Unmanned aerial vehicles (UAVs) are cyber-physical systems whose attack surface spans networked avionics and on-board sensor fusion: a compromised GPS or battery module can mimic a benign mission segment and evade naive anomaly detectors. We present a leakage-free evaluation of quantum machine learning for UAV anomaly detection on the multi-sensor TLM:UAV benchmark. Three contributions support the study. (i) A group-aware temporal protocol (B2) partitions the dataset into ten contiguous TimeUS blocks and evaluates over ten seeds, eliminating the inflation produced by random stratified splits that mix neighbouring samples. (ii) A three-mode feature audit (full/loose/strict) quantifies how much accuracy stems from instantaneous physical signals versus contextual proxies (cumulative energy, battery state, GPS trajectory). (iii) A hybrid XGBoost + Data Reuploading (DRU) classifier is benchmarked against five paired non-linear controls (raw, PCA, polynomial-2, random-RBF, and an untrained DRU map) under identical budgets. The standalone DRU does not consistently match the strongest classical baseline across seeds; however, the trained-DRU hybrid is the only model whose mean F1 macro shifts upward from full to strict (+0.05), a directional signal that the per-seed standard deviations prevent from being interpreted as a statistically established difference. The trained-DRU hybrid also records the lowest mean false-alarm rate under proxy-free evaluation, subject to the inter-seed variance reported. We frame this as an incremental, reproducible quantum-enhanced hybrid benefit, and provide an open Qiskit 2.x implementation as a benchmark for cybersecurity analytics in NISQ-era aerospace systems.
面向无人机信息物理异常检测的量子机器学习：基于代理审计特征集的无泄漏评估 / Quantum Machine Learning for Cyber-Physical Anomaly Detection in Unmanned Aerial Vehicles: A Leakage-Free Evaluation with Proxy-Audited Feature Sets
本文提出了一种无泄漏评估方法，在无人机多传感器数据上严格测试了量子机器学习（QML）的异常检测性能，发现混合量子-经典模型能在去除时间数据泄漏后保持更低的误报率，为近中等规模量子（NISQ）时代的航空安全分析提供了可复现的基准。
源自 arXiv: 2605.19233
