Pareto-optimal quantum kernel selection for unsupervised anomaly detection on real malware beaconing data

Quantum kernel methods are leading candidates for a practical quantum advantage in machine learning, but assessing that potential requires two quantities usually reported separately: how well a kernel performs on the task, and how far its geometry departs from the classical kernels available for the