Quantum vs Classical: Performance Benchmarking of CNN and QCNN in Binary Image Classification

This paper presents a comprehensive comparison of Convolutional Neural Network (CNN) and Quantum Convolutional Neural Network (QCNN) in cloud-based environments, focusing on training accuracy and computational efficiency. Using datasets of dog and cat images, we conducted extensive experiments on GPU platforms. Our findings indicate that QCNN consistently outperform CNN across various batch sizes and epochs, demonstrating superior accuracy and reliability in binary image classification tasks. The results highlight the potential of QCNN to revolutionize deep learning by addressing the computational limitations of classical approaches. The study also underscores the importance of quantum computing in enhancing the scalability and efficiency of neural network models.