Physicist, Joint Appointment in the High-Performance Computing group
Physicist, Joint Appointment in the High-Performance Computing group


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. Wiebe currently holds a Joint Appointment in the High-Performance Computing group at PNNL and is an assistant professor of Computer Science at the University of Toronto. Wiebe also holds an affiliate professorship in the Department of Physics at the University of Washington.

Disciplines and Skills

  • Machine Learning
  • Precision Measurement
  • Quantum Algorithms
  • Quantum Computing
  • Quantum Information
  • Quantum Metrology
  • Quantum Simulation


  • PhD in Physics, University of Calgary (2011)
  • MSC in Physics, Simon Fraser University (2005)
  • BSC in Mathematical Physics, Simon Fraser University (2002)

Affiliations and Professional Service

  • Assistant Professor University of Toronto, Department of Computer Science
  • Affiliate Assistant Professor University of Washington, Department of Physics
  • Associate Editor Nature Quantum Information
  • Thrust Lead for Algorithms, Theory and Software for DOE Codesign Center for Quantum Advantage (C2QA)

Awards and Recognitions

  • Yale Distinguished Scholar (2022)
  • Province of Ontario Early Research Award (2022)
  • Google Research Award (2021)
  • Google Research Award (2020)


  • N Wiebe, A Kapoor, K Svore “Method and system for computing distance measures on a quantum computer” (2013)
  • N‍ Wiebe, M‍ Roetteler ‍“Quantum‍ algorithms ‍for‍ arithmetic ‍and ‍function‍ synthesis”‍(2014)
  • N ‍Wiebe, A‍ Kapoor,‍ K‍ Svore‍ “Quantum ‍deep ‍learning” ‍(2014)
  • N‍ Wiebe, ‍A ‍Kapoor,‍ K‍ Svore, ‍C ‍Granade‍ “Fast low-memory methods for Bayesian inference, Gibbs sampling and deep learning”‍ (2015)
  • N ‍Wiebe, C ‍Granade‍ “Efficient‍ online ‍methods‍ for‍ quantum‍ Bayesian ‍inference” ‍(2015)
  • I ‍Zintchenko, ‍N‍ Wiebe‍ “Randomized‍ gap‍ and ‍amplitude‍ estimation” ‍(2015)
  • I Zintchenko, M Hastings, N Wiebe, M Troyer “Partial Reinitialization for optimizers” (2015)
  • M ‍Kieferova,‍ N‍ Wiebe ‍“Tomography and generative data modeling via quantum Boltzmann training” (2016)
  • C ‍Granade,‍ N‍ Wiebe ‍“Random ‍Walk‍ Phase ‍Estimation” ‍(2017)
  • N Wiebe, ‍R‍ K‍ Shankar‍ “Adversarial ‍Quantum‍ Machine ‍Learning” ‍(2017)
  • G H Low, N Wiebe. “Hamiltonian simulation in the interaction picture” (2018)
  • A ‍Gilyen,‍ N‍ Wiebe‍ “Phase‍ arithmetic‍ for‍ quantum ‍computation” ‍(2018)



  • Bauman N.P., H. Liu, E.J. Bylaska, S. Krishnamoorthy, G. Low, C.E. Granade, and N.O. Wiebe, et al. 2021. "Toward quantum computing for high-energy excited states in molecular systems: quantum phase estimations of core-level states." Journal of Chemical Theory and Computation 17, no. 1:201-210. PNNL-SA-154437. doi:10.1021/acs.jctc.0c00909
  • Childs A.M., Y. Su, M. Tran, N.O. Wiebe, and S. Zhu. 2021. "Theory of Trotter Error with Commutator Scaling." Physical Review X 11, no. 1: Article No.011020. PNNL-SA-150114. doi:10.1103/PhysRevX.11.011020


  • Darulova J., S.J. Pauka, N.O. Wiebe, K.W. Chan, G.C. Gardner, M.J. Manfra, and M.C. Cassidy, et al. 2020. "Autonomous Tuning and Charge-State Detection of Gate-Defined Quantum Dots." Physical Review Applied 13, no. 5: Article No. 054005. PNNL-SA-158016. doi:10.1103/PhysRevApplied.13.054005


  • Bauman N.P., E.J. Bylaska, S. Krishnamoorthy, G. Low, N.O. Wiebe, C.E. Granade, and M. Roetteler, et al. 2019. "Downfolding of many-body Hamiltonians using active-space models: extension of the sub-system embedding sub-algebras approach to unitary coupled cluster formalisms." Journal of Chemical Physics 151, no. 1: Article Number 014107. PNNL-SA-141041. doi:10.1063/1.5094643