Quantum Machine Learning (QML) promises computational advantages over Deep Learning (DL), but its effectiveness in practical network Traffic Classification (TC) remains underexplored. In this paper, we provide the first systematic use of eXplainable Artificial Intelligence (XAI) to interpret QMLbased classifiers for (mobile) network TC. We combine (a) SHAPbased attribution and (b) calibration analysis to compare QML and DL models across both small- and large-scale TC tasks. This dual perspective enables a deeper understanding of QML interpretability and reliability relative to DL in network TC, exposing key limitations and avenues for improvement.
Nascita et al. (Tue,) studied this question.