This research proposes parameter-efficient quantum-inspired fast weight programmers to forecast network-wide traffic matrices (TMs). The approach aims to provide accurate forecasts under strict memory and training budget constraints without relying on transformers, diffusion models, or dedicated graph modules. The study specifically adapts gated quantum-inspired architectures to optimize online network control.