Life Sciences A to Z - Q is for Quantum Computing: Opportunities and challenges for life sciences innovation

The life sciences sector is already being transformed by artificial intelligence (AI), with the promise of faster, AI-driven drug discovery, improved diagnostics, and more efficient clinical trials. Ernst and Young’s 2026 M&A Firepower report highlighted a 256% increase in the potential value of life sciences deals aimed at accessing AI technology platforms.

However, AI’s potential is ultimately constrained by the limits of classical computers’ processing capacity. It is not yet able to simulate or predict the behaviour or interaction of certain novel compounds because they are simply too complex for even the most advanced supercomputers.

This is where quantum computing is emerging as a next frontier technology, with the potential to transform innovation across pharmaceuticals, healthcare, and biotechnology. Its possibilities are already being explored by Big Pharma, with a 2025 report on quantum technology by McKinsey & Company estimating potential value creation for the life sciences industry of $200 billion to $500 billion by 2035.

Whilst quantum technology remains at an early stage, it is no longer an intangible thing of the future. It was announced at the end of 2024 that Google had built a quantum chip, Willow. Whilst largely an experimental tool, Google claims Willow takes just five minutes to complete tasks that would take ten septillion years for even the world’s fastest conventional computers to complete. The potential for this to transform drug discovery in the future, including by speeding up the experimental phase of development, is clear.

This article explores the opportunities and risks that quantum technologies could present for life sciences businesses.

Quantum vs. AI: A Leap Forward?

Quantum technologies are designed to address precisely the challenges limiting AI capabilities that are underpinned by classical computers, leveraging the principles of quantum mechanics to process information. Where classical computers use binary “bits” to perform calculations, quantum computers use qubits. These can exist in multiple states at once (i.e. representing both 0 and 1 simultaneously), allowing them to explore solution spaces exponentially faster than classical computers. This opens the door to solving problems that are beyond the capabilities of AI.

It is important though, not to see these technologies as competing, or mutually exclusive. Quantum computing has the potential both to complement existing AI technologies and improve machine learning algorithms (e.g. by generating accurate, high-quality synthetic data to fill gaps in training data) and vice versa.

Opportunities: Transforming Life Sciences

Quantum technologies could revolutionise the life sciences sector in a number of ways:

Drug discovery: Quantum computing enables highly accurate simulations of molecular structures and interactions, which could significantly accelerate the identification of drug candidates and reduce reliance on costly laboratory experiments. For example, quantum algorithms can simulate and predict protein folding and drug-target binding with a precision that would not be achievable with AI, which could help in better understanding diseases such as Alzheimer’s and Parkinson’s.
Optimising clinical trials: Quantum algorithms would be able to support and optimise trial design, patient selection, and data analysis, making trials more efficient, and potentially reducing time to market as a result, for new therapies.
Diagnostics: Quantum sensing technologies are likely to be able to offer enhanced sensitivity and resolution, improving medical imaging and enabling earlier and more accurate disease detection.
Data analysis: Quantum computers can process complex genomic data sets, uncovering patterns and correlations that AI systems might miss. Quantum computing also has the power to generate accurate synthetic data that simulates real-world data, which could solve problems hindered by a lack of credible, high-quality data (e.g. in rare disease research). However, whilst quantum’s “problem-solving” is faster and more accurate in theory, it is not immune to error. For example, currently certain quantum computers are highly susceptible to environmental disturbances such as noise, and even changes in temperature can impact their output.
Supply chain and manufacturing: Looking to the lifecycle of drug manufacturing and supply chain, even simple optimisation calculations, or fine-tuning machine learning models, could lead to streamlined pharmaceutical manufacturing and distribution, improving efficiency and reducing costs.

Risks: Legal and Regulatory Challenges

The opportunities that arise with quantum technologies, come with legal and regulatory considerations. The following challenges largely mirror the pressure points also identified with AI implementation, and in respect of both new technologies, life sciences professionals should be anticipating the need to navigate an evolving regulatory landscape, with new risks and responsibilities.

Intellectual property: Quantum innovations challenge traditional IP frameworks. As quantum algorithms and hardware are developed collaboratively (particularly with collaborations potentially spanning government, academics and industry) and in a climate of rapid innovation, determining ownership and patentability is likely to become more complex. A new approach may be needed in due course to protect quantum inventions, combining patents, copyright, and trade secrets where necessary. There could also be a future need for open-source models to facilitate collaboration (with some quantum computer companies already offering open-source tools to assist in creation with fault-tolerant algorithms).
Data protection and cyber security: Quantum computing threatens certain current encryption standards, raising the risk of unauthorised access to sensitive patient and research data. Regulators such as the UK Information Commissioner’s Office are already urging the adoption of “post-quantum cryptography” and enhanced data protection measures. Key concerns are that sensitive patient data, genomic information, and proprietary research that has already been harvested in encrypted form could, in future, with the enhanced capabilities of quantum computing, be de-encrypted by threat actors manipulating the capabilities of quantum technology.
Ethics and consent: Quantum technologies heighten ethical concerns around data use, particularly to the extent that they enable deeper analysis of genetic and personal health information. There are the challenges of transparency and whether those who are affected by quantum processing are made aware of its use. As with AI, explainability of quantum outcomes (and the process to produce those outcomes) may also be challenging. With transparency and explainability comes the question of informed consent to quantum processing from those affected, as a matter of ethics and law.
Bias: As with AI, quantum algorithms can perpetuate biases from biased or unrepresentative datasets if not carefully designed and validated. Moreover, in life sciences and pharmaceutical quantum applications, bias and over- or under-representation of target groups or group characteristics (for example based on gender, race or socio-cultural factors) in pharmaceutical development can lead to a range of health risks or inefficacy.
Liability: Determining liability for quantum-informed decisions, such as adverse drug reactions or diagnostic errors, will almost certainly require new legal frameworks, particularly in circumstances where these are made with potentially decreased transparency and/or meaningful “human-in-the-loop”.
Legal and regulatory uncertainty: It is fair to say that existing regulatory frameworks are already struggling to keep up with technological advancements. Mirroring its approach to AI, the UK government appears to be avoiding strict legal requirements and taking a pro-innovation regulatory approach to quantum technologies, advocating sector-specific regulation focused on each technology’s risks and benefits (see Policy paper Regulating quantum technology applications: government response to the Regulatory Horizons Council). In due course, regulators in the life sciences sector will also need to update their guidelines and standards to reflect progress in quantum technology to ensure that they are addressing sector-specific concerns.

Legal Landscape for Quantum Technologies in Europe

In comparison with the UK, the legal landscape in Europe seems to be moving at a swifter pace. Last year, the European Commission published its Quantum Europe Strategy, to position Europe as a global leader in quantum by 2030 with an outline of its approach to quantum research, development and commercialisation. Its legislative proposal for an EU Quantum Act will follow later this year, with an ambitious timeline for adoption in 2026. The objectives for the Act focus on boosting research and innovation, scaling up industrial capacity, and reinforcing supply chain resilience and governance.

Next Steps for Life Sciences Organisations

It is fair to say that quantum technologies represent a (quantum?) leap forward in life sciences innovation, promising breakthroughs that AI alone cannot deliver. However, for life sciences organisations there are clear legal challenges to anticipate and overcome, which will require proactive engagement from life sciences professionals. To unlock the full potential of quantum technologies, and make advance preparations for future regulation, life sciences organisations should be looking to:

Establish internal governance processes that are clearly documented, transparent and flexible, whilst legal frameworks and guidelines are in flux and struggling to keep up with technological progress.
Consider specific use cases that quantum compute power could be helpfully applied to, having regard to the opportunities and challenges.
Develop ethical policies for quantum-driven research.
Audit and update IP strategies to reflect future challenges.
Assess cyber security and data protection frameworks for quantum resilience (and post-quantum security) and compliance with evolving regulations, update data protection impact assessments (DPIAs) and data governance policies accordingly. A focus on data minimisation and robust access controls should be prioritised.
Engage proactively with regulators to shape sector-specific guidelines for quantum applications.

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