David Gosset

Photograph of David Gosset
Institute for Quantum Computing (IQC)
Research Associate Faculty
Areas of research:
Quantum Information
(519)569-7600 x7002
If you are interested in working with me as a PhD student, please submit an application directly to my department at the University of Waterloo and indicate that you would like to be supervised by me. Perimeter Institute is committed to diversity within its community and I welcome applications from underrepresented groups.
I am interested in quantum algorithms, classical simulation of quantum computers, and the complexity of quantum many-body systems.
  • Research Staff Member and Manager, Theory of quantum algorithms group, IBM T.J. Watson Research Center, 2017-2018
  • Research Staff Member, Theory of quantum computing and information group, IBM T.J. Watson Research Center, 2016-2017
  • Grant for Quantum Algorithms Machine Learning and Optimization, The U.S. Army Research Office (ARO), 2020-2023
  • Discovery Grant, Natural Sciences and Engineering Research Council of Canada (NSERC), 2019
  • Discovery Launch Supplement, Natural Sciences and Engineering Research Council of Canada (NSERC), 2019
  • Discovery Accelerator Supplement, Natural Sciences and Engineering Research Council of Canada (NSERC), 2019
  • IBM Q Scholars Joint Study Agreement, International Business Machines Corporation, 2019
  • Startup Funds, University of Waterloo, 2018
  • CIFAR Fellowship, Canadian Institute for Advanced Research (CIFAR), 2018
  • Watts, A. B., Gosset, D., Liu, Y., & Soleimanifar, M. (2024). Quantum advantage from measurement-induced entanglement in random shallow circuits. arxiv:2407.21203v1
  • Bravyi, S., Gosset, D., & Liu, Y. (2024). Classical Simulation of Peaked Shallow Quantum Circuits. In Proceedings of the 56th Annual ACM Symposium on Theory of Computing (pp. 561-572). Association for Computing Machinery (ACM). doi:10.1145/3618260.3649638
  • King, R., Gosset, D., Kothari, R., & Babbush, R. (2024). Triply efficient shadow tomography. arxiv:2404.19211v1
  • Gosset, D., Grier, D., Kerzner, A., & Schaeffer, L. (n.d.). Fast simulation of planar Clifford circuits. Quantum, 8, 1251. doi:10.22331/q-2024-02-12-1251
  • Bravyi, S., Chowdhury, A., Gosset, D., Havlícek, V., & Zhu, G. (2024). Quantum Complexity of the Kronecker Coefficients. PRX Quantum, 5(1), 010329. doi:10.1103/prxquantum.5.010329
  • Bravyi, S., Carleo, G., Gosset, D., & Liu, Y. (n.d.). A rapidly mixing Markov chain from any gapped quantum many-body system. Quantum, 7, 1173. doi:10.22331/q-2023-11-07-1173
  • Bravyi, S., Gosset, D., & Liu, Y. (2023). Classical simulation of peaked shallow quantum circuits. doi:10.48550/arxiv.2309.08405
  • Bravyi, S., Chowdhury, A., Gosset, D., Havlicek, V., & Zhu, G. (2023). Quantum complexity of the Kronecker coefficients. doi:10.48550/arxiv.2302.11454
  • Bravyi, S., Chowdhury, A., Gosset, D., & Wocjan, P. (2022). Quantum Hamiltonian complexity in thermal equilibrium. Nature Physics, 18(11), 1367-1370. doi:10.1038/s41567-022-01742-5
  • Anshu, A., Arad, I., & Gosset, D. (2022). Entanglement Subvolume Law for 2D Frustration-Free Spin Systems. Communications in Mathematical Physics, 393(2), 955-988. doi:10.1007/s00220-022-04381-2
  • Anshu, A., Arad, I., & Gosset, D. (2022). An area law for 2d frustration-free spin systems. In Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing (pp. 12-18). Association for Computing Machinery (ACM). doi:10.1145/3519935.3519962
  • Bravyi, S., Gosset, D., & Liu, Y. (2022). How to Simulate Quantum Measurement without Computing Marginals. Physical Review Letters, 128(22), 220503. doi:10.1103/physrevlett.128.220503
  • Qassim, H., Pashayan, H., & Gosset, D. (n.d.). Improved upper bounds on the stabilizer rank of magic states. Quantum, 5, 606. doi:10.22331/q-2021-12-20-606
  • Anshu, A., Gosset, D., Morenz Korol, K. J., & Soleimanifar, M. (2021). Improved Approximation Algorithms for Bounded-Degree Local Hamiltonians. Physical Review Letters, 127(25), 250502. doi:10.1103/physrevlett.127.250502
  • Bravyi, S., Gosset, D., & Movassagh, R. (2021). Classical algorithms for quantum mean values. Nature Physics, 17(3), 337-341. doi:10.1038/s41567-020-01109-8
  • Bravyi, S., Gosset, D., Grier, D., & Schaeffer, L. (2021). Classical algorithms for Forrelation. doi:10.48550/arxiv.2102.06963
  • Triply efficient shadow tomography, Quantum lunch seminar, Los Alamos National Laboratory, Los Alamos, United States, 2024/07/25
  • Triply efficient shadow tomography, IQC-PCQT Workshop on quantum computer science, Institute for Quantum Computing, University of Waterloo, 2024/05/10
  • Classical simulation of peaked shallow quantum circuits, Fundamental limitations to quantum computation workshop, Banff International research station, Banff, Canada, 2024/03/04
  • Stabilizer rank simulators, Quantum Resources workshop, Singapore, 2023/12/12
  • Classical simulation of peaked shallow quantum circuits, Google quantum CS seminar, Google quantum AI, United States, 2023/11/28
  • Navigating opportunities in quantum industry, 2023/10/31
  • Classical simulation algorithms versus quantum computers, Q-Site conference, University of Toronto, Toronto, Canada, 2023/09/30
  • How to simulate measurement without computing marginals, IBM Qiskit quantum information seminar series, IBM Research - Thomas J. Watson Research Center, Yorktown Heights, United States, 2023/06/16
  • How to simulate measurement without computing marginals, Quantum information seminar, Harvard University, Cambridge, United States, 2023/04/13
  • Classical algorithms for Forrelation, Seminar, Google quantum AI, 2023/03/29
  • On the complexity of quantum partition functions, ICMAT focus week on Quantum Many Body Systems and Quantum Information, Institute of Mathematical Sciences, Madrid, Spain, 2023/03/14
  • Shallow circuits and the quantum-classical boundary, Quantum information summer school, Canadian Institute for Advanced Research, Quebec City, Canada, 2022/09/27
  • Shallow Clifford circuits: quantum advantage and classical simulation, NSF workshop on quantum advantage and next steps, University of Chicago, Chicago, United States, 2022/08/01
  • How to simulate quantum measurement without computing marginals, Joint Center for Quantum Information and Computer Science (QuICS) seminar, Joint Center for Quantum Information and Computer Science (QuICS), 2022/01/01
  • How to simulate quantum measurement without computing marginals, Fifth workshop on algebraic structures in quantum computation, University of British Columbia, 2022/01/01
  • Classical algorithms for Forrelation, Quantum Information Processing 2022, 2022/01/01
  • An area law for 2D frustration-free spin systems, Quantum Information Processing 2022, 2022/01/01
  • Classical algorithms for Forrelation, Los Alamos National Laboratory summer school on quantum information, Los Alamos National Laboratory, 2022/01/01