From Classical to Hybrid: A Practical Framework for Quantum-Enhanced Learning

The recent study presents a three-stage framework designed to assist practitioners in moving from classical to hybrid quantum-classical machine learning workflows. This framework begins with a classical self-training model, introduces a minimal hybrid quantum variant, and incorporates diagnostic feedback through QMetric to refine the hybrid architecture. Notably, experiments conducted on the Iris dataset revealed that the refined hybrid model's accuracy improved dramatically from 0.31 in the classical approach to 0.87 in the quantum approach. These findings underscore the effectiveness of integrating even modest quantum components into machine learning processes, suggesting that with appropriate diagnostics, practitioners can significantly enhance class separation and representation capacity. This research offers a practical pathway for classical machine learning practitioners to leverage quantum-enhanced methods, marking a significant step forward in the field of artificial intelligen…