Solving Cyber Hard Problems with Transparent Hybrid Quantum AI for Anomaly Detection

This working paper introduces a comprehensive architectural framework that integrates the Quantum-Inspired Lifecycle Interpretability System (QILIS) and the Quantum-Powered Anomaly Detector (Q-PAD) to address six foundational Cyber Hard Problems identified by the National Academies. The proposed architecture leverages quantum-inspired interpretability and real-time anomaly detection to provide transparent, modular, and lifecycle-aligned AI security for hybrid classical-quantum systems. Q-PAD enhances detection accuracy and contextual awareness through hybrid classical-quantum analytics, while QILIS ensures end-to-end explainability and governance alignment across all AI lifecycle phases. The framework emphasizes composability, operator configurability, and relevance-driven decision support, establishing a forward-looking foundation for resilient, transparent, and adaptable AI systems in structured communication environments.