PhD: Optimal characterization & calibration of quantum computers for error correction & applications

The studentship will equally be embedded in Prof. Mintert's quantum theory research group at Imperial College London, and the Prof. Rungger's Quantum Software and Modelling team at NPL. Prof. Mintert has a strong focus in the use of statistical machine learning to identify the optimal use of noise quantum devices, with practical demonstrations for a number of devices. Prof. Rungger's team at NPL is developing a number of machine learning based algorithms for characterization of quantum devices, such as the recently proposed hierarchical discrete fluctuation auto-segmentation method allowing to disentangle individual sources of decoherence from the noisy output of devices. The successful candidate should have an undergraduate degree in Mathematics, Physics, Computer Science or a related area. A strong education in quantum mechanics and machine learning is an advantage as will be excellent computer programming skills.

A joint PhD studentship at the National Physical Laboratory (NPL, https://www.npl.co.uk/) and Imperial College London (https://www.imperial.ac.uk/) is available starting September 2026. The goal of this theoretical and computational project is to develop optimal strategies for characterization, calibration and quantum error correction of quantum computers as they scale up to the large qubit numbers required for achieving quantum advantage over conventional computers. The studentship will cover tuition fees for home students and provides a stipend with London allowance for a period of 3.5 years. The prospective PhD student will be based both in the Quantum Technologies Department at NPL (Teddington, Greater London) as well as at Imperial College London, with an approximately even time split between the two institutions over the duration of the PhD. The close proximity between NPL and Imperial will allow for a close collaboration with regular in-person meetings with all involved partners. The student will have the opportunity to engage with a number of academic and industrial collaborators. Description of the project In the international race towards making quantum computing practically useful, both in terms of hardware and software, significant progress is being made, with the UK quantum ecosystem emerging as a key driver. Nonetheless, understanding and correcting errors due to noise in quantum computers remains one of the key challenges that must be addressed before quantum computers can reliably outperform conventional computers. Many conventional algorithms for accurately characterizing smaller-scale quantum computers require too many measurements to be applied on the emerging larger-scale devices. This project will develop new models and algorithms to tackle this challenge by combining physics informed approaches with machine learning techniques to extract maximal information about a quantum device from a minimal number of measurements. These methods will be integrated with active learning and other AI techniques to progressively accumulate the data required for, 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. Find out more about what it is like working here - The measure of us - Overview 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 (as defined by the Equality Act 2010) who satisfy the minimum criteria for the role will be guaranteed an interview under the Disability Confident Scheme. At NPL, we believe our success is a result of the diversity and talent of our people. We strive to nurture and respect individuals to ensure everyone feels valued by treating everyone on the basis of their own individual merits and abilities regardless of their own or perceived identity, as part of our commitment to diversity & inclusion, we ensure we're creating an environment where all our colleagues feel supported and welcome. More about this on our Diversity & Inclusion page.