How Florida Atlantic ran a lung cancer quantum support vector machine on D-Wave hardware

Quentir Medicine Monitor

Evidence-based insights for quantum medicine.

Engineers at Florida Atlantic University have taken their quantum classifiers off the simulator and onto a working quantum computer. In a study that predicts lung cancer from symptoms and risk factors, a quantum support vector machine running on a D-Wave Advantage processor classified 91.90 percent of test cases correctly, against 94.31 percent for the same method in simulation.

The paper, by Muhammad Minoar Hossain, Md. Hasibul Hassan Himal and Arslan Munir, appeared in Discover Quantum Science on 9 September 2026 as article 34 of volume 2. The university announced it on 2 November 2026 in a Florida Atlantic release that calls the findings preliminary. The study asks a narrow question about lung cancer prediction: how quantum learning methods behave when the training set is kept deliberately small.

For a hospital reader, the step worth noting is the hardware. In August Quentir reported that the same group's 90.26 percent heart-disease result came from a simulator. This time the team ran its highest-scoring model on D-Wave annealing hardware, on two D-Wave systems, and reported the hardware numbers next to the simulated ones.

The starting point is a public file, the Lung Cancer Dataset posted on Kaggle by Mysar Ahmad Bhat in 2023. It holds 309 entries with 15 input attributes, such as smoking, yellow fingers, wheezing, coughing and chest pain, and a yes-or-no lung cancer label. It contains no medical images, pathology results, laboratory values or cancer stages. The authors describe their work as "a preliminary screening-support and methodological study rather than a clinical diagnostic system."

The cancer and non-cancer groups in the file were unequal in size, so the team applied SMOTE, a technique introduced by Chawla and colleagues in the Journal of Artificial Intelligence Research in 2002 that creates synthetic examples by interpolating between real neighbors in the smaller class. That brought the file to 540 entries, 270 per class. A feature-selection step written as a QUBO, the binary optimization format that quantum annealers solve, then kept ten inputs: allergy, alcohol consumption, wheezing, swallowing difficulty, coughing, chest pain, peer pressure, yellow fingers, chronic disease and smoking.

The evaluation turns the usual split around. The 540 entries were divided into five partitions of 108. In each of five rounds the models trained on one partition and were tested on the remaining 432. Training on one fifth of the data is deliberate: it imitates the small cohorts that many hospital and rare-disease datasets offer.

The team compared three classifiers: quantum support vector machines, quantum k-nearest neighbors and quantum boosting. Gate-based versions ran through IBM's Qiskit, Google's Cirq and Xanadu's PennyLane, and annealing versions ran through OpenJij and D-Wave's Ocean software. Testing every method under one data preparation and one evaluation protocol is the paper's main methodological contribution, since many quantum machine learning papers test one method on one platform.

The D-Wave quantum support vector machine scored highest, at 94.31 percent in simulation. On hardware it reached 91.90 percent on the system D-Wave calls Advantage_system6 and 90.05 percent on Advantage_system4. The paper describes Advantage_system6 as a sixth-generation Advantage2 processor with the Zephyr topology. D-Wave's own solver documentation lists both systems as Advantage processors with the Pegasus topology and reserves the Advantage2 and Zephyr labels for solvers named Advantage2_system, so this post follows the manufacturer. D-Wave's hybrid solvers landed between 87.04 and 91.89 percent. The gate-based quantum k-nearest neighbors method reached 92.96 percent in Qiskit and 92.99 percent in PennyLane, so the top hardware result sits below two gate-based simulations in the same paper.

The gate-based side ran into a practical limit. When the team tried the Qiskit quantum support vector machine on an IBM quantum computer, the job called for 59,719,680 circuit shots, above IBM's system limit of 10,000,000. The authors cut that hardware test down to 50 samples, 30 for training and 20 for testing. Quantum kernel methods compare every pair of training examples, so their cost grows quickly with the size of the dataset, and that growth is a fair question for any vendor offering such a model.