Dataset design and analysis for ML-assisted performance evaluation in QKD networks

Progress in monitoring and performance analysis in quantum key distribution (QKD) networks has been influenced by the limited availability of well-structured and publicly accessible datasets. As a result, this limitation forces researchers to rely on specifically designed data collections, which not only makes reproducibility harder but also makes it difficult to compare methodologies and results across different studies. To address this gap, we present publicly available synthetic datasets specifically designed for the evaluation of QKD system performance at the optical network level. These datasets encompass a wide range of configurations for point-to-point QKD, including two operational scenarios: (i) dark fiber, where only QKD traffic is transmitted, and (ii) coexistence of classical and quantum channels over the same fiber in the C-band. Additionally, the datasets incorporate variations in detector technologies and utilize two distinct analytical models for performance evaluation. By establishing a consistent benchmark, these datasets aim to accelerate the development of machine learning (ML)-driven automation in QKD networks. To demonstrate their utility, we developed and assessed a regression-based ML model, focused on predicting QKD performance metrics. Our findings underscore the value of these datasets in enabling robust benchmarking of ML approaches, promoting reproducibility, and facilitating meaningful cross-study comparisons. Ultimately, this contribution is expected to support the broader adoption of ML-based solutions for the automation and optimization of QKD networks.