Developing reliable machine learning models for health care can be challenging when only small datasets are available. This challenge motivates research into whether quantum computing approaches can learn useful patterns from limited symptom and risk-factor data.

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, and director of the Intelligent Systems, Computer Architecture, Analytics, and Security Laboratory, 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.

The study, published in Discover Quantum Science, “Inferring lung cancer under limited learning data through gate-based and annealing-based quantum techniques,” provides a common framework for evaluating different quantum machine learning methods under the same data preparation and testing conditions.