Finding new safe and effective medicines is more challenging than ever. A significant shift in the drug development process utilising new methods and technologies is required. Project partners GTN and Rahko are addressing these problems with disruptive technology, incorporating ideas from quantum physics and machine learning, that will increase the efficiency of the drug development process, decreasing the time and cost it takes to develop new small-molecule drugs. The goal is to develop and utilise the partners' patented technology platforms to radically improve the likelihood of discovering novel medicines for untreated diseases. This has the potential to re-invigorate work in rare, neglected and hard to treat diseases, impacting a huge number of lives globally.

Quantum technology -- mapping and map integration for buried assets (QT-MIBA) seeks to evaluate the feasibility of obtaining and publishing more complete and accurate information on the location of buried assets through enhanced processing of geophysical sensor data. The goal of QT-MIBA is to address the accidental strikes on underground utility pipes and cables that cost the country £1.2bn a year as well as reducing the traffic delays caused by utility streetworks estimated as 6.16 million days of work lost between 2014-2015\. It will also prevent incidents of workers accidentally hitting gas and electric pipes and thereby endangering their lives and interrupting supply of services to customers. QT-MIBA represents a major collaboration between Great Britain's national mapping agency and world-leading geospatial authority, an asset owner, a survey company, a data processing SME and an academic partner leading the application of quantum technology sensors for civil engineering applications. The project aligns with quantum technology sensor development, by providing a roadmap and value assessment of the data to end users. It also supports the initiative promoted by the Geospatial Commission to bring together existing data on underground infrastructure currently held by individual organisations (both privatised and non-privatised) to create a National Underground Asset Register (NUAR). OS and NWL currently collaborate on a pilot project in the North East to explore how accurate geospatial data can reduce the likelihood of utility strikes, improve underground infrastructure maintenance and inform new-build development projects. While bringing together existing buried infrastructure data is a significant step forward, there are many questions about the quality of this existing data, including omissions. There is, then, a role for data derived from geophysical surveys to update statutory record data. QT-MIBA will deliver a feasibility study to assess how data from QT, combined with data from traditional geophysical sensors, can be enhanced using novel processing techniques including Artificial Intelligence, deep learning and quantum machine learning. Moreover, it will develop protocols which will enable survey data collected at disparate locations across the network to be integrated into geospatial maps. This will enable an assessment of the value of enhancing the positional accuracy of buried asset records without the need to wait until they are dug up for maintenance.

QUANTIFI aims to develop a world-leading Quantum Computing Dynamical Mean Field Theory (DMFT) solution for strongly correlated catalytic materials. DMFT is needed to properly describe a large number of important transition metal oxides used as catalytic materials for emissions reductions as well as oxides for batteries and other applications. On conventional computers DMFT is restricted to very small systems due to the prohibitive computational cost. Quantum computers are expected to lead to exponentially large speedups, making currently unfeasible calculations feasible. We will bring the resulting quantum software product to the market and integrate it in cloud services. This will enable the UK to maintain its world leading position in the quantum materials software market with the advent of quantum computers (QCs).This will be achieved through the development of a framework based on quantum algorithms that interfaces with a QC to solve the electronic structure problem using DMFT. The vision directly relates to the overall need of the chemicals/materials sector for accurate, rapid modelling solutions, overcoming existing limitations that prevent accurate modelling of materials, reducing the need for lengthy, expensive lab trials. Application of the solution to the materials sector will enable faster discovery of new materials, new economies and new (patentable) discoveries.The technology will be innovative in a number of clear ways, in particular this will demonstrate the feasibility of using quantum computing to accelerate materials modelling and discovery, including:\* Use of a Variational Quantum Eigensolver (VQE) for ground and excited states within an exact diagonalization (ED) DMFT approach.\* Quantum Machine Learning algorithms for noise reduction and error mitigation.\* Use of quantum DMFT solvers on currently available and near-term ('NISQ') QCs for real materials of industrial relevance. These are expected to be able to solve systems, where state-of-the-art classical methods fail due to the exponential growth of computational times.QUANTIFI is innovative in that we use a Variational Quantum Eigensolver (VQE) for ground and excited states within an exact diagonalization (ED) DMFT approach to demonstrate the feasibility of quantum DMFT solvers on currently available and near-term ('NISQ') QCs for industrially relevant materials. The work is supported by NPL and KCL, world-leading experts in DMFT.QUANTIFI, therefore, has potential high impact in catalysis and hence a large product relevance for many of the UKs chemistry manufacturers, materials designers, and pharmaceutical companies.By achieving this, it is estimated that the consortium and wider supply chain will achieve significant benefits.