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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.

"Our goal was to examine how different quantum learning approaches perform when the amount of training data is deliberately constrained," said Munir. "By evaluating multiple methods within a common experimental framework, we can better understand their capabilities and limitations and identify directions that warrant further investigation."

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. To balance the cancer and noncancer categories, they used a statistical technique that generates synthetic examples, producing an experimental dataset of 540 samples.

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