Quantum networks are becoming increasingly important because of advancements in quantum computing and quantum sensing, such as recent developments in distributed quantum computing and federated quantum machine learning. Routing entanglement in quantum networks poses several fundamental as well as technical challenges, including the high dynamicity of quantum network links and the probabilistic nature of quantum operations. Consequently, designing hand-crafted heuristics is difficult and often leads to suboptimal performance, especially if global network topology information is unavailable. In this paper, we propose RELiQ, a reinforcement learningbased approach to entanglement routing that only relies on local information and iterative message exchange. Utilizing a graph neural network, RELiQ learns graph representations and avoids overfitting to specific network topologies – a prevalent issue for learning-based approaches. Our approach, trained on random graphs, consistently outperforms existing local information heuristics and learning-based approaches when applied to random and real-world topologies. When compared to global information heuristics, our method achieves similar or superior performance because of its rapid response to topology changes.

There is growing interest in point cloud content due to its central role in the creation and provision of interactive and immersive user experiences for extended reality applications. However, it is impractical to stream uncompressed point cloud sequences over communication networks to end systems because of their high throughput and low latency requirements. Several novel compression methods have been developed for efficient storage and adaptive delivery of point cloud content. However, these methods primarily focus on data metrics and neglect the influence on the actual Quality of Experience (QoE). In this paper, we conduct a user study with 102 participants to analyze the QoE of point cloud sequences and develop a QoE model that can enhance the quality of point cloud content distribution under dynamic network conditions. Our analysis is based on user opinions regarding two representative point cloud sequences, three different frame rates, three viewing distances, and two state-of-the-art point cloud compression libraries, Draco and V-PCC. The results indicate that the proposed models can accurately predict the users' quality perception, with frame rate being the most dominant QoE factor.