Quantum computing, a type of quantum technology, is poised to address pressing challenges in the healthcare sector. For example, while the average cost of the drug development pipeline has tripled since 2010, this has not translated into shorter times to market or higher numbers of newly approved drugs. Quantum-based discovery techniques could augment and accelerate existing efforts in drug discovery, diagnostics and precision medicine.
Several countries including the UK, USA, China, and Canada are investing substantially in quantum computing, with the global market for quantum computing in healthcare projected to reach $503 million by 2028 at a compound annual growth rate (CAGR) of 42.5%. However, important questions remain: What real-world healthcare needs could quantum computers meet in a cost-effective way? And if quantum computers fall short, are there viable alternatives that should be pursued instead?
What is quantum computing?
Quantum computers leverage the principles of quantum mechanics – the study of atomic and subatomic particles, their behaviours, and their interactions with each other – to perform computations. These are inherently different from the classical computers that we currently use. In classical computing, the basic unit of information is a bit, which can be 0 or 1, and a combination of these bits is used to encode information. In contrast, quantum computers use qubits (quantum bits), which can exist in a superposition of states i.e. in combinations of both 0s and 1s. The 0s and 1s, for example, can correspond to high and low energy states of atoms or ions that are used to build the qubits. The qubit collapses into or assumes a 0 or 1 state when it is measured. For example, a qubit existing as 40% 0 and 60% 1 would collapse into the 1 state with a 60% chance when measured. There are also several types of qubits, built with different physical technologies, which offer different capabilities and operational requirements. For example, some require super cooled temperatures to work whereas other qubits can operate at room temperature.
While a single qubit is not more powerful than a classical bit, combining multiple qubits introduces quantum entanglement, a phenomenon where qubits become linked and act as a single system. This entanglement allows fast transmission of information between qubits and enables quantum ‘parallelism’ which allows quantum computers to conduct multiple operations simultaneously, making them exceptionally powerful for solving certain complex problems which are beyond the capabilities of current classical computers. However, errors can propagate equally fast through such an entangled system, adding instability or noise to the output. Researchers are actively working to mitigate errors in quantum computers before large-scale deployment, otherwise we risk building expensive systems that output meaningless results. Furthermore, successful deployment and uptake of quantum computers will be based on demonstrating quantum advantage or proof that a quantum computer can solve a problem that is either intractable or can be solved faster and more resource-efficiently than on the best available classical supercomputer.
Quantum computers: why now?
Google’s 2019 quantum advantage experiment made headlines when its quantum computer completed a task in 200 seconds that would have taken a classical supercomputer 10,000 years. However, progress in quantum computing has not been linear. Coined in 2018, we are currently in the Noisy Intermediate-Scale Quantum (NISQ) era, where physical qubits are difficult to stabilise due to environmental interference which causes them to collapse or ‘decohere’ and produce unreliable results or ‘errors.’ To overcome these challenges, the term ‘logical’ qubits was coined. These are an abstraction formed by grouping together physical qubits such that collectively they are robust against errors, ensuring more reliable quantum computations.
There was also a substantial decrease in venture capital investment in 2023 (approximately 50%), prompting discussions of a “quantum winter”. Despite this setback, 2024-2025 marked a turning point with huge progress in the quantum industry. This acceleration was driven by demonstrated exponential error reduction and the emergence of clear, milestone-driven commercial roadmaps. For example, Google’s new Willow quantum chip showed improved error correction with increasing qubit numbers, completing a task in under five minutes that would have taken a classical supercomputer an estimated 1025 years.
The UK’s Commitment to quantum
Quantum technology is one of the fastest growing sectors of the UK economy. It is anticipated to contribute £11 billion to the UK’s GDP and create over 100,000 jobs by 2045. While some countries are investing heavily in a specific qubit hardware, the UK is taking a broader, modality-agnostic approach with a strategic, government-backed push, centered around a £2.5 billion National Quantum Strategy. In addition, some key programmes and institutes that are uncovering or supporting new quantum capabilities in the UK include:
The National Quantum Technologies Programme (NQTP) was a 10-year programme launched in 2014 to help translate academic research in quantum mechanics. Four quantum hubs focussed on computing, sensing & timing, imaging, and communications were set up as part of the NQTP. In 2024, the NQTP concluded and to continue momentum in this space, five new hubs were announced. These new hubs aimed to support advanced work in biomedical sensing, integrated quantum networks, quantum computing implementation, and navigation technologies.
The NQCC is a UK Research and Innovation (UKRI)-funded centre focussed on accelerating quantum computing development, particularly by addressing scale-up challenges. It brings together industry, government, and academia to coordinate research, development and operations of new quantum facilities. The NQCC received a new 10-year funding from UKRI in 2025.
Established in 2015, QMI advances testing, evaluation, and commercialisation of quantum technologies, and acts as a national hub for standards and measurement.
In 2023, the UK published its National Quantum Strategy outlining five key missions to help realise the potential of quantum technologies. Following this, the Regulatory Horizons Council (RHC) reviewed the scope of quantum regulation and agreed it was too early to put strict rules in place, but emphasised the need for ongoing capability building and future-ready frameworks. Based on the RHC’s recommendation, the UK government launched a Quantum Regulators’ Forum in April 2025. The purpose of this forum is to provide a space for regulators to discuss quantum technologies as and when they develop.
In 2025, the UK government announced substantial funding, including £121 million in April and >£670 million in June, to support quantum research and computing efforts. Separately, the UKRI announced a budget allocation of over £1 billion for quantum technologies in December 2025, though clarity on how this budget will be utilised or deployed is pending.
In March 2026, the UK government announced further significant investment, committing £1 billion for procuring large-scale quantum computers and dedicating over £500 million to help companies scale and develop new quantum computing applications in areas including pharmaceuticals.’
The UK has begun to pioneer approaches to quantum scale-out and shared infrastructure, with initiatives such as Project Lyra focusing on developing quantum data-centre architectures and multi-node computing environments to enable industry-scale utility and scalable deployment.
The Wellcome Trust’s Quantum for Bio (Q4Bio) programme has a $50 million portfolio focussed on healthcare quantum applications.
The Commercialising Quantum Technologies Challenge (2018–2025) led by Innovate UK has funded around 140 projects across sectors, including healthcare.
UK spinouts such as Riverlane (error correction) and Phasecraft (quantum algorithms for molecular modelling) are emerging, though companies focusing on quantum algorithms remain few, highlighting an area for growth.
The UK currently has limited capacity for manufacturing critical components like silicon chips, and to hedge against uncertainty, has adopted a broader, modality-agnostic approach rather than committing to a single hardware platform. Furthermore, a recent review of UK quantum companies found that existing open-access infrastructure is inadequate to meet industry needs, recommending upgrades and improved access to existing infrastructure for industry, and better coordination across industry, academia and government to develop and deploy quantum technologies. As systems move toward deployable infrastructure, supply chain considerations are shifting from qubits alone to critical components and specialist engineering capabilities, including photonics, control electronics, packaging, and networking. Ensuring access to these capabilities, whether through domestic manufacturing, foundry partnerships, or international collaboration, will be essential for translating research strength into scalable hardware deployment.
Working quantum computers do exist today, but they are not yet practical for widespread use. Nonetheless, early applications and prototypes such as those from IBM suggest that usable quantum computers could emerge within the next decade. Furthermore, the task ahead is a transition from today’s NISQ machines to what is called fault-tolerant application-scale quantum (FASQ) which are systems capable of running long, error-free computations. Therefore, now is the time to think actively about how quantum computers could reshape sectors like healthcare.
Applications of quantum computing in healthcare
The NQCC published an Insights Paper in March 2025, exploring how quantum computing could help shape healthcare and pharmaceuticals over the coming decades. The paper identified over 40 proof-of-concept use cases which aligned with the healthcare priorities set out in the NHS 10-Year Health Plan for England and the UK’s National Quantum Strategy. Therefore, there are early adopters of quantum computing within the healthcare sector, largely driven by substantial investment and the strategic co-design of use cases with the quantum industry. This is further evidenced by early acquisitions (Odyssey Therapeutics acquiring Rahko, a quantum machine learning company), major partnerships (Roche, Merck’s partnership with quantum startups), and dedicated quantum funds (supported by DKK 1 billion commitment from the NovoNordisk Foundation).
Such developments highlight a favourable climate for the healthcare sector to model ‘proof-of-concept’ studies that focus on high-value but computationally challenging problems such as the 5,000 logical qubit requirement for pioneering cancer treatments. It also enables a virtuous cycle of mutual development wherein the quantum sector’s drive for utility meets the healthcare industry’s concern for cost-benefit and real-world impact.
Some current promising use cases in healthcare include:
Quantum computers can be used to simulate complex molecular systems, model how new and complex proteins fold, predict chemical bonding with high accuracy, and carry out high-precision calculations to estimate binding affinities between drug molecules and their protein targets. This level of detail is especially valuable in accelerating drug development and creating better drugs for challenging conditions such as cancer and antimicrobial resistance (AMR). Progress in this area includes:
Amgen and Quantinuum successfully completed an exploratory proof of concept showing how quantum computing can aid drug discovery. They used a quantum processor to study peptide binding by framing the therapeutic protein design challenge as a sequence classification task suitable for current quantum hardware, thereby showing that quantum-enabled molecular modelling can address concrete drug discovery problems without requiring fully fault-tolerant quantum chemistry.
PASQAL in collaboration with Qubit Pharmaceuticals have developed a hybrid quantum–classical algorithm to identify water molecule configurations within protein binding pockets which is critical to estimating binding affinities accurately. These computations are advantageous as they are difficult to resolve with classical simulations alone.
Hard-to-treat cancers, particularly those driven by mutations in KRAS, have historically resisted classical drug design due to the computational complexity of their underlying molecular interactions. Researchers at St. Jude Children’s Research Hospital have reported one of the first drug discovery workflows to incorporate quantum-enhanced machine learning with experimental validation to synthesise candidate molecules that can target KRAS. These molecules were shown to exhibit biological activity in laboratory assays.
A major NHS priority and public health threat is AMR. Researchers used a quantum machine learning approach to demonstrate that quantum methods can match or outperform classical models in predicting antibiotic resistance in urine culture data. This work represents one of the first quantum machine learning applications targeting AMR, highlighting the potential of hybrid quantum-classical systems for building diagnostic and optimisation tools.
Quantum-inspired algorithms have shown promise in improving the accuracy and speed of diagnostics, by helping identify new biomarkers. In one recent proof-of-concept, a quantum machine learning approach outperformed a classical model in classifying cancer cell types from liquid biopsies. They can also help with low-data, high-compute problems, which is relevant for underserved areas such as rare diseases and women’s health, where data are sparse, and disease-specific patterns are hard to detect with classical methods. In another example, Universal Quantum has joined the Open Quantum Institute and are exploring how quantum simulation methods might speed up the discovery of new non-hormonal treatments for endometriosis by modeling the complex biological processes that underlie this condition.
Genomic analysis often involves working with a large number of data points, such as millions of single nucleotide polymorphisms (SNPs) per individual. Traditional methods require simplification or feature reduction to make the data manageable, but quantum computing could allow researchers to analyse high-dimensional genomic and clinical data in real time. Over time, advances in computational approaches to genomics may prove relevant to national initiatives like the NHS Genomic Medicine Service, which seeks to integrate genomic insights into routine care for cancer, rare disease, and pharmacogenomics, areas where managing high-dimensional genetic data remains a central challenge. Some current promising developments include:
Quantum annealing is a specialised method in quantum computation for solving large optimisation problems by searching for the best solutions, and therefore, can help analyse complex genome-scale tasks. Researchers used the D-Wave quantum annealing device to conduct small-scale haplotype assemblies on synthetic diploid and triploid genomes, achieving a reduction in processing time by three orders of magnitude compared to traditional algorithms. This represents a major leap in the intersection of quantum computing and bioinformatics.
Identifying new Chimeric Antigen Receptor (CAR) T-cell constructs is a non-trivial, highly data-constrained combinatorial problem. Researchers addressed this challenge with a quantum approach using a Projected Quantum Kernel (PQK). Utilising 61 qubits on a gate-based quantum computer, they demonstrated the largest PQK application to date, achieving an enhancement in CAR T cytotoxicity prediction performance over purely classical machine learning methods, particularly where data was sparse.
In theory, a moderate-scale quantum machine could handle entire population-level datasets like those from UK Biobank as a single object, making it possible to apply quantum algorithms across genomic, clinical, and imaging data simultaneously. From a systems perspective, the near-term significance of multimodal quantum analysis is its potential to identify stronger correlations across heterogeneous data types of population-level datasets. Proof-of-concept work, including NQCC-supported clustering of multiomic data to understand cancer via the Omics Insights with Quantum (OmIQ) collaboration and novel validated on the National Health and Nutrition Examination Survey (NHANES) and UK Biobank demonstrates the feasibility of quantum-enhanced models on large, complex health data. The HQML framework achieved 94.7% AUC for type 2 diabetes prediction five years before clinical diagnosis, outperforming state-of-the-art baselines by 6.2%. In another recent proof-of-concept, a quantum machine learning algorithm was used to analyse multiomic data, successfully identifying meaningful clusters for further investigation.
Because many biological problems are essentially optimisation problems, quantum-inspired techniques could significantly improve how we study and treat diseases. For example, in-depth understanding of gene expression regulation networks, genome-scale alignments, disease models at the molecular level, could be achieved through quantum computers. A spin-out led by UK academic researchers (UCL), Quantum Motion, has installed a fully functional silicon-based quantum computer at the NQCC, enabling real-world testing of quantum applications including drug discovery and complex chemical simulations. This machine uses spin qubits embedded in purified silicon, and the same highly scalable and mature fabrication processes already established globally for classical chips. This is a crucial advantage for moving quantum computing beyond the lab and toward mass production.
In addition to the use cases above, quantum computers may also help improve healthcare delivery. For example, as quantum computers are particularly adept at handling complex combinatorial optimisation problems, they could be used to manage staff rostering, shift planning, bed allocation, operating theatre scheduling at a multi-hospital level. Moreover, quantum sensors, devices that use principles of quantum mechanics to detect physical quantities with extreme precision and sensitivity, are being developed for use in clinics. Processing the data generated by a quantum sensor using a classical computer, requires transforming the data into a classical file where much of its “quantum-ness” is lost. Therefore, integration of quantum computing with quantum sensing is another promising but technically challenging frontier. Currently, experts in quantum computing may have limited understanding of or may not directly work with quantum sensing systems and vice versa, a gap that needs to be bridged for meaningful progress.
As discussed above, quantum computing presents promising new capabilities for both healthcare research and delivery, although most applications are still in the research phase. Moving these promising applications into clinical settings requires a problem-led rather than technology-led approach, i.e. a clear focus on meeting unaddressed clinical needs and involving relevant healthcare stakeholders to co-develop solutions. While it is a question of when (not if) quantum computing is used to inform clinical care, the question of who within healthcare will champion the uptake of quantum computing applications needs further thought.
Challenges and considerations for at-scale deployment of quantum computers
There is a lack of consensus within industry, government, or academia on precisely when quantum computing will reach widespread usefulness at scale. This uncertainty reflects a combination of factors, including limited transparency around company roadmaps due to competitive dynamics, as well as divergent technical strategies across the sector. Some quantum hardware developers focus on scaling physical qubit counts alongside applying increasingly sophisticated quantum error-correction schemes, while others aim to engineer qubit technologies with greater intrinsic stability or alternative architectural advantages. This divergence leads to differing assumptions about timelines to practical utility. For example, major players such as IBM have articulated near-term goals focussed on demonstrating useful workloads on increasingly large and error-mitigated systems, rather than waiting for fully fault-tolerant machines.
A further source of confusion lies in the conflation of terminology. Concepts such as quantum advantage, usefulness, and industrial-scale deployment are often mixed with specific technical milestones (e.g., breaking RSA-2048 encryption). While breaking RSA-2048 is a well-known benchmark, it is neither necessary nor sufficient for delivering practical value in fields such as healthcare, chemistry, or optimisation, where useful quantum applications may emerge earlier through hybrid or problem-specific approaches. Healthcare-specific considerations include:
1. Technical challenges
Some types of qubits require extremely controlled environments with low temperatures that are isolated from vibrations and electromagnetic interference to maintain a probabilistic state. Moving from tens of qubits to thousands or even millions is still considered an R&D challenge. This is because adding more qubits makes the system much more complex, mainly because of increased difficulty in controlling, calibrating, and correcting errors. While error-correction techniques are improving, they are expensive and resource intensive. Researchers are, therefore, also exploring architectures that can inherently reduce reliance on complex error-correction protocols.
Unlike classical computers with established operating systems (e.g., Linux, Windows, iOS), quantum hardware lacks a standardised OS. The lack of a mature quantum OS is not just a software gap, but a structural consequence of hardware heterogeneity and the absence of networked quantum architectures. Some projects like Deltaflow and NSIQ.OS are in development and aim to unlock qubit-agnostic software. Furthermore, platforms like qBraid, BlueQubit, and Strangeworks are emerging to make quantum hardware access more user-friendly and reach a broader user base.
While the UK is making impressive progress in advancing quantum hardware, the development of quantum algorithms is lagging. These algorithms are essential for performing the mathematical and physical operations that give quantum systems their edge. Quantum software initiatives, such as those at the National Quantum Computing Centre (NQCC), are working to bridge this gap. But progress is slow. From a technical perspective, many near-term quantum algorithms face well-documented scaling limitations and hardware imperfections further limit the ability to maintain advantage at scale. Consequently, research emphasises that quantum algorithms must be co-designed with hardware architectures and error-correction strategies, as their performance is tightly constrained by system overhead.
Researchers are still figuring out what quantum-ready data looks like and how to prepare or adapt existing datasets for quantum computing. This is a crucial piece of the puzzle for making quantum applications truly effective in real-world scenarios. For healthcare in particular, the sensitive and fragmented nature of clinical datasets adds an extra layer of complexity to this data preparation challenge. From a systems perspective, quantum-ready data is not simply a matter of formatting, but of alignment between data architectures, algorithms, and hardware capabilities. Most real-world datasets are generated and optimised for classical analytics and machine learning, whereas quantum algorithms often require alternative data representations, encodings, or problem reformulations to deliver value. As a result, preparing data for quantum applications is a cross-disciplinary challenge involving domain expertise, data engineering, and quantum algorithm design, and remains an underdeveloped but critical area for enabling meaningful quantum advantage in practice.
2. Systemic hurdles to implementation
Beyond the significant technical challenges, there are numerous systemic hurdles to be overcome:
The NHS relies heavily on legacy IT infrastructure and there will be a need to upgrade infrastructure such as high-speed connectivity to support use of advanced computational resources. From a systems and implementation perspective, a critical constraint is that proof-of-value for advanced technologies is typically established at a local level, and variations across NHS trusts makes large-scale, uniform rollouts difficult, but simultaneously creates an opportunity for targeted, trust-level pilots. Modular, use-case-driven deployment that integrates incrementally with existing systems is therefore more likely to succeed, highlighting the importance of a strong translation layer between advanced computation and healthcare delivery.
While quantum computers are becoming more energy efficient, quantum computers capable of delivering real-world value still have significant energy demands for cooling and maintenance. From a systems perspective, the question is nuanced: some neutral-atom architectures have demonstrated power usage (e.g., a 256-qubit neutral-atom system at ~7 kW of total power use) orders of magnitude below classical supercomputers for similar scientific problems, suggesting potential efficiency advantages on specific high-complexity tasks. Conversely, empirical studies show that current quantum devices can have higher energy consumption for simple problems due to cooling and control overheads, underscoring that realised efficiency gains will depend heavily on workload type, system architecture, and future hardware and algorithm co-design. Over time, greater abstraction and cloud-based access to quantum hardware may play an important role in improving energy efficiency, by increasing utilisation and enabling shared access to specialised systems.
Quantum-driven automation may reshape jobs. While UK universities are producing many quantum PhDs, there is a shortage of professionals who can bridge the gap between quantum science and healthcare workflows, especially at policy or leadership levels. Beyond technical expertise, entrepreneurial and communication skills may be needed to translate quantum innovations into practical clinical or pharmaceutical applications. Without this cross-disciplinary leadership layer, promising quantum applications risk remaining confined to research settings rather than progressing into operational healthcare workflows. Initiatives such as the NHS Learning Hub quantum programme reflect growing awareness and capability-building across the system, and present a potential foundation upon which more clearly defined leadership and translation roles could be developed over time.
Warnings from bodies like the World Economic Forum highlight the risk of a “quantum divide“, where capabilities concentrate in well-resourced sectors, potentially widening existing healthcare disparities. Within the NHS, inequitable implementation could emerge if access to quantum-enabled capabilities is limited to well-funded healthcare centres and therefore deployment will need to be carefully governed. Addressing these risks requires not only adherence to ethical frameworks, but also deliberate choices around infrastructure, access models, and coordination to ensure that potential benefits are distributed across the health system.
Cybersecurity risks – Tactics such as “Harvest now, decrypt later”, where encrypted data is collected and stored in anticipation of a powerful decryption tool such as a quantum computer, pose risks. Data such as patient records that are secure today might not be secure in future. To counter this threat, organisations are encouraged to start considering post-quantum cryptography, which involves using cryptographic algorithms believed to be resistant to quantum attacks.
Most clinicians and service commissioners are not familiar with what quantum computing entails, often focusing on AI-based applications nearing clinical use, with quantum frequently perceived as a distant concern. In practice, limited clinical appetite for quantum computing reflects a lack of translated, use-case-specific propositions that align with clinical priorities and operational constraints. Adoption is therefore more likely where quantum-enabled capabilities are embedded within familiar tools, workflows, or decision-support systems, and where value is demonstrated through locally relevant outcomes. Ultimately, improving awareness requires effective translation, integration, and demonstrating proof-of-value within real clinical contexts.
How could quantum fit within the current healthcare ecosystem?
The UK Government’s vision is to deploy functional quantum computers for use in NHS trusts within the next decade. But this is a big ask. From a healthcare perspective, quantum computing poses no more risk than regular computational devices, but can add new capabilities suggesting that there could be value in its uptake. However, in an environment where resources are limited, healthcare funding may be better spent on innovations that solve near-term problems such as:
In parallel, advances in non-standard compute architectures are offering substantial improvements in performance and energy efficiency without needing to wait for quantum breakthroughs. New GPUs are being engineered specifically for high-throughput health applications. These developments open new possibilities for scaling AI in healthcare using existing infrastructure and better systems engineering between institutions.
In the UK, the complexity of data governance, with NHS data fragmented across hundreds of trusts each with unique access protocols, often makes moving data more challenging than its analysis. Federated approaches avoid this problem by allowing data to remain at its source, whilst enabling national-scale model training through the secure exchange of learned parameters. This model is both privacy-preserving and infinitely scalable. Programmes such as DARE UK are actively working on projects to improve understanding in this space. It is worth noting that federated data approaches can aid future large-scale efforts in multimodal quantum analysis, as they establish the necessary foundations for governance, interoperability, and distributed learning required to apply advanced computational methods, including quantum computing, across fragmented healthcare datasets.
Investing in these near-term applications may be more appropriate for the healthcare sector while at-scale usable quantum computers are being developed. However, the sector should not ignore quantum computing, as inaction risks missing out on its immense value in improving health outcomes. The healthcare sector can drive momentum in this space through targeted pilots, workforce upskilling, and cross-sector learning to ready itself to benefit from quantum computing as it matures.
Do we really need quantum computers?
There exists a notion that quantum computers can “do everything”, but their real promise lies in tackling specific types of problems that are currently out of reach for even the most advanced classical computers. In healthcare research, areas such as protein folding, molecular modelling, and large-scale genomic analysis involving extremely complex and high-dimensional data are challenges that quantum computers are inherently better suited to address. Equally compelling are optimisation and precision-driven tasks, like mapping gene–gene regulatory pathways.
There is also recognition that quantum computers are not intended to replace classical computers but augment its functioning by offering new capabilities. Therefore, quantum-classical hybrid systems are widely regarded as the most practical path forward for the near future of quantum computing. Much like how today’s software distributes tasks between CPUs and GPUs, future systems will extend this model to include quantum processing units (QPUs). In this setup, high-performance classical hardware gathers the data, handles the pre-processing before sending the relevant data to the quantum computer. The QPU then constructs the appropriate quantum circuit, performs the necessary measurements, and returns the output, typically as numerical data to the classical system for further analysis.
Some promising applications, which combine the capabilities of classical and quantum computers, are emerging in histopathology. Classical models can already distinguish between benign and invasive tissue images, but quantum computing could go further by helping to characterise cancer types, stages, and molecular features with unprecedented precision. For example, researchers augmented an existing AI workflow that classifies breast cancer subtypes from pathology slide images with quantum machine learning, and developed a methodology that achieves diagnosis with comparable accuracy to existing AI models but using 50% less data and approximately 25 times faster.
Such seamless interplay between classical and quantum hardware leverages the strengths of both quantum and classical systems. Yet, the engineering work that makes this integration possible does not receive sufficient attention. Developing the software and infrastructure to coordinate these hybrid workflows efficiently is a critical step toward making quantum computing useful in real-world scientific and industrial settings.
Conclusion
Quantum computing offers capabilities beyond that of existing classical computers. While the full scope of its likely impact in healthcare remains unclear, it has real potential to revolutionise a select class of problems where traditional approaches struggle. For example, in molecular simulation, advanced image reconstruction, and complex staff and hospital optimisation challenges. Such new capabilities make quantum computing attractive. The most practical path forward for this technology is seen in quantum-classical hybrid systems, where the engineering of efficient software and infrastructure to coordinate these workflows is a critical, current priority.
Ultimately, if quantum is going to be implemented in the healthcare sector, it will need to show clinical value – whether that is improved clinical outcomes or greater cost-effectiveness, and how it meets priority healthcare challenges.
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Acknowledgements
We would like to thank Laura Elvin and the Iusegenius team for sharing their extensive knowledge base and market insights in the quantum space that has greatly benefited this long-read. In addition, we would like to thank Dr Derek Craig, Prof Richard Harding, Matt Jones, Dr Rocio Martinez Nunez, Dr Stefano Mensa, Dr Jack Miller, Dr Keith Norman, Tom Scott, and Prof Ian Simpson for their time and valuable knowledge that has informed this long-read.