Innovate UK, under the Small Business Research Initiative (SBRI), has awarded financial support to Vorsus, an SME based in Oxford, which operates at the forefront of quantum computing, specialising in quantum-enabled application development. Vorsus has a team experienced physicists, computational scientists, engineers and business leaders who are harnessing the power of quantum mechanics to solve complex problems, unlocking unprecedented possibilities in optimisation, quantum simulations, and Quantum Machine Learning. The funding from Innovate UK is for a study and research into using the emerging power of quantum computing to enhance public health by creating new medicines to diseases with no known cure. It also aims to reduce government healthcare expenses by optimising the production efficiency of existing treatments. Recombinant protein drugs (RPDs) are medications created using genetic engineering techniques, offering advantages over small molecule drugs due to their high precision and targeted nature. These drugs are particularly difficult to discover and develop but are already transforming the treatment of some chronic medical conditions, such as diabetes and some cancers. Vorsus aims to develop a highly impactful quantum-enabled RPD discovery pipeline toolkit which will operate on second-generation quantum computing hardware. Based on the outcome of the feasibility study, further funds may be allocated to Vorsus to develop the first version of the toolkit for licensing to research-based pharmaceutical companies.

Innovate UK, under the Small Business Research Initiative (SBRI), has awarded financial support to Vorsus, an SME based in Oxford, which operates at the forefront of quantum computing, specialising in quantum-enabled application development. Vorsus has a team experienced physicists, computational scientists, engineers and business leaders who are harnessing the power of quantum mechanics to solve complex problems, unlocking unprecedented possibilities in optimisation, quantum simulations, and Quantum Machine Learning. The funding from Innovate UK is for a study and research into using the emerging power of quantum computing to enhance public health by creating new medicines to diseases with no known cure. It also aims to reduce government healthcare expenses by optimising the production efficiency of existing treatments. Recombinant protein drugs (RPDs) are medications created using genetic engineering techniques, offering advantages over small molecule drugs due to their high precision and targeted nature. These drugs are particularly difficult to discover and develop but are already transforming the treatment of some chronic medical conditions, such as diabetes and some cancers. Vorsus aims to develop a highly impactful quantum-enabled RPD discovery pipeline toolkit which will operate on second-generation quantum computing hardware. Based on the outcome of the feasibility study, further funds may be allocated to Vorsus to develop the first version of the toolkit for licensing to research-based pharmaceutical companies.

Innovate UK, under the Small Business Research Initiative (SBRI), has awarded financial support to Vorsus, an SME based in Oxford, which operates at the forefront of quantum computing, specialising in quantum-enabled application development. Vorsus has a team experienced physicists, computational scientists, engineers and business leaders who are harnessing the power of quantum mechanics to solve complex problems, unlocking unprecedented possibilities in optimisation, quantum simulations, and Quantum Machine Learning. The funding from Innovate UK is for a study and research into using the emerging power of quantum computing to enhance public health by creating new medicines to diseases with no known cure. It also aims to reduce government healthcare expenses by optimising the production efficiency of existing treatments. Recombinant protein drugs (RPDs) are medications created using genetic engineering techniques, offering advantages over small molecule drugs due to their high precision and targeted nature. These drugs are particularly difficult to discover and develop but are already transforming the treatment of some chronic medical conditions, such as diabetes and some cancers. Vorsus aims to develop a highly impactful quantum-enabled RPD discovery pipeline toolkit which will operate on second-generation quantum computing hardware. Based on the outcome of the feasibility study, further funds may be allocated to Vorsus to develop the first version of the toolkit for licensing to research-based pharmaceutical companies.

Fairness, trust, and transparency are the primary concerns hindering the wider adoption of AI in critical application domains. AI systems have been found to produce unfair, biased, and unethical decisions in many instances. Explainability is one way to understand the reasons behind a model's predictions can help ensure models are treating all users fairly. The field of Explainable AI (XAI) is focused on developing tools, frameworks, and methods that help understand how machine learning models make decisions. Topological Data Analysis (TDA) is an nascent field of data science that aims at characterizing data using its shape. It is one of the enabling technologies for Explainable AI. TDA extracts topological features to capture multi-scale, global, and intrinsic properties of complex data sets. It has applications across all the industry verticals ranging from finance, biology, neuroscience, computer vision and text analytics. Persistent Homology (PH), the workhorse of TDA, is a useful way to summarise the topological characteristics of noisy and high dimensional datasets as an intuitive, interpretable and lower dimensional representation. However, the use of TDA/Persistent Homology in commercial applications is often hindered by the computational complexity required to compute topological descriptors exactly. Our project will study the implementation of persistent homology techniques suitable for near-term quantum hardware in order to establish commercial deployment roadmap aligned with hardware developments. We take advantage of recent advancements in quantum computing algorithms and hardware availability to extract topological features from high dimensional big datasets.