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arXiv · 2503.23966

Machine Learning-assisted High-speed Combinatorial Optimization with Ising Machines for Dynamically Changing Problems

Yohei Hamakawa·Tomoya Kashimata·Masaya Yamasaki·Kosuke Tatsumura
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Abstract

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.

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Yohei Hamakawa, Tomoya Kashimata, Masaya Yamasaki, Kosuke Tatsumura. 2025-03-31. Machine Learning-assisted High-speed Combinatorial Optimization with Ising Machines for Dynamically Changing Problems. https://doi.org/10.1038/s41467-026-73725-6

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