用嵌入式伊辛机实现动态优化，无需调参即可高速求解。
Machine Learning-assisted High-speed Combinatorial Optimization with Ising Machines for Dynamically Changing Problems
- 通过压缩模型和定制电路加速计算，实现快速求解。
- 在无线多跳网络调度中，比传统方法快且能自适应变化。
- 结合机器学习自动估算参数，适合实时动态场景。
量子或类量子伊辛机近期在短时间内解决组合优化问题方面展现出潜力。现实应用如无线多跳网络的时分多址（TDMA）调度和金融交易，需要连续求解规模与特性动态变化的问题。然而，使用伊辛机面临系统延迟高、大尺寸伊辛模型传输或云端访问耗时，以及每问题需手动调参等挑战。本文提出一种嵌入式伊辛机的组合优化方法，可在无运行时参数调整的情况下高速解决多样化问题。我们定制了基于分岔模拟的伊辛机算法与电路架构，压缩伊辛模型并加速计算，并构建机器学习模型，利用大量训练数据估计合适参数。在无线多跳网络的TDMA调度演示中，该系统展示了对问题变化的自适应能力，且相比传统方法具有显著速度优势。
原文摘要 · Abstract (English)
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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