Abdella Battou

Abderrahim Amlou, Amar Abane, Anouar Rahmouni et al.

Quantum networking testbeds lack a distinct plane for coordinating distributed measurements and collecting experimental data across heterogeneous devices. To address this gap, we present the Measurement Plane, a dedicated plane that complements the data, control, and management planes rather than replacing or extending their pipelines. The contribution is presented as a distributed framework that organizes measurement functions into four layers: application, experiment coordination, capability, and resource agents. Our design separates user workflows from device-specific control. We implemented the framework as containerized microservices connected through publish--subscribe messaging, and validated it on a two-node quantum networking setup connected by an optical network. The framework successfully coordinated remote nodes to execute coincidence measurement and polarization entanglement distribution experiments with visibility interference of up to 98 percent. This evaluation demonstrated the effectiveness of the framework for supporting complex, distributed quantum experiments, enabling online measurement and feedback, and significantly reducing manual configuration and execution effort.

Amar Abane, Anis Bekri, Abdella Battou et al.

Efficiently processing and interpreting network data is critical for the operation of increasingly complex networks. Recent advances in Large Language Models (LLM) and Retrieval-Augmented Generation (RAG) techniques have improved data processing in network management. However, existing RAG methods like VectorRAG and GraphRAG struggle with the complexity and implicit nature of semi-structured technical data, leading to inefficiencies in time, cost, and retrieval. This paper introduces FastRAG, a novel RAG approach designed for semi-structured data. FastRAG employs schema learning and script learning to extract and structure data without needing to submit entire data sources to an LLM. It integrates text search with knowledge graph (KG) querying to improve accuracy in retrieving context-rich information. Evaluation results demonstrate that FastRAG provides accurate question answering, while improving up to 90% in time and 85% in cost compared to GraphRAG.