Healthcare has always been a domain where better tools mean longer, healthier lives. From the X-ray to the sequencing of the human genome, transformative technologies have repeatedly reset what medicine can achieve. Classical computers and AI have pushed the boundaries of drug discovery, imaging, and personalised care further than any previous generation of technology. Yet they are running into a ceiling defined not by engineering ambition but by the fundamental nature of the problems themselves. Many of the most important questions in medicine, how a protein folds, how a drug binds to a target, how genetic variations combine to produce disease, are rooted in quantum-level molecular behaviour that classical computers cannot simulate with precision or at scale.

Today, a new class of technologies, broadly called quantum technologies, has entered the picture. Though still maturing, the three pillars (quantum computing, quantum sensing, and quantum communications) carry the potential to reshape how we discover drugs, diagnose disease, secure patient data, and deliver care. We are already seeing progress. Internationally, pioneering partnerships such as the collaboration between IBM's Quantum System One and the Cleveland Clinic, the world's first quantum computer dedicated to healthcare, are already pointing to what the coming decade could look like.  Over in Europe, the United Kingdom has signalled its intent clearly, with an ambition to integrate quantum technologies across the National Health Service by 2030, alongside a recent commitment of $2.67 billion to establish the UK as a leader in quantum. This article maps the current state of quantum technologies in healthcare, where the promise is real, where it remains nascent, and what investors, policymakers, and healthcare systems should be watching.

Overview of the three quantum technology pillars

Before exploring specific use cases, it is worth establishing a clear map of the technology landscape. To truly appreciate what quantum technologies offer, it helps to understand the problem they are trying to solve. Within the quantum landscape, there are 3 distinctly relevant branches applied to various industries, including healthcare.

Quantum computing uses quantum bits (qubits) to process information in ways that allow certain problems to be solved exponentially faster than on classical hardware. Classical computers model the world in binary logic, using 0s and 1s, which works brilliantly for most tasks but runs into a wall when the problem involves the behaviour of molecules and atoms, which follow the rules of quantum physics. Within quantum computing, it is important to distinguish quantum-inspired approaches, classical algorithms that borrow ideas from quantum mechanics and run on conventional hardware, from genuinely transformative approaches that require actual quantum hardware. Fujitsu's digital annealer, for instance, is quantum-inspired: it delivers meaningful speed-ups for certain optimisation problems today, but it is not a quantum computer. This distinction matters enormously for setting realistic expectations.

Quantum sensing exploits the extreme sensitivity of quantum systems to external disturbances, such as magnetic fields, temperature, gravitational pull, to build measurement instruments of unprecedented precision. These devices are frequently closer to clinical deployment than quantum computers because they do not require the same scale of error-corrected qubit systems. Quantum communications and cryptography, the third pillar, leverages quantum mechanical principles to transmit information in ways that are theoretically immune to eavesdropping, with direct relevance to securing the sensitive patient data that flows through modern health systems.

Reimagining biomedical R&D and drug discovery

Drug discovery is one of the most compelling near-term applications for quantum computing in healthcare. The rate of new drug discovery has slowed dramatically since the turn of the century, a trend sometimes called Eroom’s Law, the mirror image of Moore’s Law in computing. Today, a single drug takes 10-15 years to develop and costs on average $2.6 billion. The bottleneck is not capital or intent but the sheer complexity of simulating molecular behaviour at the quantum level. This is precisely the kind of problem quantum computers are designed to tackle. The molecular interactions that determine whether a drug candidate will work, fail, or cause harm are fundamentally quantum in nature, and classical computers can only approximate them with diminishing accuracy as molecular complexity increases.

Quantum computers offer the prospect of simulating molecular interactions and protein folding with a fidelity no classical machine can match. Understanding protein folding is critical to diseases including Alzheimer’s, where misfolded proteins are a hallmark. A quantum computer capable of simulating these processes accurately could predict how a drug molecule will bind to its target protein, a challenge sometimes called Levinthal's paradox, (that finding the native folded state of a protein by a random search among all possible configurations can take an extremely long time, yet proteins can fold in seconds or less), dramatically accelerating lead-finding and reducing the cost of late-stage failures. 

The ecosystem is already making promising headway. IBM's Qiskit Nature platform enables researchers to explore protein folding and drug-molecule interactions on today’s quantum hardware, while Quantinuum's InQanto toolkit allows pharmaceutical researchers to model complex molecular systems. On a global scale, The Wellcome Leap Quantum for Bio programme (Q4Bio) is funding next-generation quantum algorithms for drug discovery and genomics.

Within the pharmaceutical industry itself, several major players are taking a lead in quantum adoption. Boehringer Ingelheim has been a pioneer, establishing a dedicated Quantum Lab and partnering with Google Quantum AI for molecular dynamics simulations, while PsiQuantum and Boehringer Ingelheim researchers have demonstrated significant speedups in calculating electronic structures relevant to drug design. Google's own quantum ambitions extend further through SandboxAQ, an Alphabet spinoff combining AI and quantum physics to accelerate drug discovery simulations, with partners including AstraZeneca and Sanofi already using its platform to de-risk drug portfolios before clinical stages. Novo Nordisk has invested broadly across the quantum stack, from evaluating optimal hardware platforms to collaborating with the Cleveland Clinic on quantum applications relevant to its core therapeutic areas in medical science and healthcare. As Dr Lara Jehi, Chief Research Information Officer at Cleveland Clinic has observed, “drug discovery is now fundamentally a software and technology undertaking, far more computational than it ever was”, recently discussed on Dr. Kris Naudt’s Time to Talk Quantum podcast. 

A critical caveat runs through all of this, which is that the most transformative applications, e.g. simulating large, complex biological molecules, will require fault-tolerant quantum computers, machines with enough error-corrected qubits to perform sustained, accurate computation. These do not yet exist at the scale needed, just yet. What exists today are noisy intermediate-scale quantum (NISQ) devices, useful for research and early benchmarking but not yet capable of clinical-grade molecular simulation. The honest framing is that quantum computing for drug discovery is more akin to a two-stage story. Firstly, near-term gains in hybrid quantum-classical workflows, and a longer-horizon transformation once fault-tolerant hardware arrives.

Early use cases in diagnostics and imaging show significant promise

Even before quantum computing reaches its potential, quantum sensing technologies have begun yielding real-world results in diagnostics. Quantum sensors can detect signals e.g. magnetic fields, photons, temperature changes, that are simply too faint for conventional instruments to register, and in medicine those faint signals often carry vital information.

One of the most clinically promising areas involves optically pumped magnetometers (OPMs), atom-based sensors that detect the extremely weak magnetic fields produced by electrical activity in the brain. Optically Pumped Magnetometer-based Magnetoencephalography (OPM-MEG) can map brain activity with high precision, with uses in epilepsy, Alzheimer’s disease, and traumatic brain injury. Unlike traditional MEG systems, which require bulky superconducting equipment cooled to near absolute zero, OPM-based systems are portable, operate at room temperature, and are far more affordable. A spin-out from the UK Quantum Technology Hub, Cerca Magnetics, has developed the world's first wearable MEG system, permitting patients, especially children who often struggle to stay still in these procedures, to move more freely during scans.

Quantum sensing is also transforming cardiac diagnostics. Neuranics is developing sensors based on quantum tunnelling, a phenomenon in which particles pass through barriers they classically should not be able to cross, to build wearables capable of measuring the heart’s magnetic field via a smartphone, an approach known as magnetocardiography (MCG). Genetesis’ CardioFlux system already offers cardiac imaging without the electrodes used in a conventional ECG. Meanwhile, diamond-based sensors containing nitrogen-vacancy (NV) centres are emerging as highly sensitive quantum sensors capable of detecting biomarkers in blood plasma and cerebrospinal fluid with exceptional speed. At finer scales, nanodiamond sensors could eventually offer nanometre-scale resolution at the level of individual cells.

The diagnostics landscape illustrates a broader principle, that quantum sensing tends to outpace quantum computing in near-term clinical relevance because the devices are smaller, less computationally demanding, and more compatible with existing healthcare workflows.

Recognising this potential, dedicated research hubs are now emerging to accelerate quantum sensing for healthcare. In the United States, the NSF Quantum Leap Challenge Institute for Quantum Sensing for Biophysics and Bioengineering (QuBBE), based at the University of Chicago, brings together researchers across medicine, physics, and engineering to create quantum sensors tailored for biomedical applications, including tracking immune cell behaviour and detecting protein misfolding linked to neurodegenerative diseases. In the UK, the Quantum Biomedical Sensing Research Hub (Q-BIOMED), led by UCL and the University of Cambridge and backed by $18 million (£24 million) in funding from UKRI and NIHR, is the first quantum research hub dedicated to healthcare, developing quantum-enhanced blood tests, brain scanners, and biosensors for earlier disease detection.

Precision medicine and genomics

Quantum technologies are also beginning to influence how medicine is personalised. Precision medicine depends on analysing enormous, complex datasets, such as genomic information, protein expression data, microbiome profiles. Quantum machine learning (QML) algorithms show early potential for extracting clinically meaningful insights from sparse but information-rich datasets in ways that classical models, optimised for large data volumes, struggle to match.

Rare diseases and women’s health are two areas where this advantage could be decisive. In conditions affecting only a small number of people, datasets are by definition limited, and quantum algorithms are theoretically better equipped to find meaningful patterns. The longer-term vision is personalised medicine at a genuinely molecular level, where quantum sensing provides a detailed understanding of an individual’s biology, while quantum computing simulates the optimal drug regimen for that individual. This represents a fundamental shift from population-level medicine to medicine that accounts for each patient’s biochemistry.

Optimising care delivery and treatment

Not all of quantum computing’s healthcare applications require simulating molecules. Healthcare systems generate enormous optimisation challenges every day, including but not limited to, scheduling patients across operating theatres, allocating clinical staff, routing urgent medical supplies, and planning radiotherapy to maximise tumour dose while minimising exposure to healthy tissue. Quantum annealing and related quantum optimisation algorithms are beginning to be applied to exactly these types of challenges.

In care delivery and continuous monitoring, quantum sensing opens new possibilities for non-invasive, real-time patient tracking. Fetal magnetocardiography (fMCG) could monitor heart rhythms of unborn babies with a precision that ultrasound alone cannot achieve. Real-time microbiome analysis, early detection of neurodegenerative diseases through continuous biosensing, and non-invasive glucose monitoring are all areas of active development demonstrating future potential.

Securing the data backbone with quantum communications

Healthcare is data-intensive, and the sensitivity of patient data makes it one of the most valuable and most targeted categories of personal information in existence. Classical encryption faces a long-term existential threat from quantum computers. This is because a sufficiently powerful quantum machine could break the cryptography standards that currently underpin digital security across healthcare and beyond. This is sometimes called the ‘harvest now, decrypt later’ threat, in which adversaries may be collecting encrypted health data today, intending to decrypt it once quantum hardware matures.

Quantum key distribution (QKD), sharing encryption keys using individual photons in a way theoretically immune to interception, provides a quantum-safe communications channel. In the UK, a working quantum-secured network spanning 410 kilometres has already been established around Bristol and Cambridge, connected via London, demonstrating seamless integration with existing infrastructure. Looking further ahead, distributed quantum computing could allow hospitals to pool quantum computational resources without centralising sensitive patient data, with implications for privacy-preserving genomic analysis and cross-institution collaboration.

The road ahead for quantum healthcare

The quantum healthcare landscape is being shaped not only by technology but by policy and investment frameworks. NVIDIA is emerging as a critical enabler of quantum-healthcare convergence. Its CUDA-Q open-source platform and NVQLink architecture provide the hybrid quantum-classical computing infrastructure that underpins much of the work in the quantum-AI domain, with recent examples include connecting quantum processors with GPU supercomputers across 17 quantum hardware builders and 9 US national laboratories. NVIDIA has also partnered with Classiq and the Tel Aviv Sourasky Medical Centre to establish a Quantum Computing for Life Sciences & Healthcare Centre focused on developing quantum algorithms for drug discovery, molecular analysis, and pharmaceutical supply chain optimisation.

Across the Atlantic in the UK, the National Quantum Computing Centre convenes researchers, industry, and NHS stakeholders around shared priorities. The Hub for Quantum Computing via Integrated and Interconnected Implementations (QCi3) brings together academic, industrial, and government expertise, while the Wellcome Leap Q4Bio programme continues to stand out with a mandate specifically targeting quantum algorithms for drug discovery and genomics.

For investors, the opportunity is real but requires careful framing of timelines. A useful distinction is between quantum-centric high-performance computing (HPC), hybrid workflows combining the best of quantum and classical computing today, and the longer-horizon prospect of fully fault-tolerant quantum systems. The former is investable now, while the latter requires patient capital.

Quantum technologies will not transform healthcare overnight. Despite this, the trajectory across all three pillars points clearly toward capabilities that will meaningfully expand what is possible in medicine: drugs discovered faster and at lower cost, diseases detected earlier and with greater accuracy, treatments personalised to individual biology, and patient data protected by mathematically provable security. The question for healthcare systems, investors, and policymakers is not whether these capabilities will arrive, but whether the institutions making decisions today are positioned to benefit when they do.