Nathan Wiebe is a researcher in quantum computing who focuses on quantum methods for machine learning and simulation of physical systems. His work has provided the first quantum algorithms for deep learning, least squares fitting, quantum simulations using linear-combinations of unitaries, quantum Hamiltonian learning, near-optimal simulation of time-dependent physical systems, efficient Bayesian phase estimation and also has pioneered the use of particle filters for characterizing quantum devices as well as many other contributions ranging from the foundations of thermodynamics to adiabatic quantum computing and quantum chemistry simulation. He received his PhD in 2011 from the university of Calgary studying quantum computing before accepting a post-doctoral fellowship at the University of waterloo and then finally joining Microsoft Research in 2013. In 2019 he left Microsoft to accept a joint appointment at the university of Washington and Pacific Northwest National Labs.
- Reiher, Markus, Nathan Wiebe, Krysta M. Svore, Dave Wecker, and Matthias Troyer. "Elucidating reaction mechanisms on quantum computers." Proceedings of the National Academy of Sciences 114, no. 29 (2017): 7555-7560.
- Biamonte, Jacob, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd. "Quantum machine learning." Nature 549, no. 7671 (2017): 195.
- This is a complete list of my publications and citations on google scholar.