Quantum Convolutional Neural Networks for Line Orientation Classification in Pixelated Images

Abstract

Quantum Convolutional Neural Networks (QCNNs) offer a promising avenue for image classification tasks due to their potential to leverage quantum properties for enhanced computational capabilities. In this paper, we explore the application of QCNNs for line orientation classification in pixelated images. Specifically, we investigate the differentiation between horizontal and vertical lines, a fundamental task in image processing and computer vision. We propose a QCNN architecture tailored to this task, leveraging quantum convolutional layers to extract features from pixelated images and classify line orientations. We demonstrate the effectiveness of our approach through experimental evaluation on benchmark datasets, comparing the performance of QCNNs with different optimizers. Our study integrated the QCNN operator and optimizer into Qiskit Machine Learning’s Neural Network Classifier, leveraging quantum computing techniques for classification tasks. Our QCNN model demonstrated a training accuracy of 71.43% and a test accuracy of 60.0%. Noteworthy observations include the failure of the SPSA optimizer to converge within the designated iterations, requiring twice the iterations compared to the COBYLA optimizer for convergence.