量子分类模型在复杂任务中优于经典模型，尤其QNN表现更佳。
Performance Analysis of Quantum Support Vector Classifiers and Quantum Neural Networks
- 对比量子与经典模型在两类数据集上的表现
- 高复杂度下量子神经网络性能超越经典模型
- 推荐使用Qiskit框架实现更优优化效果
本研究对比了量子支持向量分类器（QSVC）和量子神经网络（QNN）与经典模型在机器学习任务中的表现。在Iris和MNIST-PCA数据集上评估发现，随着问题复杂度提升，量子模型普遍优于经典方法。尽管QSVC结果更稳定，但QNN在高复杂度任务中因更强的量子容量表现出更优性能。同时，超参数调优分析表明，特征映射与变分量子线路配置显著影响准确率。框架对比显示，Qiskit在本实验中提供更优的优化效率与执行性能。研究揭示了量子机器学习在复杂分类问题中的潜力，并为模型选择与优化策略提供参考。
原文摘要 · Abstract (English)
This study explores the performance of Quantum Support Vector Classifiers (QSVCs) and Quantum Neural Networks (QNNs) in comparison to classical models for machine learning tasks. By evaluating these models on the Iris and MNIST-PCA datasets, we find that quantum models tend to outperform classical approaches as the problem complexity increases. While QSVCs generally provide more consistent results, QNNs exhibit superior performance in higher-complexity tasks due to their increased quantum load. Additionally, we analyze the impact of hyperparameter tuning, showing that feature maps and ansatz configurations significantly influence model accuracy. We also compare the PennyLane and Qiskit frameworks, concluding that Qiskit provides better optimization and efficiency for our implementation. These findings highlight the potential of Quantum Machine Learning (QML) for complex classification problems and provide insights into model selection and optimization strategies
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