Menu
Close
Sign up for NPL updates
Close
Sign up for NPL updates

For people, place, prosperity and planet, we deliver impact with measurement science

Yannic Rath

Yannic Rath

Senior scientist

Yannic Rath is a senior scientist in the Quantum Software and Modelling team (Quantum Technologies department), where he develops algorithms and software to support the development and benchmarking of quantum hardware and software. To bridge the gap between the theoretical promise of quantum technologies and their industrial application, his work largely focuses on designing algorithms for simulating strongly correlated matter, helping to identify where practical quantum advantage may emerge, and building trustworthy AI frameworks to support the scalability of quantum hardware.

Research Interests 

  • Physics-informed machine learning for quantum science and technologies
  • Classical and quantum algorithms to probe quantum advantage in the simulation of correlated matter and machine learning
  • Trustworthy AI to address scalability challenges in quantum hardware

Contact

Biography 

Before joining NPL in late 2023, Yannic was a postdoctoral researcher and PhD student in the Theory and Simulation of Condensed Matter group at King’s College London, working with Prof. George Booth (2018 - 2023). He holds an MSc in Physics with Extended Research from Imperial College London (2018), and dual BSc degrees in Physics and Computer Science from Leibniz University Hannover (2014 and 2016).

Research

Yannic’s PhD research, recognised with the 2023 King’s College London Outstanding Thesis Prize, introduced the Gaussian Process States framework, a Bayesian machine learning approach for simulating quantum systems. This work advanced the use of machine-learned quantum states, such as Neural Quantum States, for first-principles simulation of chemical systems. He also developed a framework for high-accuracy molecular dynamics simulations across extended timescales using only a few quantum state solutions, significantly reducing the computational cost of simulating chemical dynamics from first-principles quantum states. By pushing the boundaries of classical simulability of quantum matter, this research supports efforts to identify where practical quantum advantage may emerge.

In parallel, Yannic develops machine learning frameworks to address scalability challenges in quantum technologies. He leads the Trustworthy Machine Learning for Quantum Technologies research activity, which recently introduced the Active Learning Sparse Measurement (ALSM) scheme, a protocol that substantially reduces measurement overhead in the characterisation of quantum metrological devices.

Selected Publications

See Google Scholar for a full list of Yannic’s publications.