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Jarrod R McClean

Showing results (1-10 of 13) with videos related to

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Nature Communications|December 31, 2025
Quantum advantage for learning shallow neural networks with natural data distributionsLaura Lewis, Dar Gilboa, Jarrod R McClean
Proceedings of the National Academy of Sciences of the United States of America|September 25, 2013
Feynman's clock, a new variational principle, and parallel-in-time quantum dynamicsJarrod R McClean, John A Parkhill, Alán Aspuru-Guzik
The Journal of Physical Chemistry Letters|August 15, 2015
Exploiting Locality in Quantum Computation for Quantum ChemistryJarrod R McClean, Ryan Babbush, Peter J Love, et al.
Nature Communications|November 18, 2018
Barren plateaus in quantum neural network training landscapesJarrod R McClean, Sergio Boixo, Vadim N Smelyanskiy, et al.
Nature Communications|February 2, 2020
Decoding quantum errors with subspace expansionsJarrod R McClean, Zhang Jiang, Nicholas C Rubin, et al.
Journal of Chemical Theory and Computation|June 3, 2016
Error Sensitivity to Environmental Noise in Quantum Circuits for Chemical State PreparationNicolas P D Sawaya, Mikhail Smelyanskiy, Jarrod R McClean, et al.
Nature Communications|May 12, 2021
Power of data in quantum machine learningHsin-Yuan Huang, Michael Broughton, Masoud Mohseni, et al.
The Journal of Chemical Physics|October 23, 2021
What the foundations of quantum computer science teach us about chemistryJarrod R McClean, Nicholas C Rubin, Joonho Lee, et al.
Science (New York, N.Y.)|June 9, 2022
Quantum advantage in learning from experimentsHsin-Yuan Huang, Michael Broughton, Jordan Cotler, et al.
Science Advances|February 2, 2018
Witnessing eigenstates for quantum simulation of Hamiltonian spectraRaffaele Santagati, Jianwei Wang, Antonio A Gentile, et al.
Pageof 2

Showing results (1-10 of 13) with videos related to

Sort By:
Pageof 2
Nature Communications|December 31, 2025
Quantum advantage for learning shallow neural networks with natural data distributionsLaura Lewis, Dar Gilboa, Jarrod R McClean
Proceedings of the National Academy of Sciences of the United States of America|September 25, 2013
Feynman's clock, a new variational principle, and parallel-in-time quantum dynamicsJarrod R McClean, John A Parkhill, Alán Aspuru-Guzik
The Journal of Physical Chemistry Letters|August 15, 2015
Exploiting Locality in Quantum Computation for Quantum ChemistryJarrod R McClean, Ryan Babbush, Peter J Love, et al.
Nature Communications|November 18, 2018
Barren plateaus in quantum neural network training landscapesJarrod R McClean, Sergio Boixo, Vadim N Smelyanskiy, et al.
Nature Communications|February 2, 2020
Decoding quantum errors with subspace expansionsJarrod R McClean, Zhang Jiang, Nicholas C Rubin, et al.
Journal of Chemical Theory and Computation|June 3, 2016
Error Sensitivity to Environmental Noise in Quantum Circuits for Chemical State PreparationNicolas P D Sawaya, Mikhail Smelyanskiy, Jarrod R McClean, et al.
Nature Communications|May 12, 2021
Power of data in quantum machine learningHsin-Yuan Huang, Michael Broughton, Masoud Mohseni, et al.
The Journal of Chemical Physics|October 23, 2021
What the foundations of quantum computer science teach us about chemistryJarrod R McClean, Nicholas C Rubin, Joonho Lee, et al.
Science (New York, N.Y.)|June 9, 2022
Quantum advantage in learning from experimentsHsin-Yuan Huang, Michael Broughton, Jordan Cotler, et al.
Science Advances|February 2, 2018
Witnessing eigenstates for quantum simulation of Hamiltonian spectraRaffaele Santagati, Jianwei Wang, Antonio A Gentile, et al.
Pageof 2