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This is an exploration using synthetic data in CSV format to apply QML models for the sake of binary classification. You can find here three different approaches. Two with Qiskit (VQC and QK/SVC) and one with Pennylane (QVC).
Q-SupCon integrates quantum principles into supervised contrastive learning, enhancing feature learning with minimal labeled data for efficient image classification, especially in medical applications.
In medical applications with limited training data, traditional self-supervised deep learning struggles for accuracy. This study combines self-supervised learning with Variational Quantum Classifiers (VQC) and Principal Component Analysis (PCA) for dimensionality reduction.