Early and accurate detection of diseases significantly improves treatment outcomes and reduces healthcare costs. Classical machine learning models have achieved notable success in medical diagnosis; however, they often face limitations when dealing with high-dimensional, noisy, and complex biomedical data (e.g., genomics, medical imaging, and electronic health records). Quantum machine learning (QML) offers the potential to capture intricate patterns through quantum superposition and entanglemen
This problem focuses on designing and developing a hybrid quantum machine learning system for early disease detection.
Early and accurate detection of diseases significantly improves treatment outcomes and reduces healthcare costs Classical machine learning models have achieved notable success in medical diagnosis; however, they often face limitations when dealing with high-dimensional, noisy, and complex biomedical data... Quantum machine learning (QML) offers the potential to capture intricate patterns through quantum superposition and entanglemen
This problem focuses on designing and developing a hybrid quantum machine learning system for early disease detection. The system will integrate classical pre-processing and feature engineering with quantum-enhanced learning models (such as quantum support vector machines, quantum neural networks, or variational quantum classifiers). It will be applied to biomedical datasets for the early identification of diseases (e.g., cancer, cardiovascular disorders, or neurological conditions). The system should support data ingestion, hybrid model training, prediction, explainability, and performance evaluation against purely classical baselines. Objectives • Design a hybrid quantum-classical machine learning architecture suitable for early disease detection. • Develop quantum-enhanced classific