Researchers Explore Quantum Computing to Predict Lung Cancer with Limited Data | Newswise

This challenge motivates research into whether quantum computing approaches can learn useful patterns from limited symptom and risk-factor data.

The study, published in Discover Quantum Science, a Springer Nature journal, “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.

The study examined two major approaches to quantum computing: quantum annealing and gate-based computing.

The authors acknowledge FAU and D-Wave for providing access to D-Wave’s quantum computing hardware.