Image Classification using Quantum Convolutional Neural Network

The unprecedented progress in the domain of quantum computing in the last few years has influenced researchers around the globe to solve multitudes of problems in this promising computing technology. This power of the quantum computer has allowed multitudes of computationally hard problems to be sped up exponentially over their classical counterparts. Along with such power, another promising application of quantum computing has been found in image processing and machine learning. Researches in both quantum image processing and quantum machine learning are still in their infancy but promise exceptional power over its classical counterparts. In this thesis, neural networks will be trained to determine parameters for various parametric quantum circuits to perform important classification tasks, such as image classification. But for image classification, features from the images must also be extracted and epresented in terms of qubits, requiring convolutional layers tailored for quantum techniques. This thesis aims to find good quantum convolutional neural network architectures for image classification with higher accuracy. This is still challenging due to increased cost and error with a higher number of qubits within a system. This thesis is expected to be important in the future direction of the research of quantum CNN.