Machine Learning-assisted High-speed Combinatorial Optimization with Ising Machines for Dynamically Changing Problems

• URL: http://arxiv.org/abs/2503.23966v2
• Date: Wed, 02 Apr 2025 11:48:35 GMT
• Title: Machine Learning-assisted High-speed Combinatorial Optimization with Ising Machines for Dynamically Changing Problems
• Authors: Yohei Hamakawa, Tomoya Kashimata, Masaya Yamasaki, Kosuke Tatsumura,
• Abstract summary: We show a method using embedded Ising machines to solve diverse problems at high speed without parameter tuning.<n>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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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