Collaborative R&D

The NQCC collaborates in R&D projects with UK businesses and research groups that aim to develop key technologies in the quantum computing value chain.

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Building the UK’s quantum computing capabilities

The NQCC is working with partners across industry, government and the research community to solve technology challenges and realise the benefits of quantum computers for social and economic development. Through our SparQ programme we engage with research institutions, companies, and government organisations to explore the potential of emerging quantum computers for tackling real-world applications.

Proof of Concept projects in Quantum Computing 2025-26

Quantum-Enhanced Clinical Decision Support for Intensive Care Medicine

Clinicians in Intensive Care Units (ICUs) face overwhelming volumes of patient data under extreme time constraints. Rapid identification of the most pressing risks and supporting evidence from disparate sources is critical to save lives and the prevention or mitigation of long-term disabilities, however, this area of intensive care is currently insufficiently supported by existing technology. This project explored the potential to use hybrid quantum‑classical risk‑event prediction models to augment existing classical models, evaluating performance across a range of longitudinal and static datasets. The project developed a model that generates clinically valuable predictions while building interdisciplinary expertise between health data science and quantum computing. The primary output was a prototype hybrid quantum‑classical model capable of supporting ICU risk prediction, with the potential for publication as a research paper. Lead Organisation: Applied Quantum Computing Limited Project Partners: University College London Evelina London Children’s Hospital and King’s College London, NQCC

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Nitrogen Doped Graphene Catalysts

Hydrogen fuel cells are a promising zero‑emission energy source with the potential to provide clean power to electric vehicles. However, uptake is currently limited due to the requirement for costly Platinum Group Metal (PGM) catalysts. This project explored the use of quantum computing to improve the performance of a low‑cost, metal‑free alternative catalyst based on nitrogen‑doped graphene. By formulating the nitrogen placement as an optimisation problem, Q‑CTRL used its Fire Opal software with IBM quantum computers to determine which graphene configurations yield high dopant densities to maximise catalyst performance. Working with Johnson Matthey, a world leader in sustainable technology solutions, the project explored the potential for quantum computers to become a key part of a future catalyst design tool. Lead Organisation: Q-CTRL UK Limited Matthey Technology Centres, NQCC

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Efficient Quantum Data Loading of Matrices with Origins in CFD

Computational fluid dynamics (CFD) simulations are essential for companies like Rolls‑Royce to design quieter, cleaner, and more efficient engines. However, industrial‑scale calculations can push even the largest supercomputers to their limits and take a long time to run. This proof‑of‑concept project, led by PsiQuantum in collaboration with Rolls‑Royce and the Hartree Centre, investigated resource‑efficient ways of loading CFD‑derived data into fault‑tolerant quantum computers, with the goal of reducing overheads and helping to unlock future quantum‑accelerated CFD workflows for aerospace and other high‑value engineering applications. Lead organisation: PsiQuantum Project Partners: Rolls-Royce PLC, STFC Hartree Centre Observers: NQCC

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Quantum-Enhanced Optimisation for Peer-to-Peer Energy Trading

This project investigated whether quantum‑inspired and quantum enhanced optimisation can overcome scalability and speed limits of classical solvers in decentralised peer‑to‑peer energy trading. Building on prior E.ON–Hartree research and recent evidence of quantum scaling advantages, the team prototyped and benchmarked hybrid classical–quantum workflows for real‑time matching of energy prosumers under complex network constraints. In collaboration with E.ON Digital Technology, NQCC, STFC Hartree, and using Qoro’s distributed orchestration platform, the work, which included validated benchmarks on IBM and AWS (hosted) hardware, defined when quantum approaches outperform classical methods, delivered integration‑ready architectures for future P2P energy systems, and provided evidence for the commercial and operational viability of quantum optimisation in future decentralised energy systems. Lead organisation: Qoro Quantum Ltd Project Partners: E.ON Digital Technology, NQCC Observers: STFC Hartree Centre

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Unit Commitment Problems in the Energy Industry: Solving with Hybrid Decomposition across Classical and Photonic Quantum Computing

This project demonstrated a potential pathway towards practical quantum advantage in the energy sector by applying a hybrid quantum–classical decomposition algorithm to the unit commitment problem—a core optimisation task for scheduling power generation at minimum cost. Motivated by the strategic need to reduce energy costs and maintain the UK’s competitiveness and attract investment, the project: – integrated Jij’s quantum‑enhanced optimisation methods with ORCA Computing’s photonic quantum computing system – rigorously benchmarked performance against simulation baselines in line with national testbed efforts at the NQCC, and – assessed the hardware and software requirements for scaling to industrially relevant problem sizes, with bp participating as industry observers. This first‑of‑its‑kind integration between Jij software and ORCA’s system enabled progress toward cost‑efficient, next‑generation optimisation tools for complex power‑system planning. Lead Organisation: Jij Europe Ltd. Project Partners: Orca, NQCC Observers: BP

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Quantum Base Alpha

QBA and the University of Edinburgh worked on Project Disinformation Dissemination, which addressed the urgent challenge of AI‑generated disinformation and image manipulation—a growing threat to Western democratic institutions. The proof‑of‑concept pioneered a hybrid approach that combines quantum machine learning with advanced classical AI, to protect media integrity and public discourse by leveraging the latest advances in both fields. Outcomes included software prototypes, quantum circuit designs, and benchmarking reports, alongside broader impacts such as improved productivity for media organisations, economic gains, stronger academic–industry partnerships, and social benefits through public empowerment and educational outreach delivered via the University of Edinburgh’s teaching and dissemination activities. Lead Organisation: Quantum Base Alpha Project Partners: University of Edinburgh/QSL, NQCC Observers: Aegiq

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Hybrid Quantum-Classical Machine Learning Algorithms for High Impact Applications in Energy and Utilities

This project developed scalable hybrid quantum‑classical machine‑learning models for two major challenges in the UK energy and utilities sector: water‑leakage detection and electricity‑demand forecasting. By combining classical deep learning with quantum computing, the project uncovered subtle patterns in complex, high‑dimensional data, leading to more accurate predictions and improved operational efficiency. Impacts included hybrid models that can help in delivering earlier and more precise leak detection to reduce water wastage and costs, improved energy forecasting to optimise grid management and support renewable integration, and broader societal benefits through sustainability. Lead Organisation: LTIMindtree UK Limited Project Partners: Yorkshire Water Services Limited, NQCC

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Quantum-enhanced processing of high-dimensional time-evolving data for applications in finance analytics

Oxford Ionics, an IonQ company specialising in quantum hardware, led a consortium with HSBC, a global financial institution, and Haiqu, a developer of quantum software, to explore the potential of quantum computing in financial services. Bringing together expertise in hardware, algorithms, and industry knowledge, the team focused on detecting anomalies in cross‑border transaction flows and supporting adaptive portfolio rebalancing as market conditions change. These efforts were aimed at strengthening financial system stability, improving risk management for banks, and fostering greater trust among public and non-profit funds. Lead Organisation: Algorithmiq UK Limited Project Partners: Rigetti UK Limited, NQCC Observers: Financial Conduct Authority

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ADOPT-TEM: Anomaly Detection OPTimisation with Tensor Error Mitigation

Anomaly Detection OPTimisation with Tensor Error Mitigation (ADOPT-TEM) project focused on developing a bespoke adaptation of a leading error mitigation method, Algorithmiq TEM on a Rigetti quantum computer. The key objective of the project was to improve the hardware performance of a hybrid quantum-classical fraud detection method developed by Rigetti. Such improvements in accuracy offers a strong motivation for quantum computing adoption in the banking and digital payment industries. The work from this project is expected to accelerate the deployment of a quantum-enhanced digital payment fraud detection system in the digital economy. ADOPT-TEM provided an opportunity to test and demonstrate efficient integration of quantum hardware and software, and their extension to a hybrid quantum + HPC workflow on NQCC infrastructure. Lead Organisation: Algorithmiq UK Limited Project Partners: Rigetti UK Limited, NQCC Observers: Financial Conduct Authority

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Quantum-Accelerated Mixed-Integer Optimisation for Aircraft Loading

Aircraft cargo loading presents a computationally demanding optimisation problem involving decisions about what to load, where to stow cargo, and how to satisfy weight, balance, and structural constraints while maximising capacity and meeting tight turnaround schedules. The problem scales exponentially with size, limiting the effectiveness of classical optimisation. In response, 4colors led a consortium with Airbus, ORCA Computing, DNV, and the NQCC to explore hybrid classical–quantum algorithms that accelerate key stages of the optimisation process. The project implemented and tested these algorithms on real quantum hardware (both gate-based and photonic), benchmarked performance against classical baselines, and assessed the pathway toward practical quantum advantage for logistics optimisation. Lead Organisation: 4colors Limited Project Partners: Airbus Operation Limited, ORCA Computing Ltd, NQCC Observers: DVN

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QTA-Foil (Quantum and Tensor Approaches to aeroFoil design)

Quantum computing has the potential to deliver transformational benefits for industries that rely on computational fluid dynamics (CFD) problems. By bringing together a powerful combination of domain expertise and experience in GPU and QPU systems, BAE Systems, NVIDIA, NQCC, and Aegiq clarified the timescales and quantum resources required to accelerate aerodynamic and hydrodynamic design, informing future research directions. Lead Organisation: Aegiq Limited Project Partners: BAE, NQCC Observers: NVIDIA

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Stochastic Quantum Neural Operators (SQNOs): Harnessing NISQ Noise for Biomedical PDE Forecasting

This project developed Stochastic Quantum Neural Operators (SQNOs), a new quantum machine‑learning framework that harnesses NISQ hardware as a functional resource to improve uncertainty modelling, privacy, and generalisation when forecasting complex PDE‑driven systems such as tumour growth. Building on baselines of classical and deterministic neural operators, the project delivered a working SQNO prototype, benchmarking studies, and formal analysis of hardware‑intrinsic privacy. Beyond biomedical forecasting, the project provided a foundational dual‑use capability, enabling future adaptation of SQNOs for defence and security applications, particularly orbital asset threat monitoring, where uncertainty-aware and privacy-preserving modelling of stochastic dynamical systems is essential. Lead Organisation: Zaiku Group Ltd Project Partners: QSL, NQCC

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Quantum Physics Informed Neural Networks for Derivative Pricing

OQC and Citi explored how quantum computing and AI could advance one of finance’s toughest challenges: fast and accurate derivative pricing. Citi brought deep expertise in financial modelling and real market use cases, while OQC contributed leading quantum hardware and quantum-AI research capabilities. Together, they developed a proof of concept demonstrating how quantum-classical hybrids can strengthen risk prediction and support more resilient financial systems. Lead Organisation: Oxford Quantum Circuits Project Partners: Citigroup, QSL and NQCC

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Risk-Aware Inventory Management in Warehouses

This project developed quantum and hybrid optimisation methods for warehouse inventory allocation, that balance space utilisation, retrieval efficiency, and operational risk. Insurance costs in warehouses are driven by spatially non-uniform hazards such as flood entry points, sprinkler coverage, and seismic vulnerability. By formulating this as a QUBO problem, the project benchmarks classical, quantum‑annealing, and hybrid approaches at scale. The work provides insight into the potential limitations and current utility of quantum methods for real-world logistics optimisation. Lead Organisation: Aioi R&D Lab Limited Project Partners: NQCC

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Proof of Concept projects in Quantum Computing 2024-25

Understanding the role of mid-circuit measurement in quantum algorithms for Computational Fluid Dynamics (CFD) simulations, towards demonstrating quantum advantage in aerodynamics

This project will assess the feasibility of quantum simulations of airfoil designs and vehicle aerodynamics on near-term quantum hardware. The work conducted in this partnership aims to deliver a roadmap towards powerful Computational Fluid Dynamics (CFD) simulations on quantum hardware for the aerospace industry.

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Proof of Concept Application of Quantum Optimisation in Planning of In-Field Sensor Networks for supporting Disaster Response Operations

The project is focused on exploring the optimal location and configuration of radio communication and sensing devices across an area of land to maximise usability, connectivity and sensor coverage.

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Investigating the application of Quantum Machine Learning (“QML”) to improve the early detection of cancer via the use of AI classification methods for liquid biopsies

The project is focused on the exploration of Quantum Machine Learning (QML) methods to improve and create novel faster and cheaper cancer diagnosis using liquid biopsies. The project aims to demonstrate a real world first step in clinical pathways towards exploring novel QML applications for multimodal approaches in healthcare.

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From batteries to windfarms: Quantum optimisation solutions for the renewable sector

This project aims to answer the question of whether quantum computers will be able to help with the emerging computational challenges of the renewable sector.

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QuantiCo – Quantum Computing for Corrosion

The project’s aim is to develop and implement the state-of-the-art molecular embedding methodologies, essential to explaining corrosion chemistry, critical to aerospace and energy sectors. The overall goal is to be able to determine accurate molecular energies, thereby predicting precise kinetic parameters for finite element models to explain the challenging ‘Oxygen reduction reaction’ on ‘Aluminium alloy’ surfaces, benefiting both industrial applications and academic research.

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Quantum Machine Learning for Fraud Detection in Credit Card Transactions

This project aims to investigate the use of Quantum Computing for enhancing anomaly detection in financial transactions, focusing on fraud detection and anti-money laundering. By leveraging quantum computing’s ability to analyse complex datasets and identify subtle patterns, the study aims to reduce false positives, improve operational efficiency, and provide robust solutions for regulatory compliance and market stability in the financial sector.

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Quantum Resource Estimation as a Pipeline for Polymer Simulation (QREPPS)

In this work programme, the consortium aims to use quantum resource estimation as a tool for assessing Quantum Phase Estimation (QPE) as a viable method for calculating ground-state energies of classically intractable polymers with high commercial value in the fields of energy, transport, and defence on early-stage fault-tolerant systems.

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Space-Hardened Quantum Error Correction for Orbital Computing

The project aims to develop Quantum Error Correction (QEC) methods specifically tailored for orbital computing. The project will focus on understanding and mitigating the impact of space radiation on entire quantum computing systems.

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Closure Optimisation for Road Network Maintenance

The project aims to leverage quantum computing to minimise disruptions caused by maintenance activities in transport networks. The study will focus on optimising the scheduling of maintenance actions to reduce the overall number of closures and/or the cost while increasing the work completed during each closure.

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Quantum Approach for Scalable Dynamic Flexible Job Shop Scheduling Platform

This project aims to leverage quantum computing to enhance efficient resource utilisation in manufacturing. It will focus on optimising the allocation of personnel, machinery, and materials across multiple projects, with the goal of reducing costs, energy consumption and carbon emissions.

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Quantum simulations for enhanced NMR in next-generation batteries

This study aims to provide guidance to the quantum resources required to perform NMR simulations for practical models of batteries on future quantum computers.

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Omics Insights with Quantum (OmIQ)

This collaboration aims to study the feasibility of employing quantum computing to better perform multimodal cancer data analysis.

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Highlight Call on Quantum Computing:

Proof of Concept projects 2022-23

Near-term quantum computing techniques to address operational healthcare use-cases

The project focussed on improving the efficiency of a variety of operational challenges in NHS healthcare provision.

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Data-driven reactivity prediction using computed quantum features for drug discovery

The project investigated a general approach where rich quantum-derived features can be used for downstream modelling without a significant understanding of reaction mechanisms.

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Quantum Monte Carlo radiation transport simulation

The project investigated the advantage offered by QC to make the runtime of Monte Carlo methods competitive with deterministic methods.

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Federated quantum machine learning for genomics data

The project investigated an FL solution implementation suitable for the NISQ hardware solutions with a particular focus on genomics datasets.

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Highlight Call on Quantum Computing:

Proof of Concept projects 2021-22

Automated control software to tune, stabilise and optimise qubits

The project investigated possible routes for creating an automated control software system to tune, stabilise and optimise qubits.

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Towards a quantum materials genome programme

The project paved the way for an accessible database of quantum tools for modelling key materials.

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Integrated photonics for ion trap quantum computing

The project explored the potential of thin-film lithium niobate as a future-proof material for photonics infrastructure in trapped-ion QC.

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Photonic quantum time-bin processing for image recognition

The project aimed to use software simulation of ORCA’s hardware environment to model image recognition problems on an ORCA PT-series system.

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Commercialising Quantum Technologies:

Feasibility Studies Call 2021-22

 

Realistic machine learning based ultra-fast simulator for semiconductor spin qubit devices

The project aimed to deliver an ultrafast simulator for semiconductor spin-qubit devices based on developments in classical M/L, quantum S/W and H/W.

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M-PIT: Miniature packaged ion traps

The project investigated to develop a high-performance, miniature and self-contained ion-trap system.

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INTERCOM: A high-performance ion-photon interface for multi-core trapped-ion QC

The project explored a device to enable quantum information to be transferred through photonic links between remote ion traps providing a scalable architecture.

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