Simulated Quantum Kernels Lost All 24 Matched Tests to a Default Classical SVM on Brain MRI and Breast Ultrasound: Journal of Imaging Informatics in Medicine, 21 September 2026

Quentir Medicine Monitor

Evidence-based insights for quantum medicine.

On 21 September 2026 the Journal of Imaging Informatics in Medicine published a study by four computer scientists in Lahore, Abu Dhabi and Manchester that put two quantum machine-learning classifiers against ordinary statistical tools on brain MRI scans and breast ultrasound images. In all 24 matched comparisons the top-scoring quantum model scored lower than the top-scoring classical model, and simulating the quantum models on ordinary computers took roughly 50 to more than 220 times as long as running the classical one.

The paper, by M. Usman Hashmi, M. Adnan Hashmi, Muazzam Ali and Raheem Sarwar, asks whether quantum kernels improve medical image classification once every model gets the same features, the same training budget and the same protection against data leakage. Its answer, for the settings it tested, is no: a standard RBF-SVM with its default settings outperformed both quantum kernels in every condition. The authors describe their design as a leakage-controlled benchmark, and that design is the reason the study deserves a close reading.

Many earlier papers on quantum classifiers for radiology and pathology reported encouraging accuracy figures. Several compared a tuned quantum model with a weak classical one, or tested on a single split of the data, where a small overlap between training and test images can inflate a score. This study sets out to give both sides the same conditions and then measure what is left.

For a radiology department or a hospital buyer, the result gives a practical reference point. When a vendor or research partner proposes a quantum classifier for imaging, the first question is whether it was compared with a strong, well-configured classical baseline under the same conditions. This paper shows how large the difference can be when that comparison is made carefully. It also lists the controls such a comparison needs: identical inputs, preprocessing fitted on training images only, several random seeds and reported calibration.