Adaptive Quantum-Enhanced Feature Distillation for Real-Time Anomaly Detection in Quantum-Secured Network Traffic

**Abstract:** This paper presents a novel methodology for real-time anomaly detection within quantum-secured network traffic. Leveraging adaptive quantum feature distillation (AQFD) coupled with a multi-layered evaluation pipeline (MLEP), our approach significantly enhances the precision and speed of identifying malicious activities compared to classical methods. AQFD dynamically optimizes feature extraction via quantum variational circuits and entangled measurement, intelligently distilling high-dimensional quantum traffic data into a compressed, salient feature vector. This refined feature vector is then subjected to rigorous evaluation via the MLEP, incorporating logical consistency, formula verification, impact forecasting, and reproducibility scoring, culminating in a highly reliable anomaly detection system. We demonstrate significant improvements in detection accuracy and latency through simulations incorporating realistic quantum key distribution (QKD) derived network topologies and adversarial attack scenarios. This work paves the way for robust and highly scalable quantum-enhanced security solutions crucial for protecting future quantum-enabled infrastructures.