A Fusion of Supervised Contrastive Learning and Variational Quantum Classifiers

In medical applications, machine learning often grapples with limited training data. Classical self-supervised deep learning techniques have been helpful in this domain, but these algorithms have yet to achieve the required accuracy for medical use. Recently quantum algorithms show promise in handling complex patterns with small datasets. To address this challenge, this study presents a novel solution that combines self-supervised learning with Variational Quantum Classifiers (VQC) and utilizes Principal Component Analysis (PCA) as the dimensionality reduction technique. This unique approach ensures generalization even with a small training dataset while preserving data privacy, a vital consideration in medical applications. PCA is effectively utilized for dimensionality reduction, enabling VQC to operate with just 2 Q-bits, overcoming current quantum hardware limitations, and gaining an advantage over classical methods. The proposed model was benchmarked against linear classification models using diverse public image datasets to validate its effectiveness. The results demonstrate remarkable accuracy, with achievements of 90% on PneumoniaMNIST, 90% on BreastMNIST, 80% on PathMNIST, and 80% on ChestMNIST medical datasets. Additionally, for non-medical datasets, the model attained 85% on Hymenoptera Ants & Bees and 90% on the Kaggle Cats & Dogs dataset.