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Jarrod R. McClean
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2020 – today
- 2024
- [j5]William J. Huggins
, Jarrod R. McClean:
Accelerating Quantum Algorithms with Precomputation. Quantum 8: 1264 (2024) - [c3]Hsin-Yuan Huang
Learning Shallow Quantum Circuits. STOC 2024: 1343-1351 - [i14]Hsin-Yuan Huang, Yunchao Liu, Michael Broughton, Isaac Kim, Anurag Anshu, Zeph Landau, Jarrod R. McClean:
Learning shallow quantum circuits. CoRR abs/2401.10095 (2024) - [i13]Martin Larocca, Supanut Thanasilp, Samson Wang, Kunal Sharma, Jacob D. Biamonte, Patrick J. Coles, Lukasz Cincio, Jarrod R. McClean, Zoë Holmes, Marco Cerezo:
A Review of Barren Plateaus in Variational Quantum Computing. CoRR abs/2405.00781 (2024) - [i12]Dar Gilboa, Siddhartha Jain, Jarrod R. McClean:
Consumable Data via Quantum Communication. CoRR abs/2409.08495 (2024) - [i11]Dar Gilboa, Siddhartha Jain, Jarrod R. McClean:
Consumable Data via Quantum Communication. Electron. Colloquium Comput. Complex. TR24 (2024) - 2023
- [c2]Amira Abbas, Robbie King, Hsin-Yuan Huang, William J. Huggins, Ramis Movassagh, Dar Gilboa, Jarrod R. McClean:
On quantum backpropagation, information reuse, and cheating measurement collapse. NeurIPS 2023 - [i10]Amira Abbas, Robbie King, Hsin-Yuan Huang, William J. Huggins, Ramis Movassagh, Dar Gilboa, Jarrod R. McClean:
On quantum backpropagation, information reuse, and cheating measurement collapse. CoRR abs/2305.13362 (2023) - 2022
- [j4]Yu Tong
Provably accurate simulation of gauge theories and bosonic systems. Quantum 6: 816 (2022) - 2021
- [j3]Jarrod R. McClean
From Molecules to Quantum Computers: A Research Retrospective. Comput. Sci. Eng. 23(6): 52-57 (2021) - [j2]Andrea Skolik
Layerwise learning for quantum neural networks. Quantum Mach. Intell. 3(1): 1-11 (2021) - [i9]Yu Tong, Victor V. Albert, Jarrod R. McClean, John Preskill, Yuan Su:
Provably accurate simulation of gauge theories and bosonic systems. CoRR abs/2110.06942 (2021) - [i8]Hsin-Yuan Huang, Michael Broughton, Jordan Cotler, Sitan Chen, Jerry Li, Masoud Mohseni, Hartmut Neven, Ryan Babbush, Richard Kueng, John Preskill, Jarrod R. McClean:
Quantum advantage in learning from experiments. CoRR abs/2112.00778 (2021) - [i7]Jordan Cotler, Hsin-Yuan Huang, Jarrod R. McClean:
Revisiting dequantization and quantum advantage in learning tasks. CoRR abs/2112.00811 (2021) - 2020
- [j1]Ian D. Kivlichan
Improved Fault-Tolerant Quantum Simulation of Condensed-Phase Correlated Electrons via Trotterization. Quantum 4: 296 (2020) - [i6]Michael Broughton, Guillaume Verdon, Trevor McCourt, Antonio J. Martinez, Jae Hyeon Yoo, Sergei V. Isakov, Philip Massey, Murphy Yuezhen Niu, Ramin Halavati, Evan Peters
TensorFlow Quantum: A Software Framework for Quantum Machine Learning. CoRR abs/2003.02989 (2020) - [i5]Andrea Skolik, Jarrod R. McClean, Masoud Mohseni, Patrick van der Smagt, Martin Leib:
Layerwise learning for quantum neural networks. CoRR abs/2006.14904 (2020) - [i4]Hsin-Yuan Huang, Michael Broughton, Masoud Mohseni, Ryan Babbush, Sergio Boixo, Hartmut Neven, Jarrod R. McClean:
Power of data in quantum machine learning. CoRR abs/2011.01938 (2020) - [i3]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:
Variational Quantum Algorithms. CoRR abs/2012.09265 (2020)
2010 – 2019
- 2019
- [i2]Guillaume Verdon, Michael Broughton, Jarrod R. McClean, Kevin J. Sung, Ryan Babbush, Zhang Jiang, Hartmut Neven, Masoud Mohseni:
Learning to learn with quantum neural networks via classical neural networks. CoRR abs/1907.05415 (2019) - 2018
- [c1]Kostyantyn Kechedzhi, Vadim Smelyanskiy, Jarrod R. McClean, Vasil S. Denchev, Masoud Mohseni, Sergei Isakov, Sergio Boixo, Boris Altshuler, Hartmut Neven:
Efficient Population Transfer via Non-Ergodic Extended States in Quantum Spin Glass. TQC 2018: 9:1-9:16 - [i1]Jarrod R. McClean, Sergio Boixo, Vadim N. Smelyanskiy, Ryan Babbush, Hartmut Neven:
Barren plateaus in quantum neural network training landscapes. CoRR abs/1803.11173 (2018)
Coauthor Index
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