quantum-machine-learning-uav-anomaly-detection

name: quantum-machine-learning-uav-anomaly-detection description: "Leakage-free evaluation of quantum ML for UAV anomaly detection. Group-aware temporal protocol + three-mode feature audit + hybrid XGBoost-DRU classifier." category: quantum-ml

Quantum ML for Cyber-Physical Anomaly Detection in UAVs

arXiv: 2605.19233 (cs.CR, cs.LG, quant-ph) Authors: Carlos A. Durán Paredes, Javier E. León Calderón, Nicolás Sánchez Perea, German Darío Díaz, Camilo Segura Quintero

Core Methodology

Rigorous, leakage-free evaluation of quantum machine learning for unmanned aerial vehicle (UAV) anomaly detection on the multi-sensor TLM:UAV benchmark.

Three Key Contributions

1. Group-Aware Temporal Protocol (B2):

   • Partitions dataset into 10 contiguous TimeUS blocks
   • Evaluates over 10 seeds
   • Eliminates inflation from random stratified splits that mix neighboring samples

2. Three-Mode Feature Audit (Full/Loose/Strict):

   • Quantifies how much accuracy comes from instantaneous physical signals vs. contextual proxies
   • Proxies: cumulative energy, battery state, GPS trajectory
   • Strict mode removes proxy features for fair evaluation

3. Hybrid XGBoost + Data Reuploading (DRU) Classifier:

   • Benchmarked against 5 paired nonlinear controls under identical budgets
   • Controls: raw, PCA, polynomial-2, random-RBF, untrained DRU map

Key Finding

• Standalone DRU does NOT consistently match strongest classical baseline
• Trained-DRU hybrid is the only model whose mean F1 macro shifts upward from full to strict (+0.05)
• Lowest mean false-alarm rate under proxy-free evaluation

Implementation Patterns

• Use temporal partitioning (not random splits) for time-series evaluation
• Feature audit: separate physical signals from contextual proxies
• Hybrid quantum-classical: quantum feature map + classical classifier
• Evaluate under proxy-free conditions to avoid inflated metrics
• Open Qiskit 2.x implementation for reproducibility

Evaluation Protocol

1. Partition by time blocks (contiguous, not random)
2. Test across multiple seeds for variance estimation
3. Audit features: full → loose → strict (removing proxies)
4. Compare against multiple classical baselines under identical budgets
5. Report inter-seed variance honestly (don't overclaim incremental gains)

Applications

• Aerospace cybersecurity analytics
• NISQ-era quantum-enhanced anomaly detection
• Cyber-physical system security monitoring
• Multi-sensor fusion evaluation

Activation

quantum anomaly detection, UAV security, data reuploading, leakage-free evaluation, temporal protocol, feature audit, Qiskit, cyber-physical systems