PhD - Recalibrating Quantum Advantage for the Simulation of Chemical Reactions in London

A funded 4-year joint PhD between King's College London and the National Physical Laboratory is available for methodological developments into quantum algorithms for realistic electronic structure. Quantum computers are on their way, and one of the most impactful application areas they are expected to disrupt is in the simulation of electronic structure for molecular and materials modelling.

In a collaboration between Prof. George Booth at King's College London (KCL), and Dr. Yannic Rath at the National Physical Laboratory (NPL), this ambitious PhD project will work at the intersection of classical and quantum algorithm development, to devise scalable and robust quantum algorithms for the simulation of chemistry and physical processes over reactive timescales. In particular, we will consider a grand challenge of simulating photo-induced reactivity of molecular processes, leveraging quantum devices and data-driven inference for the end-to-end simulation of non-adiabatic wave function dynamics over realistic timescales.

This will build on recent advances in a novel data-efficient classical interpolation scheme which acts directly on quantum variables, to allow for noise-resilience and boosting of the accessible time scales of the quantum simulation. By interpolating smooth electronic features from sparse quantum data, we aim to leverage both quantum and classical resources to reduce quantum computational demands by many orders of magnitude.

The candidate will be embedded in an active research environment dedicated to developing novel numerical approaches bridging the gap between the promise and practical deployment of quantum computing for simulating quantum many-body systems at both KCL, and as part of NPL's Quantum Software and Modelling team at its Teddington campus. The role will involve regular periods working at both London locations. Excellent opportunities for interaction exist within the UK quantum computing ecosystem, including the QCi3 hub, as well as London's established materials modelling community, such as the Thomas Young Centre and the Materials and Molecular Modelling Hub. The collaboration also benefits from strong industrial ties with leading quantum computing and data-driven chemistry companies.

The project will challenge the candidate to develop and apply both quantum and data-driven classical algorithms for many-body physics and molecular simulation, and connect quantum software design with industrial application, equipping the student with a combination of expertise in these different domains. The candidates should have a keen interest in computational methods for modelling quantum many-body systems from a physics or chemistry perspective, experience in scientific programming, as well as being keen to work in a team based in London. Knowledge of quantum information processing is also desirable but not essential.

Regular training, conference attendance, professional development and opportunities for project dissemination will be expected. Applicants are expected to have a masters-level degree, or are awaiting its award, in a relevant area (Theoretical/Computational Physics or Chemistry).

For more details on the project feel free to contact George Booth and Yannic Rath with a brief CV for informal discussions. Start date: October 2026.

The National Physical Laboratory (NPL) is a world-leading centre of excellence that provides cutting-edge measurement science, engineering and technology to underpin prosperity and quality of life in the UK. NPL and DSIT have strong commitments to diversity and equality of opportunity, and welcome applications from candidates irrespective of their background, gender, race, sexual orientation, religion, or age, providing they meet the required criteria. Applications from women, disabled and black, Asian and minority ethnic candidates in particular are encouraged. All disabled candidates who satisfy the minimum criteria for the role will be guaranteed an interview under the Disability Confident Scheme.

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