aiqbench analysis
This study presents a rigorous evaluation of quantum kernel methods for unsupervised anomaly detection, specifically targeting malware beaconing in real network traffic. By optimizing the trade-off between task performance and geometric distance from classical baselines, the research demonstrates that while certain quantum kernels achieve higher accuracy, they often fail to exhibit sufficient geometric divergence to certify a genuine quantum advantage over tuned classical models. The work provides critical empirical data on the current limitations of near-term quantum hardware in practical security applications.
Pareto-optimal quantum kernel selection for unsupervised anomaly detection on real malware beaconing data
arXiv:2610.09717v1 Announce Type: cross Abstract: 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 introduce a fully unsupervised, multi-objective protocol that optimises simultaneously the normalised pseudo discrepancy (NPD), a label-free proxy for anomaly detection quality, and the geometric difference (GD) to a tuned classical reference kernel, selecting models from the resulting Pareto front. We apply it to malware beaconing detection in real network traffic, using a one-class support vector machine with fidelity and projected quantum kernels over four data encodings, on simulators and on IQM's 20-qubit Garnet processor. NPD-guided selection alone finds a fidelity kernel that beats the tuned classical baseline, but with a geometric difference too small to certify the gain as quantum. Projected kernels reach far larger geometric differences; the Pareto-selected one only marginally exceeds the baseline (AUC $0.782$ versus $0.765$, $g_{C\to Q}\approx 89>\sqrt{N}$ relative to that reference kernel), still below the NPD-selected fidelity kernel ($0.840$).
Abstract: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 introduce a fully unsupervised, multi-objective protocol that optimises simultaneously the normalised pseudo discrepancy (NPD), a label-free proxy for anomaly detection quality, and the geometric difference (GD) to a tuned classical reference kernel, selecting models from the resulting Pareto front. We apply it to malware beaconing detection in real network traffic, using a one-class support vector machine with fidelity and projected quantum kernels over four data encodings, on simulators and on IQM's 20-qubit Garnet processor. NPD-guided selection alone finds a fidelity kernel that beats the tuned classical baseline, but with a geometric difference too small to certify the gain as quantum. Projected kernels reach far larger geometric differences; the Pareto-selected one only marginally exceeds the baseline (AUC $0.782$ versus $0.765$, $g_{C\to Q}\approx 89>\sqrt{N}$ relative to that reference kernel), still below the NPD-selected fidelity kernel ($0.840$).
Quantum Physics (quant-ph); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
arXiv:2610.09717 [quant-ph]
(or arXiv:2610.09717v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2610.09717 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 283 words
Related coverage
[AINews] not much happened today
Latent Space · Oct 3, 2026
Quantum Computing Is Emerging as a Tool for Tomorrow Electrical Grids
powermag.com · Jul 21, 2026
AWS and JPMorganChase collaborate to advance quantum computing R&D
AWS · Aug 5, 2026
News
Quantum Computing Report · Jun 7, 2026