2026-09-04 株式会社東芝
図1：今回開発した「量子インスパイアードリアルタイムブラックボックス最適化技術」
＜関連情報＞
- https://www.global.toshiba/jp/technology/corporate/rdc/rd/topics/26/2609-01.html
- https://www.nature.com/articles/s41467-026-76069-3
組み込みイジングマシンを用いた動的離散環境向けリアルタイムブラックボックス最適化 Real-time black-box optimization for dynamic discrete environments using embedded Ising machines
Tomoya Kashimata,Yohei Hamakawa,Masaya Yamasaki & Kosuke Tatsumura
Nature Communications Published:03 September 2026
DOI:https://doi.org/10.1038/s41467-026-76069-3
Abstract
Many real-time systems require the optimization of discrete variables. Black-box optimization (BBO) algorithms and multi-armed bandit (MAB) algorithms perform optimization by repeatedly taking actions and observing the corresponding immediate rewards without any prior knowledge. Recently, a BBO method using an Ising machine has been proposed to find the best action represented by a combination of discrete values and maximize the immediate reward in static environments. By contrast, real-time systems operate in dynamic environments and necessitate MAB algorithms that maximize the average reward over repeated trials. Due to the enormous number of actions resulting from the combinatorial nature of discrete optimization, conventional MAB algorithms cannot effectively optimize actions for dynamic, discrete environments. Here, we show a heuristic method to maximize the average reward for dynamic discrete environments by extending the BBO method, in which an Ising machine efficiently explores actions while accounting for interactions between variables and environmental changes. We demonstrate the adaptability to dynamic environments of the proposed method in a wireless communication system with moving users.
