混合量子-经典图像分类模型

Hybrid Quantum-Classical Model for Image Classification

September 14, 2025

作者: Muhammad Adnan Shahzad

cs.AI

摘要

本研究系统对比了混合量子-经典神经网络与纯经典模型在三个基准数据集（MNIST、CIFAR100和STL10）上的性能、效率和鲁棒性。混合模型将参数化量子电路与经典深度学习架构相结合，而经典模型则采用传统的卷积神经网络（CNN）。每个数据集均进行了50个训练周期的实验，评估指标包括验证准确率、测试准确率、训练时间、计算资源使用情况以及对抗鲁棒性（以epsilon=0.1的扰动进行测试）。关键发现表明，混合模型在最终准确率上持续超越经典模型，分别达到{99.38%（MNIST）、41.69%（CIFAR100）和74.05%（STL10）的验证准确率，相比之下，经典模型的基准分别为98.21%、32.25%和63.76%。值得注意的是，混合模型的优势随数据集复杂度增加而放大，在CIFAR100（+9.44%）和STL10（+10.29%）上表现最为显著。此外，混合模型的训练速度提高了5至12倍（例如，MNIST上每周期21.23秒对比108.44秒），参数数量减少了6%至32%，同时保持了更好的测试数据泛化能力。对抗鲁棒性测试显示，混合模型在简单数据集上显著更稳健（如MNIST上45.27%的鲁棒准确率对比经典的10.80%），但在复杂数据集如CIFAR100上两者鲁棒性相当（均约为1%）。资源效率分析指出，混合模型内存消耗更低（4-5GB对比经典的5-6GB），CPU利用率也更低（平均9.5%对比23.2%）。这些结果表明，混合量子-经典架构在准确率、训练效率和参数可扩展性方面提供了显著优势，尤其适用于复杂的视觉任务。

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 epsilon=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--12times 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 (sim1\% 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.