量子混合模型在图像分类中更准更快，尤其适合复杂任务。
Hybrid Quantum-Classical Model for Image Classification
- 用量子电路融合经典CNN，构建混合神经网络
- 在复杂数据集上准确率最高提升10.29%，训练快12倍
- 参数少、内存低，适合资源受限的视觉任务
本研究系统比较了混合量子-经典神经网络与纯经典模型在三个基准数据集（MNIST、CIFAR100、STL10）上的表现，评估其性能、效率与鲁棒性。混合模型将参数化量子线路与经典深度学习架构结合，经典模型则采用传统卷积神经网络（CNN）。所有实验均进行50个训练周期，评估验证准确率、测试准确率、训练时间、计算资源使用及对抗鲁棒性（ε=0.1扰动）。结果显示，混合模型在最终准确率上持续优于经典模型：分别达到99.38%（MNIST）、41.69%（CIFAR100）、74.05%（STL10），对应经典基准为98.21%、32.25%、63.76%。其中，复杂数据集上优势显著，CIFAR100和STL10分别提升9.44%和10.29%。混合模型训练速度提升5–12倍（如MNIST每周期21.23秒对108.44秒），参数量减少6–32%。对抗鲁棒性测试显示，混合模型在简单数据集（如MNIST）更具韧性（45.27%鲁棒准确率对比经典10.80%），但在复杂数据集（如CIFAR100）上两者均脆弱（约1%鲁棒性）。资源分析表明，混合模型内存消耗更低（4–5GB vs. 5–6GB），CPU利用率更低（平均9.5% vs. 23.2%）。结果表明，混合量子-经典架构在准确性、训练效率和参数可扩展性方面具有显著优势，尤其适用于复杂视觉任务。
原文摘要 · Abstract (English)
This study presents a systematic comparison between hybrid quantum-classical neural networks and purely classical models across three benchmark datasets (MNIST, CIFAR100, and STL10) to evaluate their performance, efficiency, and robustness. The hybrid models integrate parameterized quantum circuits with classical deep learning architectures, while the classical counterparts use conventional convolutional neural networks (CNNs). Experiments were conducted over 50 training epochs for each dataset, with evaluations on validation accuracy, test accuracy, training time, computational resource usage, and adversarial robustness (tested with $ε=0.1$ perturbations).Key findings demonstrate that hybrid models consistently outperform classical models in final accuracy, achieving {99.38\% (MNIST), 41.69\% (CIFAR100), and 74.05\% (STL10) validation accuracy, compared to classical benchmarks of 98.21\%, 32.25\%, and 63.76\%, respectively. Notably, the hybrid advantage scales with dataset complexity, showing the most significant gains on CIFAR100 (+9.44\%) and STL10 (+10.29\%). Hybrid models also train 5--12$\times$ faster (e.g., 21.23s vs. 108.44s per epoch on MNIST) and use 6--32\% fewer parameters} while maintaining superior generalization to unseen test data.Adversarial robustness tests reveal that hybrid models are significantly more resilient on simpler datasets (e.g., 45.27\% robust accuracy on MNIST vs. 10.80\% for classical) but show comparable fragility on complex datasets like CIFAR100 ($\sim$1\% robustness for both). Resource efficiency analyses indicate that hybrid models consume less memory (4--5GB vs. 5--6GB for classical) and lower CPU utilization (9.5\% vs. 23.2\% on average).These results suggest that hybrid quantum-classical architectures offer compelling advantages in accuracy, training efficiency, and parameter scalability, particularly for complex vision tasks.
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