Jarrod R. McClean

According to our database, Jarrod R. McClean authored at least 15 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Accelerating Quantum Algorithms with Precomputation.

William J. Huggins, Jarrod R. McClean

Quantum, February, 2024

Learning shallow quantum circuits.

Hsin-Yuan Huang, Yunchao Liu, Michael Broughton, Isaac Kim, Anurag Anshu, Zeph Landau, Jarrod R. McClean

CoRR, 2024

2023

On quantum backpropagation, information reuse, and cheating measurement collapse.

Amira Abbas, Robbie King, Hsin-Yuan Huang, William J. Huggins, Ramis Movassagh, Dar Gilboa, Jarrod R. McClean

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Provably accurate simulation of gauge theories and bosonic systems.

Yu Tong, Victor V. Albert, Jarrod R. McClean, John Preskill, Yuan Su

Quantum, September, 2022

2021

Layerwise learning for quantum neural networks.

Andrea Skolik, Jarrod R. McClean, Masoud Mohseni, Patrick van der Smagt, Martin Leib

Quantum Mach. Intell., 2021

From Molecules to Quantum Computers: A Research Retrospective.

Jarrod R. McClean

Comput. Sci. Eng., 2021

Revisiting dequantization and quantum advantage in learning tasks.

Jordan Cotler, Hsin-Yuan Huang, Jarrod R. McClean

CoRR, 2021

Quantum advantage in learning from experiments.

Hsin-Yuan Huang, Michael Broughton, Jordan Cotler, Sitan Chen, Jerry Li, Masoud Mohseni, Hartmut Neven, Ryan Babbush, Richard Kueng, John Preskill, Jarrod R. McClean

CoRR, 2021

2020

Improved Fault-Tolerant Quantum Simulation of Condensed-Phase Correlated Electrons via Trotterization.

Ian D. Kivlichan, Craig Gidney, Dominic W. Berry, Nathan Wiebe, Jarrod R. McClean, Wei Sun, Zhang Jiang, Nicholas C. Rubin, Austin G. Fowler, Alán Aspuru-Guzik, Hartmut Neven, Ryan Babbush

Quantum, 2020

Variational Quantum Algorithms.

Marco Cerezo, Andrew Arrasmith, Ryan Babbush, Simon C. Benjamin, Suguru Endo, Keisuke Fujii, Jarrod R. McClean, Kosuke Mitarai, Xiao Yuan, Lukasz Cincio, Patrick J. Coles

CoRR, 2020

Power of data in quantum machine learning.

Hsin-Yuan Huang, Michael Broughton, Masoud Mohseni, Ryan Babbush, Sergio Boixo, Hartmut Neven, Jarrod R. McClean

CoRR, 2020

TensorFlow Quantum: A Software Framework for Quantum Machine Learning.

Michael Broughton, Guillaume Verdon, Trevor McCourt, Antonio J. Martinez, Jae Hyeon Yoo, Sergei V. Isakov, Philip Massey, Murphy Yuezhen Niu, Ramin Halavati, Evan Peters, Martin Leib, Andrea Skolik, Michael Streif, David Von Dollen, Jarrod R. McClean, Sergio Boixo, Dave Bacon, Alan K. Ho, Hartmut Neven, Masoud Mohseni

CoRR, 2020

2019

Learning to learn with quantum neural networks via classical neural networks.

Guillaume Verdon, Michael Broughton, Jarrod R. McClean, Kevin J. Sung, Ryan Babbush, Zhang Jiang, Hartmut Neven, Masoud Mohseni

CoRR, 2019

2018

Barren plateaus in quantum neural network training landscapes.

Jarrod R. McClean, Sergio Boixo, Vadim N. Smelyanskiy, Ryan Babbush, Hartmut Neven

CoRR, 2018

Efficient Population Transfer via Non-Ergodic Extended States in Quantum Spin Glass.

Kostyantyn Kechedzhi, Vadim Smelyanskiy, Jarrod R. McClean, Vasil S. Denchev, Masoud Mohseni, Sergei Isakov, Sergio Boixo, Boris Altshuler, Hartmut Neven

Proceedings of the 13th Conference on the Theory of Quantum Computation, 2018