一句话：实现并评估 CNN + 八量子比特 VQC 的混合量子-经典图像分类器，总结量子软件工程问题与质量保障实践。
本文面向 NISQ 硬件限制下的混合量子-经典系统，构建并实证分析了一个用于手写数字二分类的混合分类器：将卷积神经网络（CNN）与八量子比特变分量子电路（VQC）结合，基于 PennyLane 与 PyTorch 实现。在 MNIST 的 1984 个测试样本上，该实现取得 99.85% 的峰值测试准确率，仅出现三个错误。除性能指标外，作者从量子软件工程（QSE）视角报告了若干关键工程问题，包括量子-经典接口设计、伴随（adjoint）微分、特征降维以及后端可移植性，并提出七项针对混合量子软件系统的质量保障实践。结果表明，借助现有开源工具与模拟器可以成功构建混合量子-经典架构，并为混合 QSE 的未来研究与实践提供了有效建议。
The intersection of quantum processing and classical machine learning has spawned hybrid quantum-classical systems — practical system designs that attempt to use quantum potential in the limitations of modern Noisy Intermediate-Scale Quantum (NISQ) hardware. This paper introduces the development, deployment, and empirical analysis of a hybrid quantum-classical image classifier (which combines a Convolutional Neural Network (CNN) with an eight-qubit Variational Quantum Circuit (VQC)) in the binary classification of handwritten digits. This implementation, implemented in PennyLane and PyTorch, reaches a peak test accuracy of 99.85% across 1,984 test samples of the MNIST system, and only three errors are made. In addition to performance measures, the work presents an approach based on Quantum Software Engineering (QSE) by reporting major engineering issues, such as quantum-classical interface design, adjoint differentiation, feature-dimensionality reduction, and backend portability, and suggestion of seven quality-assurance practices of hybrid quantum software systems. The results demonstrate that it is possible to manufacture successfully hybrid quantum-classical architectures with the help of existing open-source tools and simulators, and provide future research and practice with hybrid QSE with effective advice.
@inproceedings{AliAKR26,
title = {Hybrid Quantum-Classical Image Classification: Installation, Evaluation and Software Engineering Lessons},
author = {Haider Ali and Muhammad Azeem Akbar and Arif Ali Khan and Saima Rafi},
booktitle = {FSE},
year = {2026},
doi = {10.1145/3803437.3807521}
}