Public Funding for Capgemini UK PLC

**Q-ACES: Advances in Chemical Energy Storage with Quantum Computing** Q-ACES is an innovative project dedicated to propelling energy storage into a new era through the power of quantum computing. With an increasing demand for eco-friendly, cost-effective rechargeable batteries with high energy density, the need for advancements in this field has never been more pressing. While lithium-ion batteries have dominated the market due to their energy density and competitive pricing, there is a clear call for superior battery technology. Q-ACES recognizes the importance of achieving higher energy density, extended lifetimes, faster recharge rates, and reduced costs, all of which contribute significantly to global decarbonization efforts. To meet these objectives, research into new materials as well as the composition of electrolytes, which play a critical role in battery performance and stability, are necessary to get a deeper understanding of molecular interactions. Our project centers on harnessing the potential of quantum computing to unravel these molecular interactions within electrolyte chemistry. By leveraging quantum algorithms on state-of-the-art quantum computers, we focus on analyzing key sub-components, such as reaction centers, rather than attempting to simulate the intricate many-body Hamiltonian of the entire system. We will further develop a novel approach to combine quantum calculations of embedded sub-components with data-driven modelling. In doing so, we envision quantum hardware for chemical simulation and experimental data to be leveraged together for practical utility. This pragmatic approach paves the way for enhanced battery designs. Q-ACES is driving progress towards a future where energy storage systems achieve unprecedented levels of efficiency and performance. By integrating quantum computing, we aim to overcome the limitations of traditional battery research and unlock the true potential of energy storage. Our mission is deeply rooted in sustainability and innovation, envisioning a world where rechargeable batteries play a vital role in powering diverse industries while minimizing environmental impact.

**Q-ACES: Advances in Chemical Energy Storage with Quantum Computing** Q-ACES is an innovative project dedicated to propelling energy storage into a new era through the power of quantum computing. With an increasing demand for eco-friendly, cost-effective rechargeable batteries with high energy density, the need for advancements in this field has never been more pressing. While lithium-ion batteries have dominated the market due to their energy density and competitive pricing, there is a clear call for superior battery technology. Q-ACES recognizes the importance of achieving higher energy density, extended lifetimes, faster recharge rates, and reduced costs, all of which contribute significantly to global decarbonization efforts. To meet these objectives, research into new materials as well as the composition of electrolytes, which play a critical role in battery performance and stability, are necessary to get a deeper understanding of molecular interactions. Our project centers on harnessing the potential of quantum computing to unravel these molecular interactions within electrolyte chemistry. By leveraging quantum algorithms on state-of-the-art quantum computers, we focus on analyzing key sub-components, such as reaction centers, rather than attempting to simulate the intricate many-body Hamiltonian of the entire system. We will further develop a novel approach to combine quantum calculations of embedded sub-components with data-driven modelling. In doing so, we envision quantum hardware for chemical simulation and experimental data to be leveraged together for practical utility. This pragmatic approach paves the way for enhanced battery designs. Q-ACES is driving progress towards a future where energy storage systems achieve unprecedented levels of efficiency and performance. By integrating quantum computing, we aim to overcome the limitations of traditional battery research and unlock the true potential of energy storage. Our mission is deeply rooted in sustainability and innovation, envisioning a world where rechargeable batteries play a vital role in powering diverse industries while minimizing environmental impact.

Next Wing will develop a series of Scalable Models for Aircraft Robust Trades (SMART) models and enabling modelling and simulation capabilities, which will be integrated and subsequently demonstrated at wing level. These are critical enablers to shorten the aircraft Product development cycle and develop products that will deliver on Airbus' sustainability ambitions. The project will deliver: * Wing-level SMART models for components and zonal integration areas * Development of Model Based Systems Engineering-based design environment for SMART model integration * Integration and V&V of models in the design environment via use cases * Development of a collaborative co-development approach * Development of a wing Product line approach The partners collaborating in the delivery of this ambitious project are: Airbus Operations Ltd, Capgemini UK Plc, Daptablade Ltd, Imperial College London, Loughborough University, Queen Mary University London, University of Exeter, University of Manchester, and University of Sheffield.

Next Wing will develop a series of Scalable Models for Aircraft Robust Trades (SMART) models and enabling modelling and simulation capabilities, which will be integrated and subsequently demonstrated at wing level. These are critical enablers to shorten the aircraft Product development cycle and develop products that will deliver on Airbus' sustainability ambitions. The project will deliver: * Wing-level SMART models for components and zonal integration areas * Development of Model Based Systems Engineering-based design environment for SMART model integration * Integration and V&V of models in the design environment via use cases * Development of a collaborative co-development approach * Development of a wing Product line approach The partners collaborating in the delivery of this ambitious project are: Airbus Operations Ltd, Capgemini UK Plc, Daptablade Ltd, Imperial College London, Loughborough University, Queen Mary University London, University of Exeter, University of Manchester, and University of Sheffield.

IMPASA will reduce the cost of _development and testing_ of new safety critical aerospace software through the two new technologies for verification and automatic review of software source code. IMPASA supports the continued safety of air travel as airborne software continues to grow in complexity and importance.

Digital Assembly for Wing (DAWN) will develop and innovatively apply digital solutions and software tools to demonstrate end-to-end digitalisation for a high production rate aircraft wing assembly and systems installation manufacturing system utilising the Wing of Tomorrow demonstrator wingboxes. The aim is to improve process control, reduce the amount of concessions and inspections, and reduce cycle time by connecting the supply chain, operational workers and support functions to manufacturing assets and assembly processes. Airbus will utilise the results of DAWN to enable a Digital Smart Factory_._

HICLASS is a project to enable the delivery of the most complex software-intensive, safe and cyber-secure systems in the world. It is a strategic initiative to drive new technologies and best-practice throughout the UK aerospace supply chain, enabling the UK to affordably develop systems for the growing aircraft and avionics market expected over the next decades. It includes key primes, system suppliers, software companies and universities working together to meet the challenge of growing system complexity and size. HICLASS will allow development of new, complex, intelligent and internet-connected electronic products, safe and secure from cyber-attack that can be affordably certified.

Legal Business Contracts govern the business relationship between trading business partners. They are like blueprints of expected business behaviour of all the contracting parties involved, and bind the parties to obligations that must be fulfilled by expected performance events. This highly innovative project proposes the automation of the obligation extraction task using artificial intelligence, especially machine learning and natural language processing. Since the extracted information will be already in machine readable format we also propose the development of software the implements the workflows that have to do with obligation management(e.g.payment calendars, reporting notifications,etc.). The solution, accessed directly or as a service, will help legal, commercial and compliance professionals to accelerate contract review and analysis as well as avoid manual data entry into corporate systems, allowing them to focus on higher-value tasks. It will generate significant cost savings (50-90%) through the reduction of the time spend on manual tasks. On top of that, we expect significant operational risk reduction which will lead to reduced costs of litigation and potential penalties.