PNNL

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.

• 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)

• 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)

• 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)

2025

• Furches J.C., S. Chehade, K.E. Hamilton, N.O. Wiebe, and C.M. Ortiz Marrero. 2025. "Application-level Benchmarking of Quantum Computers using Nonlocal Game Strategies." Quantum Science and Technology 10, no. 4:Art. No. 045002. PNNL-SA-208588. doi:10.1088/2058-9565/adf1c0
• Li X., X. Yin, N.O. Wiebe, J. Chun, G.K. Schenter, M.S. Cheung, and J.H. Muelmenstaedt. 2025. "Potential quantum advantage for simulation of fluid dynamics." Physical Review Research 7, no. 1:Art. No. 013036. PNNL-SA-181572. doi:10.1103/PhysRevResearch.7.013036

2024

• Ang J.A., G. Carini, Y. Chen, I. Chuang, M.A. Demarco, S. Economou, and A. Eickbusch, et al. 2024. "ARQUIN : Architectures for Multinode Superconducting Quantum Computers." ACM Transactions on Quantum Computing 5, no. 3:Art. No. 19. PNNL-SA-189729. doi:10.1145/3674151
• Berry D.W., Y. Su, C. Gyurik, R. King, J. Basso, A. Del Toro Barba, and A. Rajput, et al. 2024. "Analyzing Prospects for Quantum Advantage in Topological Data Analysis." PRX Quantum 5, no. 1:Art. No. 010319. PNNL-SA-198939. doi:10.1103/PRXQuantum.5.010319

2023

• Stein S.A., N.O. Wiebe, Y. Ding, J.A. Ang, and A. Li. 2023. "Q-BEEP: Quantum Bayesian Error Mitigation Employing Poisson Modeling over the Hamming Spectrum." In Proceedings of the 50th Annual International Symposium on Computer Architecture (ISCA 2023), June 17-21, 2023, Orlando, FL, 1-13; Art. No. 8. New York, New York:Association for Computing Machinery. PNNL-SA-175133. doi:10.1145/3579371.3589043
• Zheng M., B. Peng, N.O. Wiebe, A. Li, X. Yang, and K. Kowalski. 2023. "Quantum algorithms for generator coordinate methods." Physical Review Research 5, no. 2:Art. No. 023200. PNNL-SA-180637. doi:10.1103/PhysRevResearch.5.023200

2022

• Flynn B., A.A. Gentile, N.O. Wiebe, R. Santagati, and A. Laing. 2022. "Quantum Model Learning Agent: Characterization of Quantum Systems Through Machine Learning." New Journal of Physics 24, no. 5:Art. No. 053034. PNNL-SA-179121. doi:10.1088/1367-2630/ac68ff
• Rajput A., A. Roggero, and N.O. Wiebe. 2022. "Hybridized Methods for Quantum Simulation in the Interactive Picture." Quantum 6. PNNL-SA-179363. doi:10.22331/q-2022-08-17-780
• Roggero A., J. Filipek, S. Chang, and N.O. Wiebe. 2022. "Quantum Machine Learning with SQUID." Quantum 6. PNNL-SA-179120. doi:10.22331/Q-2022-05-30-727
• Stavenger T.J., E. Crane, K.C. Smith, C.T. Kang, S.M. Girvin, and N.O. Wiebe. 2022. "C2QA - Bosonic Qiskit." In IEEE High Performance Extreme Computing Conference (HPEC 2022), September 19-23, 2022, Waltham, MA, 1-8. Piscataway, New Jersey:IEEE. PNNL-SA-174542. doi:10.1109/HPEC55821.2022.9926318
• Stein S.A., N.O. Wiebe, J.A. Ang, and A. Li. 2022. "Benchmarking Quantum Processor Performance through Quantum Distance Metrics Over An Algorithm Suite." In IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW 2022 ), May 30-June 3, 2022, Lyon, France, 618-624. Piscataway, New Jersey:IEEE. PNNL-SA-172019. doi:10.1109/IPDPSW55747.2022.00106
• Stein S.A., N.O. Wiebe, Y. Ding, B. Peng, K. Kowalski, N.A. Baker, and J.A. Ang, et al. 2022. "EQC: Ensembled Quantum Computing for Variational Quantum Algorithms." In Proceedings of the 49th Annual International Symposium on Computer Architecture (ISCA 2022), June 18-22, 2022, New York, NY, 59-71. New York, New York:Association for Computing Machinery. PNNL-SA-165576. doi:10.1145/3470496.3527434
• Stetina T.F., A. Ciavarella, X. Li, and N.O. Wiebe. 2022. "Simulating Effective QED on Quantum Computers." Quantum 6. PNNL-SA-165105. doi:10.22331/q-2022-01-18-622

2021

• 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
• Lee J., D.W. Berry, C. Gidney, W.J. Huggins, J.R. McLean, N.O. Wiebe, and R. Babbush. 2021. "Even more efficient quantum computations of chemistry through tensor hypercontraction." PRX Quantum 2, no. 3:Art. No. 030305. PNNL-SA-157861. doi:10.1103/PRXQuantum.2.030305
• Ortiz Marrero C.M., N.O. Wiebe, and M. Kieferova. 2021. "Entanglement-Induced Barren Plateaus." PRX Quantum 2, no. 4:Art. No. 040316. PNNL-SA-157287. doi:10.1103/PRXQuantum.2.040316
• Tong Y., D. An, L. Lin, and N.O. Wiebe. 2021. "Fast inversion, preconditioned quantum linear system solvers, fast Green's-function computation, and fast evaluation of matrix functions." Physical Review A 104, no. 3:Article No.032422. PNNL-SA-155909. doi:10.1103/PhysRevA.104.032422

2020

• 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
• Wiebe N.O. 2020. "Key Questions for the Quantum Machine Learner to Ask Themselves." New Journal of Physics 22, no. 9:Article: 091001. PNNL-SA-165134. doi:10.1088/1367-2630/abac39

2019

• 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