基于优化相关特征的CNN-QNN混合模型的图像分类
Image Classification Using CNN-QNN Hybrid Model with Optimized Correlated Features
- Sogang University（西江大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出优化CNN特征相关性的CNN-QNN混合模型，在CIFAR-10等三项分类任务中，使特征平均相关性达0.5可提升分类准确率与稳定性，凸显QNN的性能潜力。
AI中文摘要：
本文提出一种优化卷积神经网络（CNN）特征间相关性的方法，将其作为量子神经网络（QNN）的输入以提升图像分类准确率。与现有采用正交分解作为预处理的方法不同，本文有意引入与QNN更具物理兼容性的相关特征，该设计利用QNN固有能力借助量子纠缠表征相关态——这是经典神经网络不具备的优势。本文假设使特征相关性与QNN的纠缠结构对齐可提升二分类性能。基于对QNN输出的数学推导，蒙特卡洛模拟表明特征间平均相关性为0.5时分类准确率最优。为验证该结论，本文在三项任务上评估量子-经典混合模型：CIFAR-10（汽车vs.卡车）、Fashion-MNIST（衬衫vs.外套）以及雷达微多普勒特征（机器狗vs.非机器）。为调控特征相关性，本文在CNN输出上引入相关性正则项，推动特征相关矩阵的非对角元素趋近目标常数。在所有数据集上，引入中间相关性相比低、高或未调控的相关性，均一致提升了准确率，同时降低了分类准确率的方差。这些结果表明，在不修改量子电路的情况下施加适度特征相关性，可通过使特征统计与QNN的纠缠结构对齐，提升分类准确率与稳定性。本研究凸显了随着量子比特数增加，QNN超越经典分类器性能的潜力。
英文摘要：
We propose a method to optimize the correlation among convolutional neural network (CNN) features that are used as inputs to quantum neural network (QNN) to enhance image classification accuracy. Unlike prior approaches that employ orthogonal decomposition as preprocessing, we intentionally introduce correlated features that are more physically compatible with QNN. This design leverages the QNN's inherent ability to exploit quantum entanglement for representing correlated states-an advantage unavailable to classical neural networks. We hypothesize that aligning feature correlations with the entanglement structure of QNN improves binary classification performance. Based on a mathematical derivation of QNN outputs, Monte Carlo simulations indicate that an average correlation between features of 0.5 yields optimal classification accuracy. To validate this finding, we evaluate a quantum-classical hybrid model on three tasks: CIFAR-10 (automobile vs. truck), Fashion-MNIST (shirt vs. coat), and radar micro-Doppler signatures (robotic dogs vs. non-robots). To regulate feature correlations, we introduce a correlation-regularization term on the outputs of the CNN, driving the off-diagonal entries of the feature correlation matrix toward a target constant. Across all datasets, inducing intermediate correlation consistently improved accuracy compared to low, high, or unregulated correlations, while also reducing classification accuracy variance. These results demonstrate that imposing moderate feature correlations-without modifying the quantum circuit-enhances classification accuracy and stability by aligning feature statistics with the QNN's entanglement structure. This study highlights the potential of QNN to surpass the performance of classical classifiers as more qubits become available.
