A hybrid quantum-classical approach for liver disease detection using quantum machine learning
Laura María Donaire, Gloria Ortega, Francisco José Orts +2 more
Quantum Machine Learning (QML) combines principles of quantum computing with tradicional Machine Learning (ML) to explore computational advantages in data processing and model efficiency. With the rise of Noisy Intermediate-Scale Quantum (NISQ) devices, hybrid quantum-classical approaches are gaining momentum, especially in domains requiring high precision such as healthcare. In this work, we investigate whether hybrid quantum computing can enhance certain aspects of classical machine learning, specifically in dataset balancing and the complexity of the neural network involved in training. To this end, we use the Indian Liver Patient Dataset as a case study to determine the presence of liver disease. We present the methodology for developing ‘QML-Liver,’ a hybrid approach that seamlessly integrates classical and quantum machine learning techniques. This includes data preprocessing, model design, and optimal configuration. Our results demonstrate that ‘QML-Liver’ improves key performance metrics, such as accuracy and precision. Additionally, we successfully reduce the number of required qubits to just one, making practical deployment more feasible. These findings underscore the potential of Quantum Machine Learning for medical diagnostics, particularly in the NISQ era.
Engineering Applications of Artificial Intelligence•Nov 22, 2025•6 citations