发表机构
Multiverse Computing; IQM Quantum Computers; Tecnun – University of Navarra; Allianz Quantum Hub（Multiverse Computing; IQM量子计算机公司; 特昆——纳瓦拉大学; 安联量子中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出无监督多目标协议，同时优化NPD和几何差异，从帕累托前沿选择量子核，用于恶意软件信标检测，发现投影核几何差异大但性能略超基线，保真度核性能最优。
AI 中文摘要
量子核方法是机器学习中实现实际量子优势的主要候选方案，但评估这种潜力需要两个通常分开报告的量：核在任务上的表现如何，以及其几何结构与同一问题可用的经典核的偏离程度。我们引入了一种完全无监督的多目标协议，同时优化归一化伪差异（NPD）——一种无标签的异常检测质量代理指标，以及与调优的经典参考核的几何差异（GD），从所得的帕累托前沿中选择模型。我们将其应用于真实网络流量中的恶意软件信标检测，使用具有保真度和投影量子核的单类支持向量机，涵盖四种数据编码，在模拟器和IQM的20量子比特Garnet处理器上进行。仅基于NPD引导的选择找到了一个保真度核，其性能优于调优的经典基线，但几何差异太小，无法证明该增益是量子性的。投影核达到了远大于的几何差异；帕累托选择的核仅略微超过基线（AUC 0.782对0.765，相对于该参考核g_{C→Q}≈89>√N），仍低于NPD选择的保真度核（0.840）。
英文摘要
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 ($0.840$).
Comments13 pages, 5 figures
