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This paper explores the use of compact quantum-inspired recurrent models, specifically gated quantum-inspired Kolmogorov-Arnold network fast-weight programmers (QKAN-FWPs), for forecasting traffic matrices (TMs) under strict memory and training constraints. By adapting these models for multi-step forecasting of the Abilene TM, the authors benchmark their performance against traditional LSTM networks and classical fast-weight programmers. The G-QKANFWP variant achieves the lowest pooled root-mean-square error (RMSE) while operating with only 22.4% of the larger LSTM's resources, demonstrating a significant improvement in accuracy and efficiency for network traffic forecasting.
G-QKANFWP outperforms traditional LSTMs in traffic forecasting while using a fraction of the resources, redefining efficiency in network control.
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.
