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

arXiv: https://arxiv.org/abs/2511.08205 · HTML: https://mlsanity.com/paper/2511.08205/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 54 references that are in the map.
- First posted on arXiv 11 Nov 2025.

## Builds on (most cited first, 5 of 5)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Challenges and Opportunities in Quantum Machine Learning](https://mlsanity.com/paper/2303.09491/) (2023)
- [A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks](https://mlsanity.com/paper/2205.05040/) (2022)
- [Qiskit Machine Learning: an open-source library for quantum machine learning tasks at scale on quantum hardware and classical simulators](https://mlsanity.com/paper/2505.17756/) (2025)
- [A Preliminary Investigation on the Usage of Quantum Approximate Optimization Algorithms for Test Case Selection](https://mlsanity.com/paper/2504.18955/) (2025)

## Built on by (most cited first, 1 of 1)

- [Quantum Machine Learning for Colorectal Cancer Data: Anastomotic Leak Classification and Risk Factors](https://mlsanity.com/paper/2604.13951/) (2026)

## Abstract (arXiv)

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.