A research team led by Arslan Munir, Ph.D., professor in Florida Atlantic University’s Department of Electrical Engineering and Computer Science within the College of Engineering and Computer Science, evaluated quantum machine learning approaches in simulation and on quantum hardware using symptom and risk-factor data for lung cancer prediction under limited training data. Published in Discover Quantum Science, a Springer Nature journal, the study compared three classification methods across five computing frameworks. The best-performing configuration, a quantum support vector machine implemented using D-Wave Quantum Inc. technology, achieved 94.31% accuracy in simulation and 91.90% on D-Wave’s AdvantageTM annealing quantum computers. The researchers began with a publicly available dataset containing 309 records with information such as smoking history, coughing, wheezing, fatigue and chest pain, along with a lung cancer label, and used a statistical technique that generates synthetic examples to balance the cancer and non-cancer categories, producing an experimental dataset of 540 samples. These additional samples did not represent newly recruited patients. The results come from an experimental dataset expanded through synthetic data augmentation and represent preliminary research rather than a clinically validated diagnostic tool.