Daiichi Sankyo Uses QSimulate's QUELO Quantum Chemistry Software in Several Drug Discovery Programs to Predict Binding Affinity: What the June 2026 AWS Account Shows
How Daiichi Sankyo uses QSimulate's quantum chemistry to predict drug binding
Quentir Medicine Monitor
Evidence-based insights for quantum medicine.
Daiichi Sankyo is using software from the Boston start-up QSimulate to bring quantum chemistry into one of the most routine and most expensive judgments in drug discovery: how tightly a candidate molecule will bind to its protein target. The main public account is a Japanese-language blog on the Amazon Web Services site, published on 11 June 2026.
That blog was written jointly by scientists from Daiichi Sankyo's Smart Research Institute II, by QSimulate, and by an AWS solutions architect, and it describes how the company evaluated and then adopted QSimulate's QM-FEP method, a version of free energy perturbation that uses quantum mechanics for the energy of the interaction between drug and protein.
Predicting binding affinity well matters because it decides which molecules a medicinal chemistry team actually makes. Each round of the design, make, test and analyze cycle costs weeks of synthesis and assay work. A computer model that ranks candidates more reliably lets chemists spend that time on the compounds most likely to work, and lets them drop weak ideas before anyone orders reagents.
No quantum computer is involved here. QSimulate's software runs quantum mechanical calculations on ordinary graphics processors in the cloud. The interest for medicine lies in a large pharmaceutical company putting physics-level chemistry into its everyday design work today, years before quantum hardware is expected to take on the same problem.
The Daiichi Sankyo authors explain the problem in practical terms. Their teams already use structure-based drug design and conventional free energy perturbation, which estimates binding by simulating the gradual transformation of one molecule into another inside the protein. That method depends on a classical force field, a fixed set of rules for how atoms push and pull on each other. The authors write that more accurate energy functions are needed, especially for the chemical modalities they expect to grow more complex, where conventional FEP has struggled with non-classical interactions and with covalent drugs that form a chemical bond with their target.
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.
Kipu Quantum Ran a Molecular Toxicity Classifier on QuEra's 256-Qubit Aquila: What 200 Neutral Atoms Added for 122 Drug-Candidate Molecules
Kipu Quantum tests neutral-atom features for drug toxicity prediction
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Evidence-based insights for quantum medicine.
The German quantum software company Kipu Quantum has reported a molecular toxicity experiment run on QuEra Computing's Aquila, a neutral-atom quantum processor with up to 256 atoms. Each of 122 molecules from a public toxicity dataset was encoded into the controlled quantum-state evolution of 200 atoms, and the measured patterns were turned into about a thousand new numerical features per molecule for an ordinary machine learning classifier.
On those 122 molecules, drawn from a dataset of 171 laboratory-tested compounds, the quantum features raised the balanced accuracy of the toxic or non-toxic call from about 0.55 to about 0.70, according to the authors. For anyone who screens drug candidates for safety, the result is an early test of whether a quantum device can make a classical prediction model better at spotting the harmful minority of compounds.
The work is described in a technical blog post on the company's site, Reading Toxicity of Molecules with Neutral-Atom Quantum Processors, by Antonio Ferrer Sánchez, Carlos Flores and Alejandro Gómez Cadavid, carrying a publication date of 10 July 2026. Quantum Computing Report brought it to wider attention on 17 October 2026. The post is not a peer-reviewed paper, and it neither links nor cites a preprint with the full methods. The figures below are the authors' own, read from the post.
Aquila is an analog machine. It holds single atoms in place with focused laser beams and drives them with smooth light pulses into a highly excited Rydberg state; when one atom is excited, its close neighbors respond, and this interaction shapes how the whole array evolves. QuEra's product page states that Aquila has been publicly available through Amazon Braket since 2022, with a minimum atom spacing of 4 micrometers.
Kipu Quantum's method, which it calls quantum feature extraction, writes the description of each molecule into the settings of that evolution. The team assigned each molecule its own set of driving schedules and atom spacings, laid out 200 atoms on a grid of 14 by 15 positions a few micrometers apart, and let the system evolve for a little under four microseconds before reading out every atom. The authors write that they reached the machine through their own Kipu Quantum Hub platform.
They submitted the training set one molecule at a time, with 500 independent measurements per molecule, and processed 122 molecules in total, roughly two thirds labeled non-toxic and one third toxic. From the strings of excited and unexcited atoms they built about 1,000 features per molecule. A conventional classifier then made the final toxic or non-toxic decision. The quantum processor never classified anything itself; it acted as a transformation step in front of standard software.
Universal Quantum Announced a Series A of More Than $100 Million on 8 October 2026: What Published Cytochrome P450 Resource Estimates Say About Quantum Drug-Metabolism Chemistry
Universal Quantum funds trapped-ion machines with drug discovery in view
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Evidence-based insights for quantum medicine.
On 8 October 2026 the University of Sussex announced that its spin-out Universal Quantum had raised a Series A round of more than $100 million, co-led by Firgun Ventures and DCVC. The university calls it the largest Series A raised by a UK quantum company and the company's first significant institutional financing. Universal Quantum builds trapped-ion quantum computers from linked chip modules, and it lists drug discovery among the uses that larger, error-corrected machines could open up.
For readers in hospitals and pharmaceutical research, the useful question behind the money is how large a machine has to become before it can calculate something a medicinal chemist cannot already calculate on a classical computer. One published benchmark concerns the cytochrome P450 enzymes, which break down most medicines in the liver, and its estimate runs to millions of physical qubits on a generic error-corrected machine.
The university's announcement names further investors, among them the Singapore investor EDBI, Invus, Integral GlobalTech, Main Sequence, Hostplus, NGS Super and Roblox founder Dave Baszucki. Chief executive Sebastian Weidt, who co-founded the company with physicist Winfried Hensinger, said the round reflects investors' belief that Universal Quantum offers "one of the most credible paths to quantum computing at scale." The university states that the company now employs more than 100 people on three continents.
A trapped-ion computer holds single charged atoms in electric fields above a chip and uses each ion as a qubit. Most trapped-ion systems address their ions with many finely aimed laser beams. Universal Quantum follows the design that Hensinger and Weidt published in 2017 as a blueprint for a large machine, in which a global microwave field drives the qubit operations and local voltages select which ions respond. According to reporting by Tech.eu on the round, the company's chip carries hundreds of ion qubits on a die larger than 400 square millimeters and is made on a standard 200-millimeter commercial foundry line.
The harder engineering problem is connecting chips. In a paper in Nature Communications of 8 February 2023, Mamun Akhtar and colleagues in Hensinger's Sussex group moved ions physically from one chip module to the next at a rate of 2,424 transfers per second, with a probability below 7 in 100 million that an ion is lost during a transfer. Separately, the authors found no measurable loss of the qubits' phase coherence from the transport. That link is the basis of the modular, tileable machine the company wants to build. Tech.eu also reports a €67 million contract from the German Aerospace Center (DLR) in 2023 and a hub in Hamburg that the new money will expand, alongside growth in the United States and Japan.
Drug safety depends heavily on metabolism. A Google Research post by Nicholas Rubin and Ryan Babbush of 12 October 2023 notes that enzymes of the P450 family carry out more than 70 percent of all drug metabolism. When two drugs compete for the same P450 enzyme, levels of one can rise into a toxic range; when a patient's genes make an enzyme fast or slow, a standard dose can be too weak or too strong. Predicting how a new molecule binds and reacts at the iron center of these enzymes is therefore a real problem in pharmaceutical development.
INmune Bio Becomes Quantum Computing Inc.'s First Pharma Customer on 14 October 2026: What Dirac-3S Will Search for in the XPro Alzheimer's Trial Data
INmune Bio puts XPro trial data on a quantum optimizer
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Evidence-based insights for quantum medicine.
On 14 October 2026 the Alzheimer's drug developer INmune Bio and the photonics company Quantum Computing Inc. (QCi) announced that INmune Bio will use QCi's Dirac-3S quantum optimization machine to look for new diseases in which its anti-inflammatory candidate drug might work. The engagement is a first stage with options for more, and QCi describes it as its first commercial customer in the pharmaceutical and biotechnology market.
The drug is XPro1595, which INmune Bio calls XPro. It is designed to neutralize the soluble form of tumor necrosis factor, a signaling protein that drives inflammation, while leaving the membrane-bound form, which the immune system and nerve cells rely on, largely untouched. INmune Bio has tested it in early Alzheimer's disease in patients whose blood shows signs of inflammation.
According to the joint announcement issued from Boca Raton and Hoboken, the two companies will combine INmune Bio's data from preclinical work and four clinical trials with public biomedical datasets. The aim is to find new indications, meaning diseases driven by the same inflammatory biology XPro targets, and to rank them by their chance of success. INmune Bio's chief executive David Moss said the search would start with rare diseases, where trials can be smaller and faster, and where a qualifying pediatric approval may earn a priority review voucher from the US Food and Drug Administration.
The announcement contains one commitment that deserves more attention than the headline. The companies say they will compare the quantum machine's output with conventional statistical and machine-learning methods and judge which approach suits which part of the analysis. Whether that comparison is published will decide how much the collaboration teaches anyone outside the two companies.
The largest body of patient data INmune Bio brings to the work comes from MINDFuL, a randomized, double-blind, placebo-controlled phase 2 trial registered as NCT05318976 on ClinicalTrials.gov. The registry lists 207 enrolled participants, a start in February 2022 and completion on 12 May 2025. Patients aged 50 to 85 with mild cognitive impairment or mild dementia and raised inflammatory biomarkers received a weekly injection under the skin of XPro1595 at 1.0 mg/kg or a placebo for 24 weeks, at 35 centers in eight countries.
The results, written up by Judith Jaeger, Christopher Barnum and colleagues in a preprint posted to medRxiv on 27 September 2025, are mixed. In the 200 patients of the modified intent-to-treat population, the drug did not beat placebo on the primary endpoint, a composite of six cognitive tests called the Early and Mild Alzheimer's Cognitive Composite (EMACC), with p = 0.672. The authors write plainly that XPro1595 did not meet the primary endpoint in the overall population.
SpinQ and DIVBIO Add a Five-Qubit Circuit to GLP-1 Receptor Potency Prediction: What the arXiv Study of 9 October 2026 Shows
A five-qubit circuit sharpens GLP-1 receptor potency prediction
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Evidence-based insights for quantum medicine.
Researchers at the Shenzhen quantum computing company SpinQ Technology and the Shenzhen drug developer DIVBIO Pharmaceutical report that a small simulated quantum circuit, bolted onto a conventional neural network, makes slightly better predictions of how strongly candidate molecules switch on the GLP-1 receptor. That receptor is the target of the drug class now used widely for type 2 diabetes and weight management, so even modest gains in early compound triage attract attention.
The preprint, posted to arXiv on 9 October 2026 by Chenyan Jia, Yaping Ma, Yuen Wang and Cong Guo, tests what the authors call a quantum-circuit residual head: a five-qubit circuit that adds a correction on top of an ordinary linear prediction. Its subject is potency prediction, the task of estimating from a molecule's structure the concentration at which it produces half of its maximal effect in a laboratory assay. The study also includes a careful classical control, and that control shapes how the result should be read.
The full paper, its 19 pages of supporting information and the abstract are open on arXiv under identifier 2610.13037. It has not yet been peer reviewed.
The team drew its measurements from release 37 of ChEMBL, the open bioactivity database run by EMBL's European Bioinformatics Institute. They kept only records for the human receptor that measured cAMP, the signalling molecule a cell produces when the GLP-1 receptor is activated, and that reported an exact EC50 value. After removing flagged and duplicate entries and merging repeat measurements of the same structure by their median, the GLP-1 receptor set held 1,052 compounds, of which 472 were small molecules and 580 were peptides under the authors' grouping rule, which counts amide bonds.
Two further receptors served as checks. The beta-2 adrenergic receptor (ADRB2) set held 829 compounds, 711 of them small molecules, and the melanocortin-4 receptor (MC4R) set held 767. Both receptors are established drug targets, the first in asthma and the second in rare genetic forms of obesity.
The authors point out a limitation of public potency data. A single cAMP potency value merges measurements taken in different laboratories under different assay conditions, so part of the error any model shows reflects noise in the labels themselves.
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?
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.
NYU's Tautomer-Predictor Skips Quantum Calculations and Flags 126 PDBbind Ligands: What the September 2026 Chemical Science Paper by Pan, Han, Han and Zhang Shows
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Evidence-based insights for quantum medicine.
A single hydrogen atom can sit in two places on the same drug molecule. When it moves, the pattern of single and double bonds shifts with it, and the molecule presents a different face to the protein it is meant to bind. Chemists call these interchangeable forms tautomers, and choosing the wrong one at the start of a computer-aided drug design project can distort the docking scores and free-energy estimates that follow.
A paper in the Royal Society of Chemistry journal Chemical Science, "Deep learning of tautomer stability from crystallographic proton positions" by Xiaolin Pan, Chao Han, Fengyang Han and Yingkai Zhang of New York University, published online in September 2026, attacks this problem with an unusual source of training data. The team mined hydrogen positions resolved in small-molecule crystal structures from the Cambridge Structural Database, built more than 1.1 million tautomeric states from them, and trained a graph neural network to predict tautomer stability directly from a molecule's two-dimensional structure. The authors state that the model needs no 3D conformers and no quantum-mechanical calculations, which is what makes it fast enough for structure-based drug discovery at library scale.
The quantum-medicine interest lies in what the network replaces. Quantum chemistry has long been the accurate but slow reference for deciding which tautomer a molecule prefers, and it is also one of the problems quantum computing companies cite when they pitch molecular simulation to pharmaceutical buyers. Here a classical model trained on experimental crystal data takes over a large part of that job on a single GPU node.
The results come from retrospective tests on public structure collections. The study screened no new compounds in the laboratory and involved no patients, and its training labels describe molecules in the crystal, which is a different environment from a protein pocket or blood plasma.
Simulated Quantum Annealing Ranked Known Drug Pairs First for Diabetes and Rheumatoid Arthritis: Ramos, Coutinho and Magano in Quantum Machine Intelligence, 23 June 2026
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Evidence-based insights for quantum medicine.
Three researchers from Porto and the German Aerospace Center have written drug-pair selection as an energy problem that a quantum annealer could solve, and tested it on diabetes, rheumatoid arthritis, asthma and brain tumors. In simulation, the lowest-energy answers were dominated by drug combinations that doctors already use, and one of the top new suggestions for rheumatoid arthritis matches a pairing that a 799-patient trial found to roughly double remission rates.
The paper, by Diogo Ramos, Bruno Coutinho and Duarte Magano, appeared open access in Quantum Machine Intelligence on 23 June 2026 as article 70 of volume 8. It builds on the Complementary Exposure idea from network medicine, turns it into an optimization problem, and samples answers with simulated quantum annealing, a classical computer program that imitates how quantum annealing hardware behaves. Its central test is how often validated drug combinations appear among the top-ranked answers when they make up less than a tenth of a percent of the candidates.
Combination therapy is standard across chronic disease and oncology, because two drugs together can allow lower doses and fewer side effects. The search space is the problem. With more than a thousand approved drugs and thousands of diseases, the number of possible pairs grows far faster than any laboratory or trial program can test them.
For a pharmacology team or a hospital formulary committee, the study offers a clear example of what quantum optimization can and cannot yet contribute to that search. The ranking method works in simulation on four diseases. No quantum computer was used, and the authors state plainly that their predictions are hypotheses for experts to examine.
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.
Phasecraft's 3,000-Emulation VQE Molecular Dataset With NVIDIA Was Computed on GPUs Under Wellcome Leap's Q4Bio: What the 14 September 2026 Announcement Shows
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Evidence-based insights for quantum medicine.
Phasecraft and NVIDIA announced on 14 September 2026 what Phasecraft calls the largest known database of Variational Quantum Eigensolver results for molecules, more than 3,000 emulations across thirteen molecular systems, computed on NVIDIA Hopper GPUs at the University of Nottingham. No quantum computer ran any of it, and the announcement says so in its own description of the method.
The dataset is an emulated VQE molecular dataset: for each of the 13 molecular systems, a classical GPU cluster worked out what a quantum processor of 4 to 32 qubits would return when asked for the lowest-energy state of the molecule, with most of the circuits in the 24-to-28-qubit range. Its purpose is to train Phasecraft's quantum-enhanced density functional theory method, a way of improving the standard workhorse of computational chemistry with data a quantum computer would produce. The Wellcome Leap Q4Bio program that paid for it asks whether new algorithms can deliver quantum advantage for health, and its prizes require a demonstration on a real quantum computer with more than 50 qubits. For a hospital buyer or a clinical pharmacologist the announcement describes training data for a chemistry method, produced on conventional hardware, with no named molecular systems, quantitative accuracy benchmarks or clinical results disclosed.
The Phasecraft release, datelined London and Toronto, gives four numbers: over 3,000 unique emulations, 13 molecular systems, a qubit range of 4 to 32 with "the vast majority in the 24–28 qubit regime", and "a 15x simulation speedup over prior results when modeling physical, many-body systems integral to biology and health applications". The GPUs were NVIDIA Hopper hardware hosted at the University of Nottingham, driven through NVIDIA's cuQuantum software kit. Ashley Montanaro, Phasecraft's co-founder and chief executive, is quoted on the need "to push the limits of today's most capable hardware, both conventional and quantum"; Sam Stanwyck, NVIDIA's director of quantum product, on shortening "the timeline" to useful quantum computing. The release describes the dataset as "a training foundation for quantum-enhanced molecular modeling, which could accelerate drug discovery at a scale previously out of reach, as quantum hardware develops". The conditional clause at the end carries the weight of the sentence.
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.
AOI Biosciences' Roughly 500 Million Yen Financing and Share Transaction of 14 September 2026 Lists Its Neo-Self Antibody Test Ahead of the Toshiba SQBM+ Quantum-Inspired Drug Platform
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Evidence-based insights for quantum medicine.
On 14 September 2026 AOI Biosciences, a diagnostics and drug-discovery company in Ibaraki, Osaka Prefecture, announced a financing and secondary-share transaction of roughly 500 million yen with existing and new investors; the company says the new-share portion takes its cumulative funding to about 1 billion yen, and it does not state how much of the 500 million is new money. The release describes two core businesses. One is a blood test for a "neo-self" autoantibody that is already sold to more than 300 Japanese medical institutions. The other is an allosteric drug-discovery method built with Toshiba on the quantum-inspired SQBM+ solver, the method whose patent Toshiba announced on 3 September and this Monitor examined on 6 September.
The release lists three planned uses for the proceeds, and that list is the useful fact for a hospital buyer or a pharma partner, with one caution: the order says nothing about how much money goes where or which business the company values more. The neo-self antibody test fills the first two entries, with domestic roll-out, Asian expansion and new autoimmune indications, and further research investment in the quantum-inspired drug discovery platform is the third. No amounts are attached to any entry. The clinical figures the release cites for the test, a pregnancy rate more than doubled and a live-birth rate 1.7 times higher, come from two Frontiers in Immunology cohort studies whose treated and untreated groups can be counted, and this note counts them.
Lu Chaoyang Called "Quantum New Drugs" a Packaged Product at the 2026 Bund Summit; Califano Calls Cancer "Quantum" as an Analogy
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Evidence-based insights for quantum medicine.
In September 2026 the word "quantum" reached medicine from two directions, and the contrast is instructive for anyone who buys for a hospital or a drug company. In Shanghai, the physicist Lu Chaoyang told the 2026 Bund Summit that some Chinese companies sell "quantum new drugs" as finished products while leading research teams still offer prize money for a practical quantum algorithm. In New York, Columbia University published a feature on 26 August in which the cancer biologist Andrea Califano called cancer a "quantum disease", a comparison with electron energy levels and no quantum hardware in it.
Califano's phrase points at two Nature Genetics papers, a pancreatic cancer dataset of hundreds of thousands of single cells, and a testable count of six cell states. The quantum new drugs that Lu Chaoyang objected to are described in a Xinmin Evening News commentary of 11 September as marketing, and the commentary supplies no product-level evidence for a buyer to assess. It also asked why the correction had to come from a scientist and why no industry regulator has set a standard for what a company may call quantum. That is the question a buyer should put to a vendor before any contract is signed.
KRIBB and Quandela Signed a Memorandum in Paris During Meetings on 7 and 8 September 2026 to Apply Photonic Quantum Computing to Drug-Candidate Discovery
Quentir Medicine Monitor
Evidence-based insights for quantum medicine.
During meetings in Paris on 7 and 8 September 2026, the Korea Research Institute of Bioscience and Biotechnology signed three memoranda of understanding, one of them with Quandela, the photonic quantum computing company founded in 2017 on research from CNRS and Université Paris-Saclay. KRIBB announced the agreements on 10 September, and the Korean daily Herald Economy carried the institute's account the same day. The signing took place during President Lee Jae-myung's state visit to France; on 8 September, Korea's science ministry and France's economy ministry exchanged a government-level memorandum on artificial intelligence, semiconductors and quantum technology at the Élysée.
The KRIBB and Quandela agreement has one stated purpose: to combine artificial intelligence and quantum computing with biological research so that finding a drug candidate takes less time and money than it does today. KRIBB puts the usual time to discover a candidate at more than ten years. The public announcement does not disclose a molecule, a disease target, a budget or a delivery date, and Quandela's own product pages describe the two machines it documents in use, Lucy at the French national computing center TGCC and Belenos in its cloud, as 12-qubit photonic machines. Read together, those two facts set the honest expectation for this partnership: a research collaboration at the stage of a signed memorandum, with no demonstrated pipeline benefit yet and no published plan for producing one.
The Minimal-Basis Run Gave T4-Lysozyme the Wrong Binding Sign: What Cleveland Clinic, RIKEN and IBM Changed to Correct It
Quentir Medicine Monitor
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.
Japanese Patent 7,908,389, Registered 10 August 2026: What Toshiba and AOI Biosciences Claim for Allosteric Site Prediction, and What Quantum-Inspired SQBM+ Actually Runs On
Quentir Medicine Monitor
Evidence-based insights for quantum medicine.
On 3 September 2026 Toshiba Corporation and AOI Biosciences announced that a patent the two companies had filed together was registered in Japan on 10 August 2026. It covers an allosteric pathway prediction device: software that reads a protein's three-dimensional structure and predicts the internal routes leading to pockets that sit away from the site where the protein does its work.
Three parts of that announcement carry different weights. Japanese Patent 7,908,389 is a legal instrument whose protection runs to its granted claims in Japan, which is a separate question from whether the method finds sites nobody had found before. The supporting evidence offered in the release is one figure, accuracy of 80 percent or higher on already-known sites, reported without a definition of accuracy, a benchmark set or a paper behind it. And the optimization engine at the center of the work, Toshiba's SQBM+, is described as quantum-inspired, a term that here means the solver runs on ordinary classical computers and uses no qubits.
Quantum Foundry Copenhagen Plans a 5,300-Square-Meter Quantum Chip Factory for 2027: What the Novo Nordisk Foundation Announced on 4 September 2026
Quentir Medicine Monitor
Evidence-based insights for quantum medicine.
A health foundation announcing a chip plant is unusual enough to state plainly. On 4 September 2026 the Novo Nordisk Foundation and Quantum Foundry Copenhagen, the company the foundation started in 2023 and owns, said they will build a 5,300-square-meter quantum chip fabrication facility in Copenhagen, with an opening expected in 2027. The plant is to combine nanofabrication of quantum materials, characterization, testing, chip assembly and packaging, and it is to sell wafer fabrication to quantum technology vendors elsewhere in the world on commercial terms.
The foundation's own Danish announcement puts faster development of new medicine among the reasons for spending the money. Its total commitment to quantum technology now exceeds DKK 2.9 billion, about EUR 390 million, and the factory is the manufacturing end of it. Two days earlier the engineering firm Ramboll handed over the Copenhagen building that will house Magne, the machine the foundation co-owns through QuNorth. Both projects run toward the same declared destination, a fault-tolerant quantum computer in Denmark before 2034, and a clinician reading the announcement is entitled to ask what any of it does for a patient in the meantime.