Healthcare and life sciences are under intense pressure. Populations are ageing, chronic diseases are rising, cyberattacks on hospitals are increasing and clinicians are burning out. At the same time, patients expect faster, more personalized and more secure care.
Classical digital technologies—cloud, AI, IoT, high‑performance computing—have already transformed much of the sector, from electronic health records (EHRs) to telemedicine and AI‑assisted diagnostics. Yet they are hitting real limits:
- Drug discovery still takes over a decade and can cost billions.
- Many diseases are diagnosed too late because current sensors cannot see the earliest signals.
- Optimization problems in hospitals, payer–provider networks and life‑science supply chains remain intractable at full scale.
- The cybersecurity model that protects patient data today will be broken by future quantum computers.
A new wave of quantum technologies is emerging that speaks directly to these bottlenecks. A recent white paper from the World Economic Forum and Accenture, Quantum Technologies: Strategic Imperatives for Health and Healthcare Leaders (December 2025), argues that the question is no longer if quantum will matter for health, but how fast organizations will move to operationalize it.
For IoTWorlds readers—who sit at the intersection of sensors, networks, AI and real‑world operations—quantum health is not a distant curiosity. It is a strategic technology stack that will influence how medical IoT devices are designed, how data flows through connected health systems and how cyber‑physical infrastructure is secured.
This guide translates the white paper’s insights into an action‑oriented playbook for:
- Life‑science creators (pharma, biotech, diagnostics, med‑tech start‑ups)
- Healthcare deliverers (hospitals, clinics, device makers, payer–provider networks)
- Ecosystem enablers (regulators, standards bodies, cloud and telecom providers, funders)
We will cover:
- The three pillars of quantum technology in health
- Four value pillars where quantum changes the game
- Detailed use cases and maturity levels for creators and deliverers
- The critical role of enablers: standards, infrastructure, security and workforce
- Strategic actions and roadmaps for innovators in the IoT and healthcare space
1. Quantum Technologies: The Next Layer in Digital Health
Quantum technology is not a single tool. It is a family of technologies that exploit the physics of the very small—superposition, entanglement and quantum measurement—to perform tasks that are impossible or impractical with classical systems.
The Forum paper highlights three domains that matter most for health and healthcare.
1.1 Quantum computing
Quantum computers manipulate qubits instead of classical bits. Thanks to superposition and entanglement, a quantum computer can explore many possible configurations in parallel. For some problem types, this enables exponential speed‑ups or completely new algorithmic approaches.
In health and life sciences, promising areas include:
- Quantum chemistry and molecular simulation – modelling drugs, proteins, RNA and complex biological systems with higher accuracy than is feasible today.
- Combinatorial optimization – optimizing clinical trial design, cohort selection, hospital schedules or supply chains.
- Machine learning and hybrid algorithms – quantum‑enhanced models that work alongside classical AI to identify biomarkers or classify complex medical patterns.
Quantum computers are still in the noisy, early‑hardware era, but hybrid quantum–classical workflows are already being tested on real biomedical problems.
1.2 Quantum sensing
Quantum sensors use delicate quantum states to detect extremely small changes in physical quantities such as:
- magnetic and electric fields
- time and frequency
- acceleration, rotation and gravity
- pressure and temperature
Because they operate near the fundamental limits set by physics, they can achieve orders‑of‑magnitude improvements in sensitivity over classical sensors.
In healthcare this enables:
- Non‑invasive neural and cardiac imaging by picking up faint bioelectric or biomagnetic fields
- Early disease detection, where subtle signals appear long before anatomical changes
- High‑precision process control in biomanufacturing and pharmaceutical production
Quantum sensing dovetails naturally with IoT: think of it as super‑sensing nodes feeding into digital twins, analytics platforms and AI copilots.
1.3 Quantum communication
Quantum communication uses quantum states—often single photons—to establish secure keys or even perform distributed computing tasks.
Two sub‑technologies matter for healthcare:
- Quantum Key Distribution (QKD) – enables two parties to generate a shared encryption key with security guaranteed by physics. Any eavesdropping attempt disturbs the quantum states and is detectable.
- Quantum Random Number Generators (QRNGs) – produce high‑quality randomness that underpins strong cryptography and secure simulations.
Combined with post‑quantum cryptography (PQC)—classical algorithms designed to resist quantum attacks—quantum communication offers a pathway to quantum‑safe health data infrastructure that protects EHRs, AI pipelines and medical IoT devices against both current and future adversaries.
2. Four Value Pillars of Quantum Healthcare
The white paper organizes the impact of quantum technologies into four value pillars. These map neatly onto the concerns of IoT and digital‑health leaders.
2.1 Discovery acceleration
Quantum computing and sensing can drastically speed up or improve:
- Target identification – understanding how biological pathways behave
- Molecular simulation – predicting binding affinities, toxicity and stability
- Candidate screening – narrowing down vast libraries of molecules or peptides
- Preclinical modelling – simulating disease progression and treatment response
The goal is to reduce the cost, time and uncertainty of bringing new therapies to market—a major pain point when each drug can cost billions to develop.
2.2 Precision diagnostics
Quantum sensing promises:
- ultra‑sensitive detection of magnetic and bioelectric fields,
- high‑contrast, low‑dose imaging,
- and measurements of biomarkers that are currently below the noise floor.
This leads to earlier, less invasive diagnostics and personalized monitoring, especially when combined with connected medical devices and cloud‑based analytics.
2.3 Operational optimization
Quantum and quantum‑inspired optimization can improve:
- operating room (OR) scheduling
- bed and staff allocation
- ambulance and patient‑transport routing
- cold‑chain management for vaccines and biologics
For large, multi‑site health systems and payer–provider networks, these are high‑value problems where even small percentage gains translate to millions of dollars and better patient outcomes.
2.4 Trusted data infrastructure
Finally, quantum communication and PQC enhance:
- confidentiality of medical records and IoT telemetry
- integrity of clinical workflows and AI inferences
- resilience of hospital networks against next‑generation cyber threats
As health systems become more software‑defined and interconnected—often across national borders—this layer becomes non‑negotiable.
3. The Health Quantum Ecosystem: Creators, Deliverers, Enablers
The Forum paper usefully segments the healthcare ecosystem into three interacting roles. This structure is helpful for clarifying where IoT and quantum intersect.
3.1 Creators: life‑science innovators
These are organizations that invent new therapies and technologies:
- pharma and biotech companies
- genetics and biomedical research institutes
- diagnostics developers and med‑tech start‑ups
They care about R&D productivity, pipeline success and scientific breakthroughs. For them, quantum is primarily a tool for discovery acceleration and high‑fidelity measurement.
3.2 Deliverers: care providers and payers
These entities deliver health services to patients:
- hospitals and clinics
- integrated payer–provider networks
- pharmacy chains and telehealth platforms
- medical‑device vendors who operate connected equipment in the field
They focus on clinical outcomes, patient experience, efficiency and safety. Quantum value shows up in:
- precision diagnostics
- treatment planning
- operations and logistics
- secure data flow between many stakeholders and devices
3.3 Enablers: regulators and infrastructure partners
This group creates the rules, rails and incentives that shape adoption:
- regulators and health authorities
- standards bodies and global agencies
- telecom operators, cloud providers and HPC centers
- public and private funders
Enablers are responsible for making sure quantum health is safe, interoperable, secure and economically viable.
4. Quantum for Creators: Reinventing Biomedical R&D
Drug and vaccine development is one of the most natural fits for quantum computing and sensing, because molecular interactions and many biological processes are intrinsically quantum in nature.
4.1 Pain points in classical R&D
Even with AI and HPC, creators face major bottlenecks:
- Molecular modelling often relies on approximations that trade off accuracy vs. tractable run times.
- Predicting toxicity, off‑target effects and real‑world biology is still extremely hard.
- Experiments to validate candidates are slow and expensive, and many fail late.
- Classical sensors in biomanufacturing and assays are hitting sensitivity ceilings.
Quantum technologies offer new routes around these walls.
4.2 Use‑case maturity levels for creators
The white paper classifies creator use cases into four readiness stages.
Stage 1: Commercial (0–2 years)
These are already in real‑world use, especially in bioprocess control and high‑throughput assays.
Examples include:
- Quantum photonic particle sensing – inline sensors that use photonics to monitor particles in bioreactors, improving yield and quality control.
- NV‑diamond biosensors – nitrogen‑vacancy centers in diamond that sense magnetic fields at the nanoscale for ultra‑sensitive biomarker assays.
For IoT professionals, these are effectively next‑gen edge sensors that can plug into existing industrial‑IoT and MES/SCADA systems.
Action points for creators
- Audit critical measurement steps in labs and plants where better sensitivity or lower false‑positive rates would create value.
- Partner with quantum‑sensor vendors to run head‑to‑head pilots against incumbent technologies.
- Track metrics like batch‑failure reduction, process variability, and cost of ownership.
Stage 2: Prototype (3–5 years)
These use cases have enterprise prototypes and strong academic backing. Hardware is mature enough for serious pilots, often in a hybrid quantum–classical mode.
Key examples:
- mRNA secondary‑structure prediction – Moderna and IBM have shown that quantum‑enhanced algorithms can compute candidate RNA folds with solution diversity similar to classical algorithms but in significantly less time. This improves the design of mRNA vaccines and therapeutics.
- Molecular dynamics simulation – start‑ups like Qubit Pharmaceuticals work with hardware providers such as PASQAL and NVIDIA to simulate complex interactions using quantum computing.
- High‑throughput ligand–protein simulations and in‑silico peptide screening – aimed at narrowing down vast search spaces faster.
- Cohort selection optimization – Cleveland Clinic and IBM explore quantum optimization for clinical‑trial recruitment, balancing many constraints.
Action points
- Identify one or two simulation‑intensive R&D problems where classical computing already strains budgets or time.
- Co‑develop pilots with quantum‑hardware and algorithm partners; use clearly defined KPIs (time‑to‑insight, diversity of solutions, predictive accuracy).
- Start discussions with regulators on how to validate quantum‑assisted results, especially when they influence trial design or regulatory filings.
Stage 3: Experimental (6–10 years)
These are technically feasible in early demos, but not yet robust or economical for production. Examples include:
- Protein folding with quantum help – beyond what today’s AI models can resolve.
- Photodynamic property modelling – simulating how light‑activated therapies behave at the molecular level.
Action points
- Join or form research consortia to de‑risk costs.
- Develop hybrid workflows where simulations can switch between HPC, AI and quantum engines.
- Train computational scientists in quantum‑classical co‑design.
Stage 4: Theoretical (10+ years)
Full‑scale predictive toxicology modelling and whole‑organism treatment simulation live here. These require fault‑tolerant quantum computers with hundreds or thousands of logical qubits.
Even though they are long‑term, early movers can:
- define research roadmaps,
- create shared data standards,
- and negotiate IP and ethics frameworks so they are ready when hardware catches up.
4.3 Case studies for creators
Moderna: quantum‑assisted mRNA design
Moderna has become synonymous with mRNA vaccines. One of its hardest problems is predicting how long RNA sequences will fold. The number of possible structures explodes combinatorially, and subtle features like pseudoknots are easily missed by approximations.
By collaborating with IBM, Moderna runs hybrid quantum–classical algorithms on IBM’s latest quantum processors. Early experiments show:
- comparable or better structural coverage than classical methods,
- diversity in candidate folds that increases the chance of finding viable drugs,
- and significant time savings—jobs that took weeks on HPC clusters can be run in hours.
Moderna’s internal benchmark for adoption is cost‑of‑ownership parity: once the total cost (infrastructure plus talent) of quantum workflows matches or beats classical HPC for certain tasks, they will scale up use. They expect this to be realistic by late this decade, when fault‑tolerant hardware with around 100 logical qubits becomes available.
Qnity: quantum‑precision molecular screening
Qnity, a start‑up spanning the US and Brazil, uses quantum‑enhanced electrodes to measure the affinity between biomolecules and targets with very high resolution. In pilots with a global pharma partner, their platform is benchmarked against industry standards like surface‑plasmon resonance.
What’s different:
- higher sensitivity, especially for small molecules,
- richer data about how molecules interact over time,
- adaptability across different classes of biomolecules.
This improves early hit validation and could later power extremely precise diagnostic assays.
5. Quantum for Deliverers: From Hospitals to Home Care
For those on the front lines—clinicians, nurses, hospital managers—quantum must prove itself in better outcomes, smoother workflows and safer data.
5.1 Deliverer use‑case maturity
As with creators, the paper maps deliverer use cases across four maturity levels.
Stage 1: Commercial (0–2 years)
These are deployed in real clinics.
- Magnetocardiography (MCG) – devices like CardioFlux measure the heart’s magnetic field, providing a new modality for diagnosing ischemia and other cardiac conditions. Centers in the US, including the West Virginia University Heart and Vascular Institute, have started using MCG alongside ECG and imaging.
- Quantum‑inspired operating‑room scheduling – hospitals such as Baptist Health South Florida pilot optimization tools from vendors like Fujitsu to increase OR utilization, reduce cancellations and shorten patient backlogs.
Action points
- Evaluate whether adding MCG or similar modalities can improve triage accuracy for chest‑pain patients or complex cardiac cases.
- Pilot quantum‑inspired schedulers in one surgical department; compare to historical utilization and overtime data.
Stage 2: Prototype (3–5 years)
Promising near‑term pilots include:
- Wearable OPM‑MEG – optically pumped magnetoencephalography helmets, used in pediatric and neurology settings for non‑invasive brain mapping. SickKids in Toronto works with vendors such as Cerca Magnetics to test this for children who cannot tolerate traditional MEG scanners.
- Bedside / portable AI‑assisted MCG – systems that bring cardiac magnetic sensing directly to the ward or emergency room.
- Quantum biomarker algorithms – hybrid quantum–classical ML models that sift through multi‑omic cancer data to identify minimal gene signatures for prognosis or therapy choice. Work led by the University of Chicago, MIT and Infleqtion is an example.
- Quantum‑secure hospital data links – pilot projects where hospitals use QKD to secure connections for imaging or EHR transfer.
Action points
- Start limited‑scope pilots in departments with clear unmet needs (pediatric neurology, cardio‑oncology).
- Embed pilots into routine workflows, with KPIs such as time‑to‑diagnosis, avoidable admissions, and confidence ratings from clinicians.
- Collaborate with telcos or national research networks for QKD pilots; compare their resilience to simulated attacks.
Stage 3: Experimental (6–10 years)
Work in this category includes:
- Hybrid quantum‑classical biomarker models for early lung cancer detection, co‑developed by Cleveland Clinic and IBM.
- Quantum neural networks for surgical risk prediction, piloted in Czech hospitals using national supercomputing centers.
- Quantum‑enhanced simulations of brain‑aneurysm fluid dynamics, aiding neurosurgical planning.
These efforts aim to transform diagnostic and treatment planning, but still require validation on large populations and better hardware.
Stage 4: Theoretical (10+ years)
Long‑term ideas include:
- Low‑dose, high‑contrast quantum biomedical imaging – potentially reducing radiation exposure in CT or PET while improving detail.
- Continuum of quantum‑secure healthcare networks – end‑to‑end quantum‑safe connections across entire regions, securing all hospital‑to‑hospital data flows.
5.2 Case studies for deliverers
Mayo Clinic: magnetocardiography for better chest‑pain triage
Mayo Clinic, working with SandboxAQ, is running an observational study of 150 patients with suspected acute coronary syndrome. The hypothesis: MCG plus AI analysis can outperform ECG alone in detecting coronary artery disease.
Benefits they’re probing:
- richer spatial maps of electrical activity,
- potentially earlier warning of ischemia,
- simpler or more comfortable workflows for patients.
Challenges include:
- proving clinical advantage and cost effectiveness in rigorous trials,
- positioning MCG among several competing measurement modalities.
Mayo imagines a near future where MCG is regularly used in cardiac clinics and ERs, and a longer‑term horizon where portable MCG devices in pharmacies or homes enable proactive monitoring.
University of Chicago & Wellcome Leap Q4Bio: quantum biomarkers
Cancer biomarkers today often require panels of hundreds of genes or thousands of features, making tests expensive and interpretively messy.
A team supported by Wellcome Leap’s Quantum for Bio (Q4Bio) programme is exploring whether quantum‑enhanced feature selection can identify much smaller gene sets—on the order of 10–35 genes—that still classify cancers effectively.
If successful, this yields:
- cheaper, faster tests,
- easier interpretation by clinicians,
- better generalization from research datasets to real‑world clinics.
Researchers are benchmarking quantum methods against best‑in‑class classical ML pipelines, and porting algorithms to near‑term hardware.
6. Quantum for Enablers: Building the Foundations
No matter how advanced the algorithms and sensors, quantum cannot scale in health without the right enablers.
6.1 Key enabler domains
The white paper highlights six:
- Regulatory bodies – define clinical validation pathways and safety rules for quantum devices and algorithms.
- Infrastructure and security – ensure quantum‑safe cryptography, resilient networks and integrated HPC + quantum resources.
- Training and workforce – cultivate quantum‑literate clinicians, data scientists and engineers.
- Funding mechanisms – align public and private capital to support pilots and scale‑up.
- Interoperability standards – evolve frameworks like FHIR to include quantum‑secure protocols and new data types.
- Joint testbeds – real‑world sandboxes where hospitals, vendors and researchers co‑develop and evaluate solutions.
6.2 Three stages of enablement
Stage 1: Establish foundations (0–2 years)
Immediate priorities:
- Adopt NIST post‑quantum cryptography candidates in health IT baselines.
- Launch first hospital‑to‑hospital QKD links for medical imaging or data backup.
- Offer healthcare‑grade cloud and HPC access to quantum systems (e.g., via IBM Quantum Network, AWS Braket, Azure Quantum).
- Run mission‑driven challenge programmes—like Wellcome Leap’s Q4Bio—to pull together interdisciplinary teams around concrete health questions.
First movers reduce cyber risk and send a strong message to innovators that the ecosystem is serious about quantum.
Stage 2: Scale across health systems (3–6 years)
Activities:
- Integrate quantum accelerators into national supercomputers, creating HPC + quantum hybrids (e.g., LUMI‑Q in Europe).
- Expand OPENQKD‑style testbeds to include more hospitals and clinical data pipelines.
- Standardize evaluation metrics for QKD and PQC implementations in procurement frameworks.
- Grow talent pipelines through national quantum information science & technology (QIST) programmes and health‑focused internships.
Stage 3: Institutionalize (7–10 years)
Longer‑term goals:
- Build continental quantum‑secure health networks, such as EuroQCI‑linked hospital grids.
- Align reimbursement, certification and procurement policies with quantum‑secure requirements.
- Create regulatory sandboxes for continuous adaptation as technologies and threats evolve.
- Establish sustainable public‑private financing models for quantum‑health infrastructure.
6.3 Enabler case studies
Wellcome Leap Q4Bio: fast‑paced global challenge
Q4Bio demonstrates how DARPA‑style challenges can rapidly advance quantum health:
- $50M+ in funding, three structured phases, strict time limits.
- Multidisciplinary teams from universities, start‑ups, big tech, labs and hospitals.
- Requirement to demonstrate algorithms on real devices within 30 months and to benchmark against the best classical methods.
The result is not just science; it is a set of end‑to‑end frameworks linking a clearly stated biological problem, data pipelines, quantum algorithms, hardware, and evaluation metrics—exactly what practitioners need to adopt the technology later.
Merck & QUTAC: pre‑competitive industrial consortium
Merck co‑founded the Quantum Technology and Application Consortium (QUTAC) with peers like BASF and Boehringer Ingelheim. The idea:
- identify high‑value industrial problems in chemistry and pharma,
- co‑develop algorithms and benchmarks,
- share IP and reduce risk in the pre‑competitive phase.
This collaborative approach strengthens regional digital sovereignty while ensuring that health‑relevant challenges are central to the quantum‑industrial agenda.
Abeer Group & Quantasphere: quantum‑safe healthcare network
Abeer Group, a large healthcare provider in Saudi Arabia, is partnering with Quantasphere to deploy quantum‑entropy engines that generate ultra‑secure keys for EHR encryption.
Phase 1:
- use a cloud‑based entropy service to protect communications across clinics and remote staff.
Phase 2:
- roll out an on‑premise engine for internal networks and sensitive mobile devices.
The group expects to halve its cybersecurity budget while getting ahead of future regulatory requirements for quantum‑safe data protection.
7. Strategic Actions for Healthcare and IoT Innovators
Pulling everything together, what should leaders actually do in the next one to ten years?
7.1 Cross‑cutting actions (all roles)
- Build coordinated partnerships
- Form joint initiatives between hospitals, pharma, quantum start‑ups, cloud providers and universities.
- Use neutral platforms—such as the World Economic Forum’s Quantum Economy Network or national innovation hubs—to anchor these collaborations.
- Participate in structured pre‑competitive programmes
- Join challenge projects, benchmark studies and working groups that define validation standards for quantum tools.
- Contribute real‑world datasets and domain expertise so that algorithms are shaped by genuine healthcare needs.
- Modernize digital foundations with quantum safety in mind
- Upgrade identity, network and device architectures to be crypto‑agile and PQC‑ready.
- Plan for secure onboarding of future QKD or quantum‑enhanced randomness sources.
7.2 Specific guidance for creators (life sciences)
- Secure C‑suite sponsorship. Quantum exploration should link explicitly to strategic scientific goals—target classes, therapeutic areas, or platform technologies—rather than being a side project.
- Integrate quantum with AI and HPC. Don’t build isolated pilots. Design workflows where classic ML, simulation and quantum algorithms can interoperate and be swapped based on cost/accuracy trade‑offs.
- Invest in quantum‑enhanced measurement. When your R&D teams control the full laboratory workflow, they can test quantum sensors, advanced readout methods and precision assays that feed higher‑quality data to downstream models.
7.3 Specific guidance for deliverers (care providers and payers)
- Implement quantum‑safe protection for clinical systems and IoT fleets. Prioritize EHR gateways, imaging pipelines, remote‑monitoring networks and third‑party links. Layer PQC now; plan QKD or QRNG adoption as infrastructure matures.
- Launch quantum‑inspired optimization pilots. Start with OR scheduling, bed management or staffing—areas where small percentage gains have large financial and human impacts.
- Evaluate quantum sensing in procurement. Treat technologies like OPM‑MEG, MCG, or quantum‑enhanced imaging as candidates in the same way you assess new MRI or CT systems—through cost‑effectiveness, workflow integration and clinical outcomes.
7.4 Specific guidance for enablers (regulators, funders, infrastructure)
- Embed post‑quantum cryptography into standards and regulations. Follow NIST guidance and adapt frameworks such as FHIR or ISO/IEC security norms to include PQC readiness.
- Publish migration roadmaps and guidance—for example, timelines for medical‑device manufacturers to support crypto agility across the product life cycle.
- Co‑fund quantum‑health networks and testbeds. Extend initiatives such as EuroQCI, OPENQKD or national research networks to include hospitals, insurers and labs.
- Scale talent programmes. Sponsor joint degrees, fellowships and on‑the‑job training that combine quantum, AI, cybersecurity and biomedicine.
8. Practical Roadmap for IoT‑Centric Organizations
Because IoTWorlds focuses on connected systems, here is a consolidated, IoT‑specific roadmap.
Short term (0–2 years)
- Inventory cryptographic dependencies across your device fleet, gateways and cloud services. Start planning PQC upgrades.
- Identify sensing bottlenecks—places where existing sensors lack sensitivity. Monitor quantum‑sensor vendors and, where feasible, run lab pilots.
- Build data pipelines that are quantum‑ready: structured, well‑labeled, and accessible through APIs that agents and tools (including future quantum models) can call.
- Experiment with GPT‑class models as orchestration layers that can, in the future, coordinate quantum tools alongside classical ones.
Medium term (3–6 years)
- Integrate quantum‑inspired optimization for asset scheduling and logistics.
- Adopt quantum‑safe networking for critical IoT paths—telemetry from intensive‑care devices, connected imaging, or remote surgeries.
- Participate in testbeds that connect hospital or industrial IoT sites to national quantum‑communication or HPC–quantum infrastructures.
Long term (7–10 years)
- Shift from pilots to platforms: treat quantum computing and sensing as standard services in your digital‑twin and analytics architectures.
- Continuously refresh security as both quantum hardware and adversarial capabilities evolve.
- Collaborate in international governance to ensure cross‑border IoT health data flows remain secure, interoperable and ethical.
9. Conclusion: From Potential to Practice
Quantum technologies promise to reshape the entire healthcare value chain—from the molecules we design to the way we detect disease, schedule surgeries, secure telehealth sessions and manage connected devices.
The 2025 World Economic Forum white paper makes one message clear: waiting for a mythical “tipping point” is risky. By the time quantum hardware is obviously superior for certain tasks, the organizations that have been quietly building expertise, partnerships and infrastructure will be far ahead.
For creators, this is a chance to redefine R&D economics and therapeutic frontiers. For deliverers, it is an opportunity to offer more precise, preventive and efficient care. For enablers, it is a responsibility to steer standards, security and funding so that quantum’s power is harnessed safely and equitably.
For the IoT and edge‑AI community, quantum health is especially relevant:
- Quantum sensors will become the most sensitive nodes in future medical IoT networks.
- Quantum‑safe communication will underpin trust in connected devices and remote care.
- Quantum computing will run alongside AI and digital twins to optimize complex, cyber‑physical systems.
Now is the time to map where quantum fits into your roadmap, build the right collaborations and begin the learning journey. Organizations that embrace this path today will be best positioned to deliver the intelligent, secure and patient‑centric healthcare systems that the coming decade demands.
