A fully funded PhD studentship is available in the Quantum Technologies Department at the National Physical Laboratory (NPL, ) in collaboration with the Computer Science Department at Royal Holloway, University of London (RHUL, ), starting in Fall 2026. This theoretical and computational project aims to develop advanced computational techniques that leverage emerging AI and machine learning frameworks to support the realization of large scale quantum computing hardware in the race towards achieving practical quantum advantage over conventional systems.
The studentship will cover tuition fees for home students and provides a London weighted stipend for 3.5 years. The successful candidate will be primarily based in the Quantum Technologies department at NPL (Teddington, Greater London), with regular visits to Royal Holloway. The project will support ongoing and forthcoming international collaborations, such as a recently awarded EPSRC-ASPIRE UK-Japan collaboration, providing ample opportunities for research exchange and global collaboration with leading international institutions.
Realizing the full potential of quantum computing across its many proposed application areas requires hardware that can perform quantum operations at scale with low error rates. Although several hardware platforms have shown great promise, the characterization, calibration, and operation of quantum devices remain major bottlenecks.
Achieving reliable performance with minimal measurement overhead is critical, as each additional measurement increases experimental time and resource costs. This project aims to develop new pipelines that reduce measurement overhead by integrating physical knowledge of quantum devices with scalable machine learning approaches. By combining domain expertise with modern AI methods, we seek to address key scalability challenges in quantum hardware platforms, with a particular focus on deploying algorithms in collaboration with superconducting and spin qubit research teams.
These new approaches will form part of active learning pipelines that support the automated identification and implementation of low noise, scalable quantum gates in experimental systems. The project will unlock new capabilities for the practical realization of quantum technologies at scale, helping to bridge the gap between today’s small scale prototypes and future large scale quantum hardware capable of tackling real world computational challenges.
The studentship will be hosted within NPL’s Quantum Software and Modelling team, which focuses on developing the computational foundations required to support emerging quantum technologies and bridge the gap between fundamental research and industrial applications. The project will be jointly supervised by Dr. Yannic Rath (Senior Scientist, NPL) and Prof. Ivan Rungger (Professor of Computer Science, Royal Holloway), whose combined expertise spans quantum theory, computational modelling, and the development of practical algorithms for quantum device characterization and simulation.
Their recent work includes adapting machine learning based techniques for quantum device tune-up, including the recently proposed Active Learning Sparse Measurement scheme, an approach designed to automate the tune up of quantum dot devices for metrological applications.