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 same problem. We in...
Boaz Micah, Nadia Milazzo, Maissa Beji et al.
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