  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.
