Quantum-Classical Hybrid Cancer Classification System

The integration of quantum computing with classical machine learning presents novel approaches to solving complex problems. This research focuses on developing a Quantum-Classical Hybrid Cancer Classification System using a quantum variational circuit for feature extraction and a classical SVM model for comparison. Breast cancer datasets were analyzed to demonstrate the potential of hybrid quantum-classical models in medical diagnostics. The results show that the quantum model achieves comparable performance to classical models with distinctive computational efficiency, offering a promising pathway for the future of quantum machine learning in healthcare.