2025/03/31 by Yohei Hamakawa, Tomoya Kashimata, Hamakawa, Yohei +5
Computer Science · #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #Machine Learning (cs.LG) #cs.ET #cs.LG
paper · pdf · doi:10.48550/arxiv.2503.23966
published as Nature Communications (2026)
arxiv created 2025/04/02 · arxiv updated 2026/07/31
Quantum or quantum-inspired Ising machines have recently shown promise in solving combinatorial optimization problems in a short time. Real-world applications, such as time division multiple access (TDMA) scheduling for wireless multi-hop networks and financial trading, require solving those problems sequentially where the size and characteristics change dynamically. However, using Ising machines involves challenges to shorten system-wide latency due to the transfer of large Ising model or the cloud access and to determine the parameters for each problem. Here we show a combinatorial optimization method using embedded Ising machines, which enables solving diverse problems at high speed without runtime parameter tuning. We customize the algorithm and circuit architecture of the simulated bifurcation-based Ising machine to compress the Ising model and accelerate computation and then built a machine learning model to estimate appropriate parameters using extensive training data. In TDMA scheduling for wireless multi-hop networks, our demonstration has shown that the sophisticated system can adapt to changes in the problem and showed that it has a speed advantage over conventional methods.