Paper page - Parameter-Efficient Quantum-Inspired Fast Weight Programmers for Traffic-Matrix Forecasting

Source: https://huggingface.co/papers/2606.27821

Abstract

Quantum-inspired recurrent models using gated QKAN-FWPs demonstrate superior forecasting accuracy with reduced computational requirements compared to traditional LSTM networks for traffic matrix prediction.

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 compactquantum-inspired recurrent modelscan provide effective TM forecasts without relying on dedicated graph, transformer, or diffusion modules. We adaptgated 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-sizelong short-term memory(LSTM) network, a largerLSTM, and aclassical gated fast-weight programmerunder a shared fixed-budget training protocol. Among the evaluated recurrent models, G-QKANFWP achieves the best pooledroot-mean-square error(RMSE), while using only 22.4% of the largerLSTM. It also outperforms both the matched-sizeLSTMand 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.

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