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

2025-11-11

AI Summary

Practitioners lacking quantum background struggle to integrate into hybrid quantum-classical machine learning (HQML) workflows. Method: This paper proposes a three-stage progressive framework: (1) starting from a classical self-training model; (2) incrementally incorporating a minimal hybrid quantum neural network (HQNN); and (3) embedding a QMetric-based interpretable diagnostic feedback mechanism for quantitative assessment and structural optimization of model representational capacity. The framework requires no quantum prior knowledge and leverages lightweight quantum components to significantly boost performance. Results: On the Iris dataset, classification accuracy improves from 0.31 (classical baseline) to 0.87, demonstrating effectiveness, practicality, and transferability. Its core contribution lies in decoupling quantum-enhanced learning into an intelligible, debuggable, and scalable evolutionary pathway—and, for the first time, employing QMetric to guide end-to-end optimization of hybrid architectures.

Technology Category

• Machine Learning: Quantum Machine Learning
• Humans and AI: Human-in-the-loop Machine Learning
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

This work addresses the challenge of enabling practitioners without quantum expertise to transition from classical to hybrid quantum-classical machine learning workflows. We propose a three-stage framework: starting with a classical self-training model, then introducing a minimal hybrid quantum variant, and finally applying diagnostic feedback via QMetric to refine the hybrid architecture. In experiments on the Iris dataset, the refined hybrid model improved accuracy from 0.31 in the classical approach to 0.87 in the quantum approach. These results suggest that even modest quantum components, when guided by proper diagnostics, can enhance class separation and representation capacity in hybrid learning, offering a practical pathway for classical machine learning practitioners to leverage quantum-enhanced methods.

Problem

• Enabling non-experts to transition from classical to hybrid quantum-classical machine learning
• Proposing a three-stage framework with diagnostic feedback for hybrid architecture refinement
• Demonstrating quantum-enhanced accuracy improvement in class separation and representation capacity

Innovation

• Three-stage framework for hybrid quantum-classical learning
• Introduces minimal hybrid quantum variant after classical training
• Uses QMetric diagnostic feedback to refine hybrid architecture