Quantum Generative Adversarial Network and Quantum Neural Network for Image Classification

Abstract

In this paper, a Quantum Neural Network (QNN) has been proposed using the Projected Quantum Kernel feature for an image classification task. The QCNN consists of four dense layers; the first layer collects the quantum data as an input and the fourth layer produced the classification output. Moreover, a Quantum Generative Advisory Network (QGAN) has been developed using the patching technique for enhancing the number of samples in the image dataset. The proposed QNN and QGAN are constructed using quantum filters. The MNIST handwritten digit dataset was used to train and test the QNN model performance on image classification. A binary classification dataset was created from the MNIST handwritten digit database using digits 0 and 6. The QGAN generated 221 samples on digits 0 and 6 classes. The generated samples were added to the training dataset for the QNN model. The size of the Filtered MNIST handwritten dataset was extended from 13779 to 14000 samples. There are 12,000 images are split for training and 2,000 images for testing. The principal component analysis technique was used to reduce the dimension of the data. The QNN was trained on the enhanced dataset using a GPU environment. The testing accuracy of the QNN model was 98.65 percent; it is superior to the traditional neural network.