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Nature Communications
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December 31, 2025
Quantum advantage for learning shallow neural networks with natural data distributions
Laura 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 dynamics
Jarrod R McClean, John A Parkhill, Alán Aspuru-Guzik
The Journal of Physical Chemistry Letters
|
August 15, 2015
Exploiting Locality in Quantum Computation for Quantum Chemistry
Jarrod R McClean, Ryan Babbush, Peter J Love, et al.
Nature Communications
|
November 18, 2018
Barren plateaus in quantum neural network training landscapes
Jarrod R McClean, Sergio Boixo, Vadim N Smelyanskiy, et al.
Nature Communications
|
February 2, 2020
Decoding quantum errors with subspace expansions
Jarrod 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 Preparation
Nicolas P D Sawaya, Mikhail Smelyanskiy, Jarrod R McClean, et al.
Nature Communications
|
May 12, 2021
Power of data in quantum machine learning
Hsin-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 chemistry
Jarrod R McClean, Nicholas C Rubin, Joonho Lee, et al.
Science (New York, N.Y.)
|
June 9, 2022
Quantum advantage in learning from experiments
Hsin-Yuan Huang, Michael Broughton, Jordan Cotler, et al.
Science Advances
|
February 2, 2018
Witnessing eigenstates for quantum simulation of Hamiltonian spectra
Raffaele Santagati, Jianwei Wang, Antonio A Gentile, et al.
Page
of 2
Search research articles
Search
Showing results (1-10 of 13) with videos related to
Sort By:
Page
of 2
Nature Communications
|
December 31, 2025
Quantum advantage for learning shallow neural networks with natural data distributions
Laura 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 dynamics
Jarrod R McClean, John A Parkhill, Alán Aspuru-Guzik
The Journal of Physical Chemistry Letters
|
August 15, 2015
Exploiting Locality in Quantum Computation for Quantum Chemistry
Jarrod R McClean, Ryan Babbush, Peter J Love, et al.
Nature Communications
|
November 18, 2018
Barren plateaus in quantum neural network training landscapes
Jarrod R McClean, Sergio Boixo, Vadim N Smelyanskiy, et al.
Nature Communications
|
February 2, 2020
Decoding quantum errors with subspace expansions
Jarrod 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 Preparation
Nicolas P D Sawaya, Mikhail Smelyanskiy, Jarrod R McClean, et al.
Nature Communications
|
May 12, 2021
Power of data in quantum machine learning
Hsin-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 chemistry
Jarrod R McClean, Nicholas C Rubin, Joonho Lee, et al.
Science (New York, N.Y.)
|
June 9, 2022
Quantum advantage in learning from experiments
Hsin-Yuan Huang, Michael Broughton, Jordan Cotler, et al.
Science Advances
|
February 2, 2018
Witnessing eigenstates for quantum simulation of Hamiltonian spectra
Raffaele Santagati, Jianwei Wang, Antonio A Gentile, et al.
Page
of 2