A Deep Learning Model for Multiclass Image Classification Using Quantum CNN
Roopa Golchha, Gyanendra K. Verma · 2023
Technological developments in machine learning have opened up new avenues of advancement almost in every sector. The main challenge is communicating effectively with machines and enhancing their classification abilities. Convolutional Neural Network (CNN) is a deep learning technique in pattern recognition that effectively utilizes data correlation information. In contrast, CNN can only train effectively if the data or model dimension is manageable. Quantum CNN (QCNN) offers a new approach to this problem using quantum computing techniques to enhance the functionality of the classical CNN model. This research article provides an overview of the implementation of deep learning with quantum computing models for image classification. The paper also discusses the modelling approach for CNN in classical and quantum domains. In this paper, we present a model for multiclass image classification using the traditional CNN and the CNN with the quantum layer using the MNIST and CIFAR-10 datasets. The simulation results show that the proposed QCNN model has lower loss and comparatively more enhanced performance than the traditional CNN model. Also, the proposed model performs relatively better when compared with recent state-of-the-art approaches.