Florence and Barcelona Researchers Run a Quantum Diffusion Model's Noise Step on IBM's 133-Qubit Torino for Blood-Cell, Brain MRI and Rib CT Images: What arXiv 2609.31070 Shows
Quentir Medicine Monitor
Evidence-based insights for quantum medicine.
Generative AI can now draw convincing medical images: blood-cell smears, brain scans, slices of a CT volume. Hospitals and research groups want such synthetic images to enlarge small training sets, to share data without exposing patients, and to test diagnostic software on rare cases. The most successful generators today are diffusion models, which learn to turn random noise back into a plausible image. A preprint posted on 25 September 2026 asks whether a real quantum computer can take over one half of that process.
The paper, "Quantum Diffusion Models for Medical Image Analysis" (arXiv 2609.31070), comes from Francesco Aldo Venturelli and Miguel A. González Ballester of Universitat Pompeu Fabra and the Barcelona Supercomputing Center, Stefano Martina, Marco Parigi and Filippo Caruso of the University of Florence, and Alba Cervera-Lierta of the Barcelona Supercomputing Center. They built a hybrid quantum diffusion model in which the step that adds noise to an image runs on IBM's 133-qubit ibm_torino processor, while a classical neural network learns to remove that noise again. They then used it to generate synthetic medical images from three public collections: blood-cell photographs, brain MRI slices and 3D rib CT volumes.
The authors report that their hybrid model is competitive with a classical diffusion model of the same design, and better on some measures, especially for the 3D rib volumes. The comparison is limited: 100 generated samples per dataset, small images, and a classical baseline the team reproduced itself. No radiologist judged the images, and no diagnostic task was run on them.
That leaves a narrow but real result. It is one of the few medical-imaging studies in quantum machine learning that runs part of its pipeline on physical quantum hardware with public medical-image datasets, and it deserves a precise reading of what the hardware did and what the numbers show.
Tecnun's Synthetic Myelodysplastic-Syndrome Patients Were Seven-Bit Samples From IBM's ibm_basquecountry
Quentir Medicine Monitor
Evidence-based insights for quantum medicine.
The SintraREV trial randomized 61 patients. It ran from 15 February 2010 to 21 February 2018 across 22 university hospitals in Spain, France and Germany, and eight years of enrollment for 61 people is what a phase 3 study costs in a rare blood cancer. A preprint posted on 28 August 2026 takes the data from that trial and asks whether a quantum computer can generate synthetic patients good enough to stand alongside them.
Olatz Sanz Larrarte and nine co-authors published "A quantum generative model for in silico clinical trials using scarce training datasets" on arXiv on 28 August 2026, accepted for the proceedings of the CIBB 2026 conference. The author list crosses two worlds. Sanz Larrarte, Reza Dastbasteh, Pedro Crespo Bofill and Josu Etxezarreta Martinez work in the Department of Basic Sciences at Tecnun, the University of Navarra engineering school in San Sebastián. María Díez-Campelo of the hematology department at Hospital Universitario de Salamanca led SintraREV itself. Felipe Prosper and Ana Alfonso-Piérola practice at the Clínica Universidad de Navarra, Mikel Hernaez and Roberto Sanchez-Navarro sit at CIMA and DATAI in Pamplona, and Sara Capponi is at IBM Research in San Jose. The circuits ran on ibm_basquecountry, the 156-qubit Heron r2 processor in Donostia-San Sebastián.
Practical takeaway. The synthetic patients this pipeline produces are seven-bit strings. Sex, age, hemoglobin, platelets, neutrophils, treatment assignment and survival are each reduced to a single yes-or-no value, and the quantum processor samples from a distribution over those 128 possible patients. The fitting step that classical generative models spend hundreds of training epochs on happens on a classical computer before the quantum machine is touched.