2014, research on experimental CIMs has made rapid progress and now reached the level where we can find solutions to large-scale combinatorial optimization problems using a CIM system. In this paper, we review recent progress on CIMs, mainly focusing on the experimental side.
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.