arXiv:2606.27821v1 Announce Type: cross 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 (
The increasing sophistication of quantum-inspired AI techniques and the demand for efficient resource management in complex systems like network traffic forecasting drive this development.
This research could significantly improve the efficiency and accuracy of network traffic management, a critical component of digital infrastructure and AI operations, using novel AI architectures.
The ability to forecast traffic matrices more effectively under tight operational constraints may lead to more resilient and performant existing and future networks.
- · Telecommunications companies
- · Cloud infrastructure providers
- · AI research institutions
- · Network equipment manufacturers
- · Traditional traffic forecasting software vendors
- · Organizations with inefficient network management
Improved network efficiency and reduced operational costs for large-scale data systems.
Enhanced performance and reliability for services reliant on robust network infrastructure, including AI applications and distributed computing.
Accelerated development or adoption of quantum-inspired computing paradigms for practical, real-world problems beyond the purely theoretical.
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