Machine learning-assisted high-speed combinatorial optimization with Ising machines for dynamically changing problems

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. Quantum-inspired Ising machines can be used to address dynamically changing combinatorial problems. Here, the authors present an embedded approach that compresses models, avoids runtime parameter tuning, and uses ML for rapid adaptation, achieving faster TDMA scheduling in dynamic wireless networks than conventional methods.