Q-SupCon: Quantum-Enhanced Supervised Contrastive Learning Architecture within the Representation Learning Framework

Abstract

In the evolving landscape of data privacy regulations, the challenge of providing extensive data for robust deep classification models arises. The accuracy of these models relies on the amount of training data, due to the multitude of parameters that require tuning. Unfortunately, obtaining such ample data proves challenging, particularly in domains like medical applications, where there is a pressing need for robust models for early disease detection but a shortage of labeled data. Nevertheless, the classical supervised contrastive learning models, have shown the potential to address this challenge up to a certain limit, by utilizing deep encoder models. However, recent advancements in quantum machine learning enable the extraction of meaningful representations from extremely limited and simple data. Thus, replacing classical counterparts in classical or hybrid quantum-classical supervised contrastive models enhances feature learning capability with minimal data. Therefore, this work proposes the Q-SupCon model, a fully quantum-powered supervised contrastive learning model comprising a quantum data augmentation circuit, quantum encoder, quantum projection head, and quantum variational classifier, enabling efficient image classification with minimal labeled data. Furthermore, the novel model attains 80%, 60%, and 80% test accuracy on MNIST, KMNIST, and FMNIST datasets, marking a significant advancement in addressing the data scarcity challenge.