Quantum computing is often discussed alongside AI as another technology that could accelerate drug discovery. But while AI uses patterns in data to generate predictions and hypotheses, quantum computing takes a fundamentally different approach to processing information.
While classical computing relies on conventional bits, which represent information as either a zero or a one, quantum computers use quantum bits, or qubits, which can exist in multiple states at once. This makes them potentially well suited to simulating the behavior of electrons and their interactions – processes that underpin chemical reactions and molecular properties but can be challenging to capture using classical computational methods.
It's a completely new computing modality.
—Ulrich Hoff, Kvantify
“It's a completely new computing modality,” Ulrich Hoff, Quantum Engagement Specialist at Danish software company Kvantify, told DDN. “Problems that would normally scale exponentially, we can now turn into more tractable scalings with quantum computers.”
For drug discovery, this could eventually help researchers investigate complex chemical interactions that current computational methods can only approximate. But the technology is still at an early stage, and integrating it into established drug discovery workflows will require progress on several fronts.
Combining quantum and classical computers
Quantum computing is not yet mature enough for widespread use in drug discovery, Hoff said. Current hardware cannot reliably tackle problems at the scale relevant to pharmaceutical research, and the technology remains largely experimental rather than part of production-scale drug discovery workflows.
For now, researchers are exploring hybrid quantum-classical computing, which combines the strengths of both types of computing within the same workflow. Rather than asking a quantum computer to perform an entire calculation, this approach assigns different parts of a problem to the computing technology best suited to handle them. Classical computers can manage tasks such as preparing data and processing results, while a quantum processor could be used for particularly challenging calculations involving molecular behavior.
“Many computations are inherently classical. There's no reason to put them on a quantum computer,” Hoff said. “It's not the computing modality that's important. It's solving the problem that's important.”
Hybrid approaches are particularly important at the current stage of quantum computing. Today's quantum processors are prone to noise and errors because qubits are highly sensitive to disturbances in their surroundings. These disturbances can disrupt the delicate quantum states of qubits through a process known as decoherence, limiting how many operations a processor can reliably perform before accumulated errors compromise the calculation.
Researchers are working to improve the reliability of quantum operations and develop error-correction methods that would allow processors to perform increasingly complex calculations. However, until quantum hardware becomes more reliable, classical computers will remain an essential part of the workflow.
Kamran Tavangar, Head of Science Strategy and Partnerships at LabCentral, has already seen one early-stage company begin incorporating quantum computing into a virtual disease model of triple-negative breast cancer (TNBC). The model is intended to help predict the most appropriate drug therapy options for individual patients with the disease, which accounts for around 10–15 percent of breast cancers and lacks the estrogen, progesterone, and HER2 receptors targeted by many common treatments.
It’s still being refined, but if quantum computing can make these models more accurate, it could enable more precise predictions about how an individual's cancer will respond to different therapies.
Expertise spanning domains
Quantum computing has the potential to accelerate and enhance drug discovery. But even as researchers work to improve the hardware and software, significant gaps remain in the infrastructure needed to bring the technology into biotechnology. Access to quantum computing remains difficult and expensive, while expertise spanning both quantum computing and biology is still limited.
We need people who understand each field and people who can work across both.
—Kamran Tavangar, LabCentral
“We need people who understand each field and people who can work across both,” Tavangar told DDN. This includes helping quantum specialists understand the biological questions researchers want to answer, as well as giving life scientists a better understanding of what quantum computing can realistically offer.
Patricia Geli, co-founder and Managing Partner of C10 Labs, believes bringing different groups together is essential to building this expertise. She highlighted the importance of education, training, and collaboration between researchers, companies, investors, governments, and foundations. These partnerships can help build the skills and infrastructure needed to turn early-stage technologies into practical applications.
LabCentral and C10 Labs have already partnered to build the AI BioHub, which supports early-stage companies working at the intersection of AI and the life sciences. The initiative combines access to laboratory facilities with technical expertise, industry mentors, and company-building support — the kind of infrastructure that could help emerging computational technologies move beyond algorithms and into real-world biological applications.
Ultimately, integrating quantum computing into drug discovery will require more than increasingly capable hardware. Researchers will need access to the technology, people who can work across disciplines, and opportunities to test whether computational predictions translate into meaningful biological results.
Pharma needs to help define the challenge
Hardware improvements alone will not guarantee that quantum computing becomes useful to drug developers. Researchers also need to identify specific problems where the technology could offer a meaningful advantage over existing approaches.
At present, pharmaceutical companies are taking a cautious approach, Tavangar said. Many are engaging with quantum computing companies through pilot projects but are waiting for the technology to mature before committing to wider adoption.
“Nobody is buying quantum off a shelf. Demand will run through research collaborations, where a lab brings a real problem and a quantum approach has to earn its place in the workflow," Geli told DDN. Pharmaceutical companies want to see evidence that a new technology can deliver value before investing in it.
But Hoff believes this wait-and-see approach risks slowing progress. Quantum computing developers need to understand the problems that drug discovery teams are struggling to solve if they are to build software and algorithms that address genuine industry needs.
“If we can solve this in a way that is better, faster, more efficient, whatever the metric is, than current solutions, then that's valuable to the industry,” he said.
That means pharmaceutical companies need to be more open about their computational challenges and work with developers to establish meaningful benchmarks. A quantum approach should not be considered useful simply because it can perform a calculation differently. It needs to demonstrate an advantage, whether through improved accuracy, reduced computational demands, or the ability to investigate systems that current methods cannot adequately model.
Creating those connections between quantum developers and the life sciences industry will also be a focus of Entangled 2026, an invitation-only summit taking place on November 19 in Cambridge, Massachusetts. Hosted by LabCentral, C10 Labs and Innovation Centre Denmark, the event will bring together researchers, investors, technology developers, pharmaceutical leaders, biotech founders, and policymakers to discuss what it will take for AI and quantum computing to contribute to meaningful advances in biotechnology.
