Machine Learning-assisted High-speed Combinatorial Optimization with Ising Machines for Dynamically Changing Problems
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912305250304000 |
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| author | Hamakawa, Yohei Kashimata, Tomoya Yamasaki, Masaya Tatsumura, Kosuke |
| author_facet | Hamakawa, Yohei Kashimata, Tomoya Yamasaki, Masaya Tatsumura, Kosuke |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_23966 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning-assisted High-speed Combinatorial Optimization with Ising Machines for Dynamically Changing Problems Hamakawa, Yohei Kashimata, Tomoya Yamasaki, Masaya Tatsumura, Kosuke Emerging Technologies Machine Learning 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. |
| title | Machine Learning-assisted High-speed Combinatorial Optimization with Ising Machines for Dynamically Changing Problems |
| topic | Emerging Technologies Machine Learning |
| url | https://arxiv.org/abs/2503.23966 |