2026-06-17 株式会社東芝
図： 今回開発した「量子インスパイアード最適化フレームワーク」
＜関連情報＞
- https://www.global.toshiba/jp/technology/corporate/rdc/rd/topics/26/2606-03.html
- https://www.nature.com/articles/s41467-026-73725-6
機械学習支援によるイジングマシンを用いた動的に変化する問題に対する高速組み合わせ最適化 Machine learning-assisted high-speed combinatorial optimization with Ising machines for dynamically changing problems
Yohei Hamakawa,Tomoya Kashimata,Masaya Yamasaki & Kosuke Tatsumura
Nature Communications Published:16 June 2026
DOI:https://doi.org/10.1038/s41467-026-73725-6
Abstract
Quantum or quantum-inspired Ising machines have recently shown promise in solving combinatorial optimization problems in a short time. Real-world and practical applications, such as time division multiple access (TDMA) scheduling for wireless multi-hop networks, financial trading, and emerging in-vehicle systems, require solving those problems sequentially where the size and characteristics change dynamically. However, using Ising machines for practical deployment 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 build a machine learning model to estimate appropriate parameters using extensive training data. In TDMA scheduling for wireless multi-hop networks, our demonstration shows that the sophisticated system can adapt to changes in the problem and has a speed advantage over conventional methods.
