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

arXiv: https://arxiv.org/abs/2503.23966 · HTML: https://mlsanity.com/paper/2503.23966/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 73 references that are in the map.
- First posted on arXiv 31 Mar 2025.

## Builds on (most cited first, 11 of 11)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [BinaryConnect: Training Deep Neural Networks with binary weights during propagations](https://mlsanity.com/paper/1511.00363/) (2015)
- [Ising formulations of many NP problems](https://mlsanity.com/paper/1302.5843/) (2013)
- [Finding Near-Optimal Independent Sets at Scale](https://mlsanity.com/paper/1509.00764/) (2015)
- [Massively Parallel Probabilistic Computing with Sparse Ising Machines](https://mlsanity.com/paper/2110.02481/) (2021)
- [Simulated bifurcation for higher-order cost functions](https://mlsanity.com/paper/2211.09296/) (2022)
- [Correlation-diversified portfolio construction by finding maximum independent set in large-scale market graph](https://mlsanity.com/paper/2308.04769/) (2023)
- [Efficient and Scalable Architecture for Multiple-chip Implementation of Simulated Bifurcation Machines](https://mlsanity.com/paper/2311.17370/) (2023)
- [Enhancing In-vehicle Multiple Object Tracking Systems with Embeddable Ising Machines](https://mlsanity.com/paper/2410.14093/) (2024)
- [Statistical Delay Bound for WirelessHART Networks](https://mlsanity.com/paper/1607.08102/) (2016)

## Abstract (arXiv)

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.