University of Florida and MD Anderson Radiologists Review Quantum Machine Learning for MRI, Chest X-Ray and CT: What the arXiv Primer of 23 October 2026 Finds
What radiologists at UF Jacksonville and MD Anderson found about quantum machine learning in imaging
Quentir Medicine Monitor
Evidence-based insights for quantum medicine.
Five radiologists from the University of Florida College of Medicine in Jacksonville and MD Anderson Cancer Center, writing with Harshada Gothankar of Johns Hopkins University, have produced a guide to quantum machine learning for practicing radiologists. The 15-page primer appeared on arXiv on 23 October 2026, with Mayur Virarkar of UF Health Jacksonville as corresponding author.
The authors start from a problem every imaging department knows. Deep learning models for volumetric MRI and CT need computing power and memory that grow quickly with image size, and large architectures such as ResNet, DenseNet and vision transformers are hard to deploy where hardware is limited. Quantum computing has been proposed as one answer, and the primer asks how far that answer has come in medical imaging.
Nearly all published systems are hybrid quantum-classical pipelines tested on small, downsampled benchmark datasets or on simulators, and the authors write that clinical deployment is not yet feasible. A radiologist reading the paper gets a map of a young research field, with its strongest claims and its weakest tests set side by side.
The paper is a narrative primer. The authors searched PubMed, arXiv and academic search engines through August 2026, prioritized peer-reviewed original studies, and state that they verified all citations against publisher records. They report no specific funding and no relevant conflicts of interest.
The first half teaches five concepts: qubits, superposition, entanglement, quantum gates and quantum circuits. The authors stress a point that vendor material often skips. An n-qubit register can hold 2n basis states at once, yet a measurement returns a single classical outcome, so any practical speedup depends on algorithm design, data encoding and measurement strategy.
Today's machines belong to the noisy intermediate-scale era, with tens to a few hundred qubits and no full error correction. The primer describes three platforms. Superconducting processors dominate medical imaging research and are reachable through IBM's cloud; trapped-ion systems hold their quantum states longer at smaller scale; photonic chips run at room temperature but remain hard to control and appear rarely in imaging studies.