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。

数据增强对 QCNN 的影响

Impact of Data Augmentation on QCNNs

This paper presents a comprehensive evaluation of the potential of Quantum
Convolutional Neural Networks (QCNNs) in comparison to classical Convolutional
Neural Networks (CNNs) and Artificial / Classical Neural Network (ANN) models.
With the increasing amount of data, utilizing computing methods like CNN in
real-time has become challenging. QCNNs overcome this challenge by utilizing
qubits to represent data in a quantum environment and applying CNN structures
to quantum computers. The time and accuracy of QCNNs are compared with
classical CNNs and ANN models under different conditions such as batch size and
input size. The maximum complexity level that QCNNs can handle in terms of
these parameters is also investigated. The analysis shows that QCNNs have the
potential to outperform both classical CNNs and ANN models in terms of accuracy
and efficiency for certain applications, demonstrating their promise as a
powerful tool in the field of machine learning.

本文研究量子卷积神经网络与经典卷积神经网络、人工 / 经典神经网络模型之间的潜力差异，研究表明，根据输入大小和批量大小，量子卷积神经网络有可能在准确性和效率上胜过经典卷积神经网络和人工神经网络，表现出作为机器学习领域中强大工具的潜力。