  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.
