The Next Biopharma Revolution: Quantum Computing

Since the current administration’s executive order to accelerate quantum innovation announced in June, we’ve seen massive breakthroughs—from ETH Zurich’s “vibrating” quantum memory chip for managing complex algorithms to NVIDIA demonstrating an AI error-correction model that can suppress quantum data noise.

These advances are important both because they demonstrate progress in quantum computing and because they signal the technology is moving closer to practical applications across every industry, but especially in drug development.

For drug developers, quantum readiness is less a hardware decision than a data, infrastructure, and governance decision.

Today, we’re still in the noisy, small-scale era: hundreds to a few thousand physical qubits, but error rates limit what’s reliable. What’s changed is the progress in error correction, with logical qubits scaling and vendor roadmaps converging on fault-tolerant systems around 2029–2030.

We’re on a phased path forward, where hybrid quantum-classical pilots will be in early discovery through 2028, followed by the first fault-tolerant systems making specific chemistry and simulation problems practical in 2029 and beyond. Beyond that, we could see broader use across development, including trial design and genomics, redefining what’s scientifically and computationally possible in drug development.

As AI taught us, organizations with purpose-built data infrastructure will move through these phases faster as quantum approaches practical application.

A New Computing Paradigm for Drug Development

Like its predecessor, AI, the emergence of quantum technology brings the potential to solve problems previous technology couldn’t address. Although AI and quantum computing may follow similar adoption challenges and barriers along the way, albeit on different timelines, the two should not be viewed as competing technologies or investment priorities. Each has a distinct potential value add, and how they ultimately complement one another will continue to evolve as the technologies themselves do.

Rather than replacing AI, quantum computing could complement it, with each technology potentially bringing different capabilities to drug development. AI can help organizations extract insights and interpret data, automate workflows, and support decision-making, while quantum computing could eventually tackle computational problems that are difficult for classical systems.

For one, quantum computing could make entirely different classes of problems tractable. For example, drug discovery requires a deep understanding of the complex interactions between molecules and biological systems. Nature is much more closely aligned with quantum mechanics than with the zeros and ones used by classical computers. Classical systems can approximate these interactions, but where that approximation becomes the bottleneck, quantum computing could open new possibilities.

In drug development, the possibilities could include:

• Modeling interactions between candidate molecules and targets with enough accuracy to help rule candidates in or out before synthesis.
• Addressing difficult problems such as metalloenzymes, where classical methods struggle most.
• Modeling how proteins move and change shape in the body, something AI-predicted static structures do not fully capture.
• Tackling complex optimization problems involving adaptive trial design, site and cohort selection, and supply scheduling.
• Identifying interactions across millions of genetic variants.

In each case, quantum computing would handle the computationally intractable piece; the data platform and human judgment would still do the rest. Getting to this new era of drug development will require organizations to prepare for and harness the technology as thoughtfully as the technology itself evolves.

Quantum Readiness Starts Long Before Quantum Hardware

AI taught us that data and infrastructure can quickly become the constraint. Quantum will be no different. The organizations best positioned to take advantage of it will be those with unified, standardized data; strong quality and traceability; open APIs that allow data to move across computing environments; and clear governance around access and security.

The obstacles are familiar: legacy systems, silos between clinical, data science, and IT teams, and treating data as a trial byproduct rather than an asset. None of these are quantum problems, but they could determine who is ready to use quantum when the technology becomes viable.

Organizations that wait until systems are mature may find themselves years behind the organizations that began preparing earlier. This does not mean organizations need to invest in quantum systems today; rather, it means ensuring the underlying data and infrastructure can support new computational approaches when they become viable. Organizations should prepare for quantum by making their data standardized, traceable, portable, and accessible across computing environments.

Practically, there are five things for organizations to act on, none of which require buying quantum hardware:

• Take inventory. Map where data lives, who owns it, how it is structured, and how it moves. Most companies are surprised by what they find.
• Consolidate onto a modern clinical data platform. Replace point-to-point integrations with a single environment for acquisition, standardization, and analytics. This is the single highest-leverage investment because it pays off with AI today and quantum later.
• Standardize and enrich metadata. Adopt industry standards, document lineage, and capture the context that future algorithms will need to interpret data correctly.
• Design for hybrid compute. Assume the future stack will route different problems to classical, AI, and quantum resources. Architect for portability, cloud-native access, and APIs rather than tying data to a specific compute environment.
• Extend AI governance to cover quantum. Establish the frameworks for validating computational results, assessing risk, and documenting decisions now, so they can be applied to new methods rather than rebuilt for each.

Beyond these five foundational steps, organizations should pay particular attention to two additional areas: security and early scientific experimentation.

First, follow the security thread. The executive order’s companion directive accelerates the move to post-quantum cryptography, and life sciences companies holding long-lived patient and IP data should be on a similar path. Second, start small on the science side by watching hybrid quantum-classical chemistry pilots and identifying which of your discovery problems fit that pattern.

Readiness is less about being first to compute and more about having nothing in the way when the compute arrives.

Innovation Without Governance Creates New Risks

More computing power does not automatically translate into better scientific outcomes, eliminate risk, or guarantee that it will be a safe and efficient route to new discoveries. Like AI, as quantum becomes more widely available and used throughout drug development, organizations will need frameworks to determine when and how it should be used, alongside plans for addressing risks as they emerge.

Governance, security, and trust will become increasingly important as organizations layer quantum systems on top of AI, clinical data platforms, and other legacy systems. This is particularly important in clinical research, where data quality and traceability are fundamental to scientific and regulatory decision-making, especially in an era marked by rapid regulatory changes. If these considerations are delayed, they could further slow the adoption and implementation of quantum technology and create risks for both organizational investments and patients.

As organizations develop AI governance frameworks, they should also consider how those principles can extend to technologies on the horizon, like quantum computing. The goal is not to predict every risk quantum may introduce, but to establish the principles and accountability needed to evaluate these risks and develop management strategies to mitigate the impact.

In doing so, organizations can establish guardrails before quantum is widely deployed, rather than repeating the pattern of scrambling to address governance after implementation is already underway.

The Next Advantage Will Belong to the Prepared

Quantum offers an opportunity that goes beyond another technology investment, potentially expanding the computational capabilities that fuel drug discovery and development. However, as the industry continues to learn with AI adoption, realizing quantum’s potential will require preparation, not just adoption.

Not every biopharma company needs a quantum computer tomorrow. Organizations need to understand where quantum could eventually create value, identify the data and infrastructure gaps that could prevent them from realizing that value, and begin closing those gaps now.

The quantum era may still be emerging, but preparing for it is no longer purely a research exercise. For drug developers, the infrastructure decisions being made today could determine who is ready to take advantage of the next generation of computing—and who is left trying to catch up.