Hybrid Quantum Variational Autoencoders for Representation Learning
Pablo Rivas, Zhao Liang, Javier Orduz · 2021 International Conference on Computational Science and Computational Intelligence (CSCI) · 2021
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.