In recent years, Classical Convolutional Neural Networks (CNNs) have been
applied for image recognition successfully. Quantum Convolutional Neural
Networks (QCNNs) are proposed as a novel generalization to CNNs by using
quantum mechanisms. The quantum mechanisms lead to an efficient training
process in QCNNs by reducing the size of input from $N$ to $log_2N$. This paper
implements and compares both CNNs and QCNNs by testing losses and prediction
accuracy on three commonly used datasets. The datasets include the MNIST
hand-written digits, Fashion MNIST and cat/dog face images. Additionally, data
augmentation (DA), a technique commonly used in CNNs to improve the performance
of classification by generating similar images based on original inputs, is
also implemented in QCNNs. Surprisingly, the results showed that data
augmentation didn't improve QCNNs performance. The reasons and logic behind
this result are discussed, hoping to expand our understanding of Quantum
machine learning theory.

在本文中，通过在三个常用数据集上测试损失和预测准确率，实现和比较了传统卷积神经网络（CNNs）和量子卷积神经网络（QCNNs），并将常用于 CNNs 的数据增强技术应用于 QCNNs，结果表明数据增强并未提高 QCNNs 的性能，讨论了这一结果背后的原因和逻辑，以期拓展对于量子机器学习理论的理解。