Hybrid Quantum Variational Autoencoders for Representation Learning

Representation learning is a standard area that has seen many improvements based on machine learning advances. Quantum machine learning advances are now spreading across different application areas such as representation learning. This paper introduces a novel hybrid quantum machine learning approach to representation learning by using a quantum variational circuit that is trainable with traditional gradient descent techniques. We use marketing data to showcase the learning potential of our model.