| title | Parameter-Efficient Quantum-Inspired Fast Weight Programmers for Traffic-Matrix Forecasting |
|---|---|
| authors | Kuo-Chung Peng, Jiun-Cheng Jiang, Chun-Hua Lin, Tai-Yue Li, Nan-Yow Chen, Samuel Yen-Chi Chen |
| date | 2026-06-26 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2606.27821 |
| upvotes | 2 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | Traffic matrices (TMs) capture network-wide origin-destination demand and are central to traffic engineering, yet accurate whole-matrix forecasting remains challenging when prediction must be performed under the memory, update, and training-budget constraints of online network control. This paper investigates whether compact quantum-inspired recurrent models can provide effective TM forecasts without relying on dedicated graph, transformer, or diffusion modules. We adapt gated quantum-inspired Kolmogorov-Arnold network fast-weight programmers (QKAN-FWPs) to direct multi-step Abilene TM forecasting, where each model predicts the next 20 five-minute frames of a 144-channel origin-destination (OD) matrix from a two-hour history. We benchmark three QKAN placement variants against a matched-size long short-term memory (LSTM) network, a larger LSTM, and a classical gated fast-weight programmer under a shared fixed-budget training protocol. Among the evaluated recurrent models, G-QKANFWP achieves the best pooled root-mean-square error (RMSE), while using only 22.4% of the larger LSTM. It also outperforms both the matched-size LSTM and the classical G-FWP baseline, indicating that the gain is not due to gated fast-weight framework alone. Convergence and channel-wise analyses further show that the quantum-inspired variants obtain lower validation-loss area under the learning curve (AULC) than matched-size recurrent baselines, while G-QKANFWP and GQKAN-FWP achieve substantially more OD-channel wins. These results identify a classical slow programmer with a quantum-inspired fast programmer as a promising accuracy-efficiency design for resource-conscious network traffic-matrix forecasting. |
cite
@misc{peng2026parameter,
title={Parameter-Efficient Quantum-Inspired Fast Weight Programmers for Traffic-Matrix Forecasting},
author={Kuo-Chung Peng and Jiun-Cheng Jiang and Chun-Hua Lin and Tai-Yue Li and Nan-Yow Chen and Samuel Yen-Chi Chen},
year={2026},
eprint={2606.27821},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2606.27821}
}