Dong-A University Opens Its G-LAMP Atomic and Quantum Research Center in Busan on 20 October 2026: KRW 25 Billion for Quantum Computing, Medical Image Analysis and Radioisotope Drugs
What Dong-A University opened in Busan on 20 October 2026
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Evidence-based insights for quantum medicine.
Dong-A University in Busan opened the offices of its new G-LAMP research group on 20 October 2026, with about 100 guests on its Seunghak Campus. The group has roughly KRW 25 billion in national funding to build a research platform on atomic and quantum technology that reaches from basic science into diagnosis and treatment.
The university announced the opening on 21 October, and Korean outlets including Money Today reported it the same day. G-LAMP is a basic science research hub program run by Korea's Ministry of Education and the National Research Foundation of Korea. Dong-A was selected as a new group in 2025, and the October ceremony marks the start of its full research activity. President Lee Hae-woo, project director Jeong Jin-woong, Busan's deputy mayor for administration Kim Kyung-deok and Heo Jeong-eun of the National Research Foundation were among those present.
The research plan, as the university describes it, has a clear medical end. Its listed priorities include quantum computing and information processing applied to medical image analysis, the synthesis of new quantum materials, research using radioisotopes, and the development of diagnostic and therapeutic substances with clinical application. Jeong said the group would build an integrated platform covering everything from basic technology to clinical application, and Lee spoke of combining atomic and quantum science with advanced medical technology to open a new field.
For a reader in a hospital or a pharmaceutical company, the opening is an early signal. It funds people, laboratories and a direction of work in Korea's southeast. The reports of the opening cite no study, no device, no measured outcome and no timetable for clinical work. What it does show is where a Korean university chose to point about KRW 25 billion of public money for quantum research: toward imaging, radiopharmaceuticals and the clinic.
The reports also leave some details uneven. The venue is given as the arts and sports college hall in some accounts and as the natural sciences college lobby in others, and the length of the funding period is reported in two ways. The substance of the plan is the same in every account. The differences matter for anyone who cites the program in a funding comparison.
Basque Health Department Funds an Alzheimer's Drug-Target Search on IBM's Quantum System Two in San Sebastián, With Brain-Organoid Validation Planned at BioBizkaia
A first-year report on the Basque quantum computer, 18 October 2026
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Evidence-based insights for quantum medicine.
Alzheimer's disease involves thousands of genes and biological processes acting on one another, and a drug developer has to pick one place to intervene. In the Basque Country, a small team of brain-imaging researchers is now trying to make that choice with help from a quantum computer in San Sebastián.
The machine is the IBM Quantum System Two that IBM and the Basque Government inaugurated on 14 October 2025 at the Ikerbasque building in Donostia-San Sebastián. On its first anniversary, the regional newspaper elDiario.es reported on 18 October 2026 that the system had supported 64 scientific projects, with 444 researchers holding access and average use at about half its capacity. Ten international publications have drawn on it so far.
One of those projects belongs to medicine. The Basque Department of Health is backing a study called Quantum-Enhanced Drug Target Discovery for Alzheimer's Disease, or ADQ, run by the Computational Neuroimaging Group of the BioBizkaia Health Research Institute near Bilbao. According to elDiario.es, the project has €70,000 for 2026 and 2027. It combines reinforcement-learning models with hybrid quantum-classical algorithms to analyze gene-expression networks, and its stated goal is a short list of three to five possible therapeutic targets against the disease.
That is a modest budget and a precise aim. A hospital research institute is asking whether a quantum processor can help sort a large biological network into a handful of candidates worth testing, and then checking those candidates in brain organoids grown from patients' own cells.
The project matters to clinicians for a plain reason. Figures cited by the regional broadcaster Onda Vasca put the number of people living with Alzheimer's in the Basque Country at 43,300, and few new Alzheimer's medicines have reached patients in recent decades. Any method that narrows the list before expensive laboratory work begins is worth watching, even at this early stage.
GSK's Anthony Rowe Puts Useful Quantum Chemistry at About 2,000 Logical Qubits Around 2030; Cleveland Clinic's Lara Jehi Tells Hospitals to Prepare Now
A pharma threshold and a hospital demonstration, reported 19 October 2026
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Evidence-based insights for quantum medicine.
Anthony Rowe, who runs research data platforms, scientific computing and AI infrastructure at GSK, gave a drug company's answer to the oldest question in quantum computing: how big must the machine be before it helps? Speaking at the City Quantum & AI Summit earlier in October, he put the tentative threshold at about 2,000 logical qubits for chemistry problems he judges beyond conventional computers, with 2030 as the horizon on current hardware roadmaps.
On the same stage, Lara Jehi, chief research information officer at Cleveland Clinic, described how her hospital, IBM and the quantum software company Algorithmiq modelled a light-activated cancer drug with a hybrid quantum-classical workflow, work that won a $2 million prize from Wellcome Leap in April 2026. Her message to hospitals was to take part now, while the machines are still being designed.
Computer Weekly reported both talks on 19 October 2026. Read together, they give a hospital research office and a pharmaceutical buyer two useful reference points: a working demonstration at today's scale, and an industry estimate of the scale at which the chemistry starts to pay. The distance between the two is the planning problem.
Rowe's figure came from his earlier pilots in quantum chemistry, protein folding and optimisation before he joined GSK. According to Computer Weekly's account of the summit, he called the threshold tentative and acknowledged that better algorithms or hardware could change it. A logical qubit is an error-corrected unit built from many physical qubits, so 2,000 logical qubits implies a machine with a far larger number of physical ones.
The more candid part of his talk concerned people. Rowe had assembled a team to explore quantum applications, and its members asked to work on AI instead. "They all came to me and said, 'We'd rather work on problems in AI right now because we can be more successful.' So fine. I mean, we can pivot," he said, adding that the industry side has to keep generating a return on investment. He also argued that the computational chemists and physicists already working in life sciences have skills that would transfer to applied quantum computing as the hardware matures.
Quantum X Labs and SciSparc, 6 and 7 October 2026: CliniQuantum's Search of 7.3 Billion Gene Triplets From 11 Patients Ran on IBM's Qiskit Aer Simulator and One NVIDIA GPU
A quantum-simulated gene search, 11 patients and one GPU
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Evidence-based insights for quantum medicine.
Precision oncology and many other parts of personalized medicine depend on finding small groups of genes that move together in a patient's tumor or blood. When a laboratory measures thousands of genes in only a handful of patients, the number of possible gene groups grows into the billions, and computing time becomes a real constraint on the analysis.
Two Nasdaq-listed companies announced the same benchmark of that kind of search this week. On 6 October 2026 Quantum X Labs said that CliniQuantum, which it describes as its subsidiary, had analyzed a clinical gene-expression dataset of 11 patients and 3,531 genes, testing every possible combination of three genes for correlated behavior. On 7 October SciSparc published nearly the same text, describing CliniQuantum as the majority-owned subsidiary of NeuroThera Labs, which SciSparc in turn controls. Both releases call the work quantum-enabled. The computation itself ran as a quantum simulation on conventional hardware: IBM's open-source Qiskit Aer simulator, accelerated by a single NVIDIA graphics card on a rented Amazon Web Services machine.
For a hospital genomics lead, a clinical research director or an investor reading the headlines, the useful work is to separate three things the releases put side by side: what was computed, how fast it was computed, and what the result means for patients. The releases answer the first two with specific numbers. On the third they are candid: the benchmark, in their own words, did not evaluate or establish the clinical validity, predictive value or potential utility of any gene combination it found.
The Quantum X Labs release of 6 October, distributed by GlobeNewswire, reports an approximately 16.8-fold computational acceleration. The comparison is between one machine using only its 64 virtual processor cores and the same machine using a single NVIDIA T4 graphics processor. The search took about 8.4 hours on the processors alone and about half an hour with the graphics processor, and both runs returned the same set of qualifying gene triplets. Dr. Tidhar Turgeman, quoted in the release as head of clinical trials data analysis, called the result an important development milestone.
Researchers in Taiwan, Korea and at Brookhaven Test a Quantum Attention Model on Merck's Alzheimer's Drug Verubecestat: arXiv 2610.04588, 3 October 2026
A quantum attention model tested on an Alzheimer's drug series
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Evidence-based insights for quantum medicine.
Merck stopped its large Alzheimer's trial of verubecestat early, a pill designed to block the enzyme BACE1, which helps produce the amyloid found in the brains of patients. The drug had been carefully refined molecule by molecule, yet in a trial involving 1,958 participants with mild-to-moderate disease it did not reduce cognitive or functional decline compared with placebo.
That chemistry now has a second life as a test set. On 3 October 2026 five researchers from National Yang Ming Chiao Tung University, KAIST, Taiwan's National Center for High-Performance Computing, Brookhaven National Laboratory and Taipei Medical University posted a study of quantum attention for drug discovery. They used the compound series that led to verubecestat as a case study, asking whether a model with a small quantum circuit inside it orders those molecules by potency the way the laboratory did. The same models were also scored on standard benchmarks, including whether a molecule can cross the blood-brain barrier.
The paper, "Variational Quantum Attention for Molecular Graph Learning," by Yu-Cheng Lin, Yu-Chao Hsu, Tai-Yue Li, Nan-Yow Chen and Samuel Yen-Chi Chen, is a preprint on arXiv (2610.04588) and has not yet been peer reviewed. For a pharmacologist, a hospital formulary lead or a research director weighing quantum computing claims in drug discovery, its value lies in how plainly it reports what changed and what did not.
Software that predicts molecular properties is already a routine filter in early drug discovery. Chemists draw large numbers of candidate structures, and models rank them for solubility, brain penetration or binding strength before anyone spends money on synthesis and testing. A poor ranking sends chemists after the wrong molecules. The question this paper asks is narrow and useful: what happens to such a model when one small part of it is replaced by a quantum circuit?
University of Michigan Quantum Hamiltonian Model Infers a 14-Gene Regulatory Network From 127,000 Glioblastoma Cells: The arXiv 2602.19496 Revision of 1 October 2026
A quantum model of glioblastoma gene regulation
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Evidence-based insights for quantum medicine.
Glioblastoma is the most common primary malignant brain tumor in adults, and one reason it resists treatment is that its cells keep changing what they are. A single tumor holds cells that resemble astrocytes, neural progenitors, oligodendrocyte precursors and mesenchymal cells, and they can move between those states.
Four researchers at the University of Michigan have now tried to model that movement with the mathematics of quantum mechanics. In a paper first posted to arXiv on 23 February 2026 and revised on 1 October 2026, Mohammad Aamir Sohail, Ranga R. Sudharshan, S. Sandeep Pradhan and Arvind Rao describe a quantum Hamiltonian-based gene expression model, which they call QHGM, and an algorithm that learns its parameters from single-cell RNA sequencing data. They apply it to about 127,000 cells from glioblastoma patients and report a network of activating and repressing links among 14 genes.
For oncologists and genomics groups, the paper is worth reading for what it attempts and for how far it still is from the clinic. The model runs as a simulation on conventional graphics processors, its biological results have been compared with published literature and not yet with laboratory experiments, and its strongest benchmark numbers come from synthetic data that the model itself generated.
A gene regulatory network describes which genes switch other genes on or off. Single-cell RNA sequencing measures how strongly thousands of genes are expressed in each of many thousands of cells, and a family of computational methods tries to read the network back out of those measurements. The authors compare their approach with four established classical methods, ARACNE, GeneNet, GENIE3 and SINCERITIES.
NEC and Taiho Join NEDO Quantum Cancer Research Program: The 2 October 2026 Announcement and ABCI-Q
NEC and Taiho enter NEDO quantum cancer research program
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Evidence-based insights for quantum medicine.
A cancer research partnership becomes easier to assess when its sponsors explain who will do the work and when the results should arrive. Readers following quantum medicine can then judge each later announcement against the original commitment.
NEC's 2 October 2026 announcement describes a NEDO-selected partnership applying quantum computing and AI to cancer immunotherapy research. Taiho participates in the project, which plans to use ABCI-Q. Our assessment focuses on how future reports can connect that research ambition to evidence that outside institutions can judge. The strongest eventual account would make the reasoning behind each claimed improvement accessible to readers beyond the partnership.
The Monitor's assessment is that the useful news here is the opportunity to follow a research commitment from its beginning. A partnership announcement supplies a starting point for questions about responsibility and delivery. It gives a reader a reason to look for subsequent publications, but leaves the reader to distinguish a promised capability from a measured outcome. That distinction matters for any organization deciding how much attention, staff time or research funding to devote to a new computing approach. A successful collaboration could produce useful knowledge even if some of its original expectations need revision.
Our reading also separates the interests of different audiences. A research funder may reasonably want to know whether a project creates reusable methods and publishes enough information for independent assessment. A pharmaceutical research manager may ask whether the collaboration makes a practical research decision easier or cheaper. A clinician will eventually need evidence that any resulting intervention helps patients. These questions can be connected over time, while retaining their own standards of proof. Treating them separately makes an early announcement easier to understand and gives later results a fairer hearing. It also leaves room for informative negative findings, which can save other teams from spending years on an approach that does not work as expected.
Chalmers, Lund and Jülich Designed Lattice-Protein Sequences on IBM's Torino Quantum Processor: What Physical Review Applied Published on 28 September 2026
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Evidence-based insights for quantum medicine.
Most drug-discovery stories about quantum computers ask a folding question: given a chain of amino acids, which shape will it settle into? Protein designers work the other way round. They start from a shape they want, a pocket that grips a drug or a surface that binds a virus, and search for a sequence of building blocks that will fold into it. A team from Chalmers University of Technology in Gothenburg, Lund University and the Jülich Supercomputing Center has now measured how far one of today's quantum processors gets on the first half of that design problem.
Their paper, "Designing lattice proteins with variational quantum algorithms", appeared in Physical Review Applied (volume 26, article 034065) on 28 September 2026, a year after a first preprint on arXiv in August 2025. Hanna Linn, Lucas Knuthson, Anders Irbäck, Sandipan Mohanty, Laura García-Álvarez and Göran Johansson compare two families of variational quantum algorithms on a task they call protein sequence optimization, and they run the more robust family on IBM's Torino quantum processor, a device built on IBM's Heron r1 chip. With circuit settings carried over from noiseless computer simulations, the hardware found the correct sequence in more than 10 percent of its attempts for 17 of the 22 test problems, including the three largest, chains of 27 to 29 units.
The test proteins are simplified models drawn on a flat grid, and the authors say so plainly. The paper is still a careful read for anyone who funds or buys computational chemistry. In one controlled setting, with every right answer known in advance, it shows where current quantum hardware helps on a design task, where it breaks down, and which engineering choices made the difference.
The test bed is the HP model, one of the oldest simplifications in protein physics. Each protein is a chain of beads on a two-dimensional square grid, and every bead is one of only two kinds. H beads are hydrophobic, the water-avoiding kind of amino acid that real proteins tend to bury in their core. P beads are polar. The energy of a folded chain drops by one unit for every pair of H beads that touch on the grid without being direct neighbors along the chain, so the model rewards a compact hydrophobic core. Real proteins have twenty kinds of amino acid, side chains and three dimensions, so the HP model is a laboratory for methods. Its great advantage is that for chains of up to 30 beads, exhaustive classical enumeration has already listed the exact answers, which lets researchers grade a new algorithm without guessing.
Quantum4Health Tests Quantum Computing on Galicia's 061 Emergency Service and Genomic Variant Calling: What Fujitsu España and Red.es Reported on 29 September 2026
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Evidence-based insights for quantum medicine.
When someone in rural Galicia calls the regional emergency number, how fast the call is answered depends on how many call handlers are on shift, and how fast help arrives depends on where the nearest ambulance or helicopter is waiting. Both are planning problems that a health service solves every week with limited staff and limited vehicles. A Spanish public research project now tests whether quantum and quantum-inspired computers can help plan them, and whether other quantum computing approaches can help read DNA.
The project is Quantum4Health, funded by Red.es, the public digital-transformation agency under Spain's ministry for digital transformation. Its progress was described in a project statement published by the Spanish outlet Demócrata on 29 September 2026 and distributed the same day through Europa Press. Fujitsu España and Fsas Technologies, both part of the Fujitsu Group, lead the work. Two technical tracks run side by side: optimization on the Fujitsu Digital Annealer, a quantum-inspired machine that runs on conventional digital hardware, and quantum machine learning for variant calling, the step in genome analysis that finds mutations in a patient's DNA.
The statement describes work in progress. According to the partners, the project has so far defined its use cases, built first models and prototypes, and compared different technical approaches on real problems from the health sector. It reports no accuracy figures, no response-time savings and no patient data results. That makes it a useful view of where European health services are pointing quantum computing today, and a thin basis for any claim about benefit.
The consortium mixes a large vendor, research centers and one real health-service user. Red.es funds and drives the initiative. DigitalES, the Spanish association of digital technology companies, acts as the lead industry partner. The technology center Eurecat, the Galician Supercomputing Center (CESGA) in Santiago de Compostela and the Barcelona quantum company Qilimanjaro bring advanced computing and quantum expertise. The end user is the Public Foundation for Health Emergencies of Galicia, known as 061, which runs emergency medical calls and dispatch for the region.
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.
QTomo Says Quantum Computing Can Rebuild CT Scans From 10 Percent of the X-Ray Data: What Kyungtaek Jun's Papers From 2023 to 2026 Tested
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Evidence-based insights for quantum medicine.
A Korean start-up called QTomo says its quantum algorithms can rebuild a CT scan from one tenth of the usual X-ray projections, which would cut a patient's radiation dose by 90 percent. The company's founder, Kyungtaek Jun, made those claims in a newspaper interview published on 24 September 2026, and his own peer-reviewed papers show how far the method has actually been tested.
Jun has published the core of QTomo's approach in Scientific Reports in 2023 and 2025, in two arXiv preprints in April and May 2025 and in EPJ Quantum Technology in July 2026. The experiments in those papers perform quantum CT reconstruction on images of at most 100 by 100 pixels, solve the problem on the D-Wave hybrid solver, which combines a quantum annealer with classical computing, and use test objects such as the Shepp-Logan phantom, a standard computer-drawn head outline, a model tooth or a single existing chest CT slice. The 90 percent figure appears as an estimate in the April 2025 preprint, and none of the papers reports a scan acquired from a patient for the study, a dose measurement or a clinical validation.
That gap matters for radiology departments and hospital buyers, because the interview lists early cancer detection, breast and heart imaging and dental work among the technology's proposed clinical applications. The published papers describe a careful and interesting research program at a much earlier stage.
Set side by side, the interview's figures and the papers' measurements separate into three groups: claims with peer-reviewed support on small test images, one estimate from a preprint, and figures that appear in no paper at all. The first group shows steady progress by a small research team. The second would need a scanner study to confirm it, and the third would need data that QTomo has not yet made public.
IonQ Will Supply SDT a Superion 256 Quantum Computer, Announced 21 September 2026, and a Korean Report Places It in a Cancer Center's Hybrid Data Center, With Deliveries From 2027
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Evidence-based insights for quantum medicine.
On 21 September 2026 the American quantum company IonQ and the Korean system integrator SDT announced a multi-year partnership under which IonQ will supply SDT with its newest quantum computer and a quantum memory module. The Korean newspaper Digital Times reported on 22 September, citing SDT, that the computer is meant for the hybrid data center of a cancer center in South Korea. For a hospital that buys imaging systems, sequencers and compute clusters on multi-year cycles, the announcement raises a practical question about what this machine can do for oncology.
The answer depends on dates and on the precise wording of two press accounts. IonQ will supply a Superion 256, a trapped-ion machine the company announced on 8 September 2026 and says it will begin delivering to customers in 2027. The cancer center has not been identified, and SDT says the project details will be disclosed later. What exists today is a purchase agreement, a plan for a hybrid quantum-classical data center, and a planned manufacturing site in the city of Gumi. The cited announcements report no medical workload or results.
That makes this a procurement story at an early stage, and procurement stories are where hospital leaders, research directors and health ministries decide whether to wait, pilot or budget. This Monitor sets out what each party said, what the machine is, and which questions a cancer center would need answered before the system becomes clinical research infrastructure.
The Korean detail matters because the English release and the Korean report differ in one respect that a buyer should notice. The difference concerns where the computer goes.
Sagutxoa Grows Neurons From Donated Milk Teeth in Alicante and Plans a Quantum Analysis on IBM's Quantum System Two in San Sebastián (El País, 18 September 2026)
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Evidence-based insights for quantum medicine.
On 18 September 2026 El País reported from the Basque Country on Sagutxoa, a research project that grows human neurons from the milk teeth of children with rare neurological diseases and intends to send the measurements taken on those neurons to a quantum computer. The neuroscience half of the project has produced something concrete: a public biobank of more than 50 frozen neuronal lines. The quantum half is a plan with access to the machine through a public regional program; the reports describe no quantum hardware result.
The project is led by Salvador Martínez, professor at the Universidad Miguel Hernández and researcher at the Instituto de Neurociencias in Alicante, and is financed by the Bilbao foundation The Walk On Project with an initial budget of 120,000 euros. Its raw material is milk teeth donated by children with rare neurodegenerative diseases; the dental pulp contains neural crest progenitors that become neurons without genetic modification. The computing partner is LKS Next of the Mondragón group, which expects to use the IBM Quantum System Two installed in San Sebastián through the Basque Government's Basque Quantum infrastructure.
For a hospital neurologist or a rare-disease foundation the interesting part is the biology, which is further along than the headline suggests, and the honest part is the timeline: the foundation's president says the phase now under way should reach its first conclusions in about a year, and warns against raising false expectations.
Martínez told El País that the first task was to establish whether the pulp of a shed milk tooth contains neural crest progenitors, and that the answer was yes. The stem cells in the pulp turn into neurons naturally, without any genetic reprogramming, which is the point of difference from induced pluripotent stem cell models that rewrite a skin or blood cell's program first. In culture, he says, the neurons behave as neurons do: they form circuits, connect to one another and develop synapses, which produces very large volumes of electrophysiological, proteomic and transcriptomic data.
China's Pharmaceutical Industry 15th Five-Year Plan, Reported 18 September 2026, Lists Quantum Computing for Drug R&D; the 2021 Plan Did Not
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Evidence-based insights for quantum medicine.
On 18 September 2026 Chinese business media reported that the Ministry of Industry and Information Technology and nine other central departments had issued a five-year plan for the pharmaceutical industry that lists quantum computing among the technologies the state wants applied to drug research. The predecessor plan of December 2021 listed artificial intelligence, cloud computing and big data for the same job, and the word quantum did not appear in its published text.
The underlying document, which this Monitor knows through the reports of it rather than from the notice itself, is the 15th Five-Year Plan for the pharmaceutical industry, filed as MIIT joint planning document 2026 number 210 and covering 2026 to 2030. In its passage on frontier technologies, as quoted by the business press, it asks industry, universities, research institutes and hospitals to speed up the use of artificial intelligence, quantum computing, supercomputing and computational medicine in drug research and development. The same plan sets a target that Chinese first-in-class drugs make up at least 25 percent of the global total by 2030.
A planning document is a statement of intent, not a result. For a reader who buys for a hospital or runs a pharmaceutical research group in Europe, its value lies in what it tells you about where Chinese public money, procurement and regulatory attention will go for the rest of the decade, and in the gap between the plan's language and the evidence a physicist in Shanghai asked the same industry to produce a week earlier.
The ten signatories are the Ministry of Industry and Information Technology, the National Development and Reform Commission, the Ministry of Natural Resources, the Ministry of Agriculture and Rural Affairs, the Ministry of Commerce, the National Health Commission, the Ministry of Emergency Management, the National Healthcare Security Administration, the National Administration of Traditional Chinese Medicine and the National Medical Products Administration.
IonQ and Kipu Quantum's 64-Qubit Protein-Folding Paper Took Third Place at IEEE Quantum Week 2026: Six Peptides, 46 to 61 Qubits, Four of Six Reference Energies Reached After Classical Post-Processing
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Evidence-based insights for quantum medicine.
A Kipu Quantum and IonQ paper on lattice protein folding took third place in the hybrid case-studies track at IEEE Quantum Week 2026 in Toronto, IonQ announced on 15 September 2026, and the release says the work "was used to improve computational speed and accuracy in drug discovery." The paper, on arXiv since 29 April 2026, reports something narrower: a hardware benchmark on six short peptides that reached the reference energy of a conventional solver in four cases, and only with the help of a classical consensus step.
The experiment ran six peptides of 14 to 16 residues through a 64-qubit trapped-ion processor, a barium development system IonQ describes as similar to its forthcoming Tempo line, folding each on a coarse lattice with no side chains using 46 to 61 qubits. The quantum circuits produced samples that mostly violated the backbone geometry, and it took a classical post-processing step, a consensus vote over the two thousand lowest-energy samples combined with a pool of feasible backbones, to reach the reference energy in four of the six cases; a random-start version of the same pipeline reached it in one. For a hospital buyer or a clinical pharmacologist that is the whole result: a hardware run on a benchmark, at a scale that a classical genetic algorithm solves to convergence, with the quantum contribution visible in the statistics of one part of the problem.
The IonQ release counts 857 submissions and 27 Best Paper honors at the conference, four of which went to papers with IonQ authors. Three of the four concern AI fine-tuning, distributed optimization with Oak Ridge National Laboratory and NVIDIA, and linear algebra with Synopsys. The fourth is the protein-folding paper with Kipu Quantum, which the release assigns to drug discovery. The conference's award list gives the paper third place in the Quantum End-to-End Hybrid Case Studies track, with the paper presented on Monday 14 September and the track's awards presented on Tuesday 15 September. Five of the fourteen authors are at Kipu Quantum in Berlin, one, Sebastián Romero, at ICMM-CSIC and the Autonomous University of Madrid, and eight at IonQ, among them Martin Roetteler, IonQ's vice president of quantum applications research, who is quoted in the release. The award recognizes a conference paper; it does not evaluate a drug, a target or a clinical claim, and the paper makes none.
QC Ware and IonQ Mapped Four Orbitals of a Cytochrome P450nor Active Site Onto Eight Qubits of IonQ Forte on 1 September 2026
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Evidence-based insights for quantum medicine.
How tightly a candidate binds its target is one important factor in whether a drug works, and metal centers are among the difficult cases to calculate: an iron atom sitting in the middle of an enzyme. On 1 September 2026 QC Ware and IonQ said they had measured a four-orbital slice of one such site on a quantum computer and landed close to the classical answer. QC Ware's Promethium platform did the classical preparation, IonQ's Forte trapped-ion processor took the quantum measurement through Amazon Braket, and the target was the heme active site of cytochrome P450nor. The workflow calculated an electrostatic interaction energy within 0.5 kcal/mol of classical benchmarks, roughly four percent, inside the one kcal/mol threshold conventionally called chemical accuracy.
The IonQ half of a workflow already run on IBM hardware
This is a follow-up. On 7 August 2026 this Monitor read the same QC Ware Promethium workflow running a nitric oxide reductase calculation on IBM's 156-qubit Heron processor. The platform, the enzyme family and the hybrid division of labor are unchanged; the processor underneath is what is new, which makes hardware portability the company's actual claim. Portability is not comparability: the August disclosure carried no numerical result that today's figure could be set against. Today's release adds four disclosures the August account did not carry: the 115-atom active-site model, its more than 1,000 molecular orbitals, the reduction to a four-orbital active space mapped onto eight qubits, and the 0.5 kcal/mol comparison itself.
What the quantum machine actually did
The quantum step ran on eight qubits of a 36-qubit machine, measuring a four-orbital slice carved out of a model containing more than a thousand molecular orbitals. Promethium built the model, identified the strongly correlated region, reduced it automatically to that active space, and computed the final interaction energies classically after Forte measured the qubits in a single basis. Everything before and after the quantum slice happened on classical hardware.
Which enzyme, and why the framing needs care
P450nor sits in the cytochrome P450 superfamily whose monooxygenase members carry out most human drug metabolism, and the release says exactly that. P450nor itself is something else: a fungal CYP55 nitric oxide reductase, characterized in Fusarium oxysporum among other fungi, reducing nitric oxide to nitrous oxide and taking its electrons straight from reduced nicotinamide adenine dinucleotide, NADH. The release names no organism and no accession. It is a well-characterized computational test case, which is a good reason to choose it for a hardware demonstration and a poor reason to describe the run as modeling human drug metabolism.
What the result can and cannot establish
The comparison target was a classical benchmark, so the classical answer existed first, and the companies report agreement for this known case. The announcement names no preprint, reports no shot count, gives no uncertainty on the 0.5 kcal/mol figure and does not identify which classical method produced the benchmark.
Tecnun's Synthetic Myelodysplastic-Syndrome Patients Were Seven-Bit Samples From IBM's ibm_basquecountry
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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.
Florida Atlantic's 90.26% Quantum Heart-Disease Result Came From a Simulator: the AI Paper of 21 May 2026 and the FAU Release of 27 August
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Evidence-based insights for quantum medicine.
Florida Atlantic University announced on 27 August 2026 that its engineers had built a quantum machine learning framework for heart disease prediction reaching more than 90 percent accuracy. The figure is precise and it is checkable. It is 90.26 percent, produced by a quantum support vector machine with angle encoding, averaged across five folds of a clinical file holding 918 patients.
The work behind that announcement appeared three months earlier. Muhammad Minoar Hossain, Md. Hasibul Hassan Himal and Arslan Munir published "A Comparative Study of Quantum Feature Maps and Quantum Classifiers for Heart Disease Prediction" in the MDPI journal AI on 21 May 2026, as article 180 of volume 7, under a Creative Commons license that lets anyone read the whole methods section. Section 2.6.2 of that paper states that every quantum experiment ran in a simulation-based environment rather than on a physical quantum processing unit. The university announcement does not carry that sentence, and neither does the coverage that followed it.
That difference decides what the study is evidence for. Simulated qubits establish whether an algorithm has promise in principle; a run on a physical processor establishes whether the machines that exist can deliver it, once noise, limited connectivity and readout error have had their say. Everything else in the paper holds up well under checking. Several of its numbers are more informative than the ones the announcement chose to lead with, and one of them disagrees with the paper's own abstract. The study is a careful piece of comparative work whose careful parts were the first thing lost in transmission.
A*STAR Put an 18-Variable Drug-Docking Problem on Six Qubits of IBM's ibm_kingston
Six qubits for an eighteen-variable docking problem
On August 20, 2026 six researchers at institutes of Singapore's Agency for Science, Technology and Research, the National University of Singapore and Nanyang Technological University posted a preprint describing a hybrid quantum-classical method for molecular docking, and executed its circuits on IBM's ibm_kingston processor. Molecular docking is the everyday question of early drug research: given a candidate molecule and a target protein, how does the molecule seat itself in the pocket, and how well. The team recast that question as a graph problem, where each plausible contact between ligand and protein is a weighted vertex and the winning binding pose is the heaviest set of contacts that can all coexist.
An encoding that carries three variables on one qubit
The contribution is an encoding the authors call full-basis encoding, which uses all three orthogonal directions of a qubit's Bloch sphere to carry information instead of one. That put an 18-variable problem on six qubits for the streptavidin-biotin complex 1STP, and a 14-variable problem on five qubits for trypsin with benzamidine, entry 9AW2. The paper also proves that a global minimizer of its objective can always be chosen to be a pure product state, so the optimum requires no entanglement between qubits and the circuits can stay shallow enough for current hardware. What changed for a reader tracking this field is the qubit cost of a docking problem, since the number of good qubits a problem consumes is the binding constraint on machines available today.
What the hardware runs did and did not settle
On both instances the runs on ibm_kingston recovered the same vertex selections as the classical simulation and matched the known clique structure under realistic gate noise and readout error. The authors state plainly that this is evidence of feasibility and that quantum advantage is not demonstrated, since both test problems were deliberately kept small enough for classical verification. Streptavidin with biotin and benzamidine in the trypsin pocket are textbook complexes, though their records differ in age: entry 1STP has been public since 1992, while entry 9AW2 is a 2025 redetermination of a complex first characterized in the mid-1970s. Either way the correct answer was known before the circuits ran, which is what made the check against ground truth possible and also what limits the claim. The open question the authors name themselves is whether the compression ratio survives realistic docking graphs with hundreds of vertices, alongside better ansatz design and hardware-aware circuit compilation.
Atrial Fibrillation Is Where Seoul Will Look for Quantum Advantage
A grant to find out whether quantum computing helps
On August 25, 2026 a Korean consortium was given two and a half years and 2.5 billion won to find out whether a quantum computer can compute cardiovascular blood flow on a schedule a clinic could live with. Seoul St. Mary's Hospital of the Catholic University of Korea, the University of Seoul and the medical software company Flownics were selected for a new 2026 challenge program run by Korea's Ministry of Science and ICT with the National Research Foundation. What the grant buys is a measurement rather than a product: the team has been funded to establish the conditions for quantum gain on one clinical calculation and to report those conditions as numbers, including how many circuits and how many measurements the answer cost.
What a hospital can already see, and what it cannot
A cardiologist reading a CT scan of a narrowed artery can measure the narrowing and cannot measure the flow. Velocity, pressure and wall shear stress are quantities that inform the risk of a clot forming or a muscle being starved, and none of them is in the picture. Computational fluid dynamics recovers them from the anatomy, and cardiology has begun buying it: pressure ratios computed from coronary CT are an established adjunct in stable chest pain assessment and have cleared a national payer's technology assessment in England. The trouble is what happens when a clinician asks for more. A finer mesh, honest nonlinear terms and patient-specific boundary conditions each multiply the arithmetic, and past a certain point the answer arrives too late to be part of a decision.
Atrial fibrillation first, adjudicated by imaging
The first target is atrial fibrillation, the most common sustained arrhythmia, present in roughly two to three percent of the population. In a fibrillating heart the left atrium stops emptying cleanly, blood stagnates in its appendage, and stagnation is where thrombus begins. Whatever the solver computes will be checked against real patients imaged with 4D Flow MRI, which records the speed and direction of the blood itself alongside the geometry of the vessels carrying it. The plan starts on classical hardware with a three-dimensional deep learning model that segments the heart, the left atrium and the aorta from CT, and a quantum hemodynamic model is built on top of that, stacking a variational algorithm, a Krylov subspace method, classical shadow measurement and non-Markovian error mitigation.
Ninety-five percent is a parity target
The headline figure deserves a careful reading. The stated aim is for quantum-based computational fluid dynamics to reach at least ninety-five percent of the precision of the classical method, which is a target to match the incumbent rather than to beat it. Any advantage the project finds will have to appear somewhere other than accuracy: in time to answer, in cost, or in problem sizes the classical solver cannot reach inside a clinical window. Stating it that way is unusually disciplined. The project fixes the accuracy at parity, names the baseline, names the disease, names the imaging modality that will adjudicate, and puts the burden of proof on resources. A hospital procurement officer has something to hold the team to in 2028, and a negative result in that year will be as informative as a positive one.