European scale-up Algorithmiq is helping pioneer the use of quantum simulation for drug discovery, one of the 10 emerging technologies for 2026 identified in a report by the World Economic Forum compiled in collaboration with Frontiers, the open-science publisher.
“The value of quantum is that it will be able to bring new medicine on the market much faster, enable personalized medicine, and help design better biomarkers and detectors for disease,” says Boris Sokolov, Algorithmiq’s lead researcher, who co-founded the company in 2020 with Sabrina Maniscalco, Guillermo García-Pérez, and Matteo Rossi. Algorithmiq began as a Finnish startup and moved its global headquarters from Helsinki to Milan in May.
The company does not develop new drugs itself. Instead, it works with pharma companies and clinics to identify targets and then develops specialized quantum software to address them. Algorithmiq’s Aurora drug-discovery platform employs a proprietary method for quantum chemistry simulations. The platform is currently being used to develop cancer drugs as part of a computational research collaboration with IBM and Cleveland Clinic.
The collaboration began in 2023 under Wellcome Leap’s Quantum for Bio (Q4Bio) challenge. IBM Quantum, Cleveland Clinic, and Algorithmiq set out to build tools to test quantum computing’s potential in cancer drug development. Using Algorithmiq’s Aurora platform, run on up to 100 qubits on IBM quantum systems and supported through the Cleveland Clinic Quantum Innovation Catalyzer Program, the team studied light-activated (photodynamic) cancer drugs. The core goal was to model photon-activated photosensitizer drugs that stay dormant in the body until triggered locally in a tumor by a specific wavelength of light. “These types of drugs are very difficult to develop using traditional methods due to complex light-matter interaction,” says Sokolov. “The drug in question is currently in clinical trials.”
Q4Bio launched with 12 teams sharing access to $40 million in funding; by March 2026, six Phase III finalists remained. To qualify for the $2 million Phase III award, teams had to demonstrate algorithms using more than 50 qubits and circuit depths of roughly 1,000 to 10,000 gates, with a clear path to scaling.
In April, Algorithmiq was named the sole winner of the $2 million prize. The other finalist teams were led by Infleqtion, the University of Nottingham, Harvard, Stanford, and Oxford. Algorithmiq is also working with Cleveland Clinic on applying quantum-inspired algorithms to map brain activity networks and better understand neurocognitive outcomes after epilepsy surgery.
The Promise of Quantum Simulation for Drug Discovery
For decades, some conditions have been screened out of drug pipelines because the odds of failure were too high to justify the cost, says the Forum report. Part of that failure has been computational: classical computers could not model molecular behavior accurately enough to predict whether a drug would work. If quantum simulation reduces that uncertainty, diseases once considered too costly or commercially marginal could become more viable targets. “This could be especially important for rare and orphan diseases — now the fastest-growing segment of quantum-enabled drug discovery,” says the report. But “whether those treatments materialize will depend on decisions about capital, regulation and research priorities, especially for patients whose diseases have long been underserved by conventional drug development.”
A Crowded Field
The promise of quantum simulation in drug discovery is attracting a host of big and small players. In 2025, IBM and Moderna completed the largest protein folding and mRNA simulation run on a quantum computer to date, a milestone that had been out of reach just a few years earlier, notes the Forum report.
In France, startups Pasqal and Qubit Pharmaceuticals are pursuing a different path, using neutral-atom quantum computers for small-molecule discovery with backing from the Wellcome Trust. Paris-based Qubit, founded in 2020 by academics from Sorbonne University, CNAM, Washington University in St. Louis, and the University of Texas at Austin, and led by CEO Robert Marino, runs its own pipeline, with programs in oncology, inflammation, and pandemic response. It performs drug-discovery tasks on Pasqal’s neutral-atom quantum processing units (QPUs) and in April 2026 announced a collaboration with Singapore’s Centre for Quantum Technologies.
Other competitors include:
- Denmark’s Kvantify, whose Qrunch platform, launched in November 2025, runs quantum chemistry on today’s quantum hardware.
- UK-based Phasecraft , part of the University of Nottingham-led Q4Bio finalist team. The team used quantum-generated data to improve classical density functional theory (DFT) simulations of covalent binding, applied to a drug program for myotonic dystrophy type 1, a disease with no current treatment.
- S.-based Infleqtion, another Q4Bio finalist, is working on quantum-enhanced cancer biomarker discovery from multimodal data with the University of Chicago and MIT.
- S.-based PolarisQB takes the quantum-annealing route: its QuADD platform runs on D-Wave quantum annealers and frames molecule design as a constrained-optimization problem. The company claims it can generate drug-like candidates in hours or in one to three days. In 2025 it won a Defense Advanced Research Projects Agency (DARPA) award to advance quantum computing for drug design.
- Big pharma and research centers. Germany’s Boehringer Ingelheim, one of the world’s largest family-owned pharmaceutical companies, collaborates with Google Quantum AI, and the pharma research and early development (pRED) unit of Swiss health care giant Roche has explored quantum machine learning with QC Ware.
Sokolov says Algorithmiq’s key differentiators include its staffing. The scale-up employs 55 people. The majority work in R&D — about half specialize in algorithms that make quantum computers work, and the other half are quantum chemists, “because when you do quantum chemical simulations you need the whole pipeline to be built at the same time,” says Sokolov. Another differentiator is the scaling approach, he says. “Everything by Algorithmiq is designed to be able to tackle more difficult problems as quantum hardware gets better,” says Sokolov. “This comes from the team’s experience of using real quantum computers practically from when they first became available.”
Although drug discovery is a key focus, the Milan-based scale-up is also applying its quantum software to materials science. In July, Algorithmiq and IBM announced a joint demonstration of quantum advantage in simulating a heterogeneous quantum material, along with a framework for trusting results when classical verification is unavailable. The scale-up has raised a total of €46 million.
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