MNIST Fashion Classification Using Quantum Convolutional Neural Networks

This paper explores the innovative application of Quantum Convolutional Neural Networks (QCNN) in the classification of the MNIST fashion dataset. Quantum computing presents novel opportunities for enhancing machine learning algorithms, particularly in handling complex datasets. The MNIST fashion dataset is a large collection of Zalando’s article images, serves as an ideal candidate for demonstrating the efficacy of QCNN. This paper dives into the implementation of QCNN, compares QCNN’s performance with traditional neural networks, and further evaluates QCNN’s potential in revolutionizing image classification tasks in the realm of Quantum Machine Learning.