FAU Researchers Test Quantum Machine Learning for Lung Cancer Prediction

Insider Brief

• Florida Atlantic University researchers evaluated quantum machine learning methods for lung cancer prediction using a limited dataset of symptoms and risk factors.
• A D-Wave quantum annealing-based support vector machine achieved the highest reported accuracy, reaching 94.31% in simulation and 91.90% on quantum hardware.
• The researchers describe the findings as preliminary, noting that the dataset was augmented with synthetic samples and that further testing on independent clinical datasets is needed to assess generalizability.

PRESS RELEASE — Developing reliable machine learning models for healthcare 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.