An Empirical Evaluation of Quantum Kernel Stability in Network Intrusion Detection

This research shows that quantum kernel classifiers can become unstable even without hardware noise. It identifies seed-dependent classifier failures and links this instability to limitations in the quantum kernel representation. The findings also highlight the importance of reporting seed variability, worst-case performance, and class-specific metrics rather than relying only on average accuracy.