Ensuring the reliability of quantum networks requires the accurate evaluation of fidelity-a metric representing link quality-and the selection of high-fidelity links. However, since fidelity estimation consumes a large number of measurements, an efficient method for identifying high-quality communication links with limited resources is desirable. Existing studies have primarily focused on identifying high-fidelity links between a pair of nodes, leaving adaptive resource allocation insufficiently explored in multi-destination environments where destinations differ in network importance (e.g., communication demand or the quantum memory capacity of nodes). In this study, we propose DaTopLinks (Demand-aware Top- $K$ HighFidelity Links), a method for efficiently identifying the top- $K$ destinations and their best link for each selected destination according to a utility function that combines destination importance and link fidelity. The algorithm introduces a dual-criterion link elimination mechanism that simultaneously performs intradestination link elimination and top- $K$ destination elimination. It also incorporates an early confirmation mechanism that allows early termination of measurements for destinations once their inclusion in the top- $K$ set and the identification of their best link are statistically guaranteed. In our theoretical analysis, we derive an upper bound on the sample complexity based on an effective gap that captures both inter-destination utility differences and intra-destination fidelity differences. Simulation results demonstrate that the proposed method functions effectively under depolarizing, dephasing, and bit-flip noise models.
Shun Yamachika, Yuto Kakihara, Shota Inoue et al.· Annual International Compute...· 0 citations
Ensuring deterministic and reliable communication is essential for in-vehicle networks supporting autonomous driving and safety-critical functions. Time-Sensitive Networking has emerged as a key enabler for such systems. Among its mechanisms, the IEEE 802.1Qcr Asynchronous Traffic Shaper (ATS) offers fine-grained traffic control without requiring global time synchronization. However, the practical deployment of ATS in Automotive Ethernet networks remains challenging due to the difficulty of parameter configuration. The performance of ATS strongly depends on the appropriate setting of key parameters such as the Committed Information Rate (CIR) and Committed Burst Size (CBSz), which are highly sensitive to both network topology and traffic workload. Conventional approaches relying on static configuration or empirical tuning may face difficulties in ensuring QoS when network conditions change. This paper proposes a method for automated, high-precision optimization of ATS parameters in automotive networks. We analyze the impact of key parameters—CIR and CBSz—on delay and frame loss, and develop a machine learning model to select optimal settings under dynamic traffic conditions. Our results reveal that proper ATS parameter configuration is essential for deterministic latency and reliability in Automotive Ethernet networks. CIR mainly governs bandwidth, affecting queuing delay and frame loss, while CBSz balances delay reduction against burst-induced congestion. Furthermore, tree-based ensemble models such as LightGBM and Gradient Boosting achieved high prediction accuracy and QoS satisfaction under varying traffic conditions.
Taisei Isobe, Han Nay Aung, Yasuhiro Yamasaki et al.· Annual International Compute...· 0 citations