Domain-Aware Hybrid Quantum Learning via Correlation-Guided Circuit Design for Crime Pattern Analytics

– Developed a comprehensive quantum-classical comparison for crime analytics with statistical validation through cross-validation across quantum, classical, and hybrid paradigms.
– Proposed a novel quantum circuit architecture that utilizes crime feature correlations through targeted entanglement based on Spearman correlation analysis. — Implemented hybrid integration strategies, including Q→C (quantum feature extraction followed by classical classification) and C→Q (classical dimensionality reduction followed by quantum modeling).