Machine Learning-assisted High-speed Combinatorial Optimization with Ising Machines for Dynamically Changing Problems
This work addresses the need for fast, adaptive optimization in real-world applications like wireless networks and finance, though it is incremental as it builds on existing Ising machine technology.
The authors tackled the challenge of using Ising machines for dynamically changing combinatorial optimization problems, such as TDMA scheduling, by developing a method that compresses the Ising model and uses machine learning for parameter estimation, achieving a speed advantage over conventional methods.
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.