Quantum Computing Could Predict Lung Cancer with Limited Data

Researchers are investigating whether quantum computers can identify useful patterns in small datasets of health records. Creating reliable computer models for healthcare is very challenging because there isn’t much patient data available.

A team led by Arslan Munir, Ph.D., a professor at Florida Atlantic University, looked at whether quantum systems could help predict lung cancer when information is scarce. Their study is published in the Springer Nature journal Discover Quantum Science.

“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.”

Testing Quantum Hardware

The team began with a public dataset of 309 records that tracked details such as smoking history, coughing, fatigue, and chest pain. They used a statistical technique to create synthetic examples, bringing the test pool to 540 samples without recruiting new patients. After narrowing the list down to 10 key variables, they trained models on just 108 samples at a time and tested them on the remaining 432.

The researchers tested both gate-based quantum setups (the primary paradigm for universal quantum computing) and quantum annealing systems (a highly specialized type of quantum computer designed to solve one massive category of problems).

“The team evaluated implementations across Qiskit, Cirq, PennyLane, OpenJij and D-Wave, enabling comparisons across several software frameworks and computational approaches,” the press release stated.

D-Wave’s annealing computers, paired with a quantum support vector machine method, hit 94.31% accuracy in simulation and 91.90% on actual quantum hardware.

“FAU’s research highlights how D-Wave’s annealing quantum computers can be used to advance AI and machine learning,” said Catherine Potts, Ph.D., manager of technical account management at D-Wave Quantum Inc. “By applying our technology to a challenging healthcare use case, the researchers demonstrate its potential to extract valuable insights in data-constrained environments, opening new possibilities for quantum-powered AI across healthcare and other data-intensive industries.”

Too Early to Tell

“This study reflects Florida Atlantic’s efforts to connect quantum computing research with meaningful application questions,” said Stella Batalama, Ph.D., dean of FAU’s College of Engineering and Computer Science. “Careful comparisons and testing on quantum hardware help build the evidence needed to assess where these technologies may be useful and what challenges remain.”

The researchers emphasized that these results are early. These tests relied on symptoms and risk factors, not lab results, tissue scans, or cancer stages. Future work will test bigger datasets from multiple institutions.

“These results provide a starting point for further evaluation,” said Munir. “The next step is to determine how well these approaches generalize to independent clinical datasets and whether they can offer useful support within carefully validated healthcare workflows.”