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

Silvie Illésová, Tomáš Bezděk, Vojtěch Novák, Ivan Zelinka, Stefano Cacciatore, Martin Beseda

TL;DR

This work tackles the gap hindering practitioners from adopting quantum‑enhanced learning by proposing a practical, three‑stage workflow that starts from a classical self‑training baseline, introduces a minimal quantum component, and then applies diagnostic feedback via QMetric to refine the hybrid architecture. The Iris dataset serves as a controlled demonstration, showing that a modest quantum embedding can substantially improve class separation and alignment with true labels when guided by diagnostics. The key contribution is a repeatable pipeline that enables non‑quantum experts to incorporate quantum components without specialized quantum knowledge, validated by a progressively improved HybridPlus model with deeper entanglement and focused feature space. The practical impact lies in providing an actionable pathway for practitioners to experiment with quantum resources and achieve tangible performance gains without deep quantum expertise. $A$ improves from about $0.31$ to $0.87$ on Iris in the reported setup, illustrating the potential of diagnostically guided hybrid learning.$

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.

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

TL;DR

This work tackles the gap hindering practitioners from adopting quantum‑enhanced learning by proposing a practical, three‑stage workflow that starts from a classical self‑training baseline, introduces a minimal quantum component, and then applies diagnostic feedback via QMetric to refine the hybrid architecture. The Iris dataset serves as a controlled demonstration, showing that a modest quantum embedding can substantially improve class separation and alignment with true labels when guided by diagnostics. The key contribution is a repeatable pipeline that enables non‑quantum experts to incorporate quantum components without specialized quantum knowledge, validated by a progressively improved HybridPlus model with deeper entanglement and focused feature space. The practical impact lies in providing an actionable pathway for practitioners to experiment with quantum resources and achieve tangible performance gains without deep quantum expertise. improves from about to on Iris in the reported setup, illustrating the potential of diagnostically guided hybrid learning.$

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.

Paper Structure

This paper contains 6 sections, 3 equations, 2 figures.

Table of Contents

  1. Introduction
  2. Methodology
  3. Minimal Hybrid Model
  4. Refined Hybrid Model
  5. Results
  6. Conclusions

Figures (2)

  • Figure 1: Scatter plots in the learned feature space. (a) classical baseline, (b) Quantum-FAST, (c) HybridPlus, and (d) ground truth labels. The quantum variants produce clearer class separation than the classical method, with HybridPlus aligning most closely with the true structure.
  • Figure 2: Proposed workflow with iterative refinement loop.