Quantum computing for materials modelling applications in photovoltaics

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Accurate simulation of complex materials yields useful insights, guiding experimental efforts and technological advancements. In photovoltaic applications, these can help to increase solar cell efficiency, their durability and manufacturability, key challenges in the industry. Designing novel materials for clean energy use, or even gaining a full understanding of existing materials, is currently a major challenge due to the necessity of taking quantum effects into account. Standard modelling techniques are unable to solve the required problems with sufficient speed and/or accuracy, implying that costly experiments in the lab are often needed in order to characterise the properties of materials.Quantum computers can natively represent quantum-mechanical systems and could efficiently solve materials modelling problems that are beyond the reach of today's best supercomputers. This could enable "in quanto" materials design and selection, where many materials are screened for their properties without needing to perform experiments. After many years of development of quantum computing technology, quantum computers have outperformed the world's fastest supercomputers for certain targeted problems. Nevertheless, the capabilities of current quantum computers are insufficient to enable standard quantum simulation algorithms to be run, and therefore the development of targeted quantum software is critical to harness the potential of existing quantum technologies.In this project we will develop efficient quantum algorithms and software to solve modelling problems in photovoltaics. Our algorithms will be targeted at specific use-cases developed in collaboration with end-users, while being sufficiently general to address other materials modelling challenges. Based on our encouraging previous results, we expect to find significant improvements on previously known algorithmic complexities, reducing the resources required to simulate quantum systems, and bringing the solution of previously unfeasible problems into reach.We will implement our quantum software on a leading quantum hardware platform, and will evaluate it against the requirements of our expert end-users Oxford PV, and also against the results of classical simulation performed by UCL. Our results will enable the development of a roadmap for future exploitation and will open the door to quantum computing solving hard materials modelling challenges beyond the capability of standard methods.