Richard Küng
Member of the Young Academy since 2024
- Johannes Kepler Universität Linz
Contact:
Orcid-ID:
0000-0002-8291-648X
Research Areas:
- Computer Sciences
- Formal languages
- Probability theory
- Quantencomputer
- math of data science and learning
Profile:
Publications:
Selected Memberships:
- FWF START Award (2023)
- ERC Starting Grant (2023)
- Kepler Award for Teaching Innovation (2023)
- Kardinal Innitzer Preis (2022)
- Willi Studer-Preis der ETH Zürich (2013)
Selected Publications:
- H.Y. Huang, R. Kueng, J. Preskill. Predicting many properties of a quantum system from very few measurements. Nature Physics 16, 1050-1057 (2020)
- H.Y. Huang, R. Kueng, G. Torlai, V.A. Albert, J. Preskill. Provably efficient machine learning for quantum manybody problems. Science 377, eabk3333 (2022)
- A. Elben, S.T. Flammia, H.Y. Huang, R. Kueng, J. Preskill, B. Vermersch, P. Zoller. The randomized measurement toolbox. Nature Reviews Physics, 1-16 (2023)
- R. Kueng. H. Rauhut, U. Testiege. Low rank matrix recovery from rank one measurements. Applied and Computational Harmonic Analysis 42, 88-116 (2017)
- F.G.S.L Brandão, W. Chemissany, N. Hunter-Jones, R. Kueng, J. Preskill. Models of quantum complexity growth. PRX Quantum 2, 030316 (2021)