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.
RPI and Kipu Quantum Ran Autism Biomarker Selection on IBM's 120-Qubit Nighthawk Processors: What the 24 September 2026 arXiv Preprint Found
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Evidence-based insights for quantum medicine.
A blood sample from a young child can yield hundreds of metabolite measurements, while a typical autism biomarker study enrolls a few dozen children. Choosing which handful of those measurements a diagnostic model should rely on is a hard combinatorial search. A team at Rensselaer Polytechnic Institute in Troy, New York, working with the Berlin company Kipu Quantum, posted a paper on arXiv on 24 September 2026 that runs this search on IBM quantum processors.
The paper, "Quantum Feature Selection for Biomedical Data Analysis" by Hongbin Liu, Robert Lahmann, Benjamin Campbell, Zhemin Zhang, Zhiding Liang, Siona Bapat and Juergen Hahn, poses metabolomic feature selection as a binary optimization problem and solves it on two IBM Nighthawk processors, ibm_rensselaer and ibm_miami, each with 120 qubits. The test data come from three published studies, two of blood plasma and one of the gut microbiome, in children with autism spectrum disorder and typically developing peers. The quantum solver picked five-feature subsets that classified about as well as several standard classical methods, and the authors state plainly that their results do not claim a quantum advantage.
For clinicians and hospital laboratories the interest lies in the diagnostic problem underneath. Blood-based autism screening has been studied at least since 2012, when the oldest of the three datasets used here was published, and every such test depends on a short, stable list of markers. This preprint tests whether current quantum hardware can help build that list. It does not test a diagnostic, and it enrolled no new patients.
The work is a first hardware feasibility study on retrospective data. It has not yet been peer reviewed, and its datasets are small: the largest group has 83 children with an autism diagnosis and 76 without.
The Minimal-Basis Run Gave T4-Lysozyme the Wrong Binding Sign: What Cleveland Clinic, RIKEN and IBM Changed to Correct It
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Evidence-based insights for quantum medicine.
On 9 September 2026 Cleveland Clinic announced that a team from Cleveland Clinic, the RIKEN Center for Computational Science and IBM had reached the finals of the 2026 ACM Gordon Bell Prize, for quantum-classical electronic-structure calculations on protein-ligand complexes of 11,608 and 12,635 atoms. Four days earlier the preprint behind that entry gained a second version, and buried in it is a result that deserves more attention than the atom count.
When the team computed the binding energy of a well-studied protein-ligand pair in a minimal basis set, the answer came out too positive, which is to say it pointed the wrong way for a ligand known to bind. Recomputing it with richer orbitals in the binding region and a tighter bath threshold together corrects the sign of the binding energy, at about ten times more in total node-hours. The revision also reports a first end-to-end wall-clock time, 62.4 hours from fragment generation to energy reconstruction, for a bound-complex ground-state run.