Quantum Convolutional Neural Network for Image Classification
From self-driving cars to medical diagnoses, machine learning (ML) has revolutionized our world in the last few decades. Additionally, Quantum Computing has considerable promise for the future, with superposition and entanglement making quantum algorithms much more efficient and effective than their classical counterparts. Quantum ML aims to apply these Quantum principles to the groundbreaking field of ML, such as Image Classification. Convolutional Neural Networks are the most effective for classification, so we study a Quantum Convolutional Neural Network (QCNN) that is trained to distinguish between handwritten numbers in the MNIST dataset and compared to a similar-sized classical network. By tuning the QCNN and Quantum Encoding, the QCNN achieved comparable accuracy to the classical network.
encoding.py: Classical and quantum image encodingmodels.py: QCNN and fair classical modelmain.py: Runs model training