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.