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Ryan Babbush
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- affiliation: Google Quantum AI, Mountain View, CA, USA
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2020 – today
- 2024
- [i9]Alexander Schmidhuber, Ryan O'Donnell, Robin Kothari, Ryan Babbush:
Quartic quantum speedups for planted inference. CoRR abs/2406.19378 (2024) - [i8]Matthew P. Harrigan, Tanuj Khattar, Charles Yuan, Anurudh Peduri, Noureldin Yosri, Fionn D. Malone, Ryan Babbush, Nicholas C. Rubin:
Expressing and Analyzing Quantum Algorithms with Qualtran. CoRR abs/2409.04643 (2024) - 2023
- [c2]Ryan Babbush, Dominic W. Berry
, Robin Kothari, Rolando D. Somma, Nathan Wiebe:
Exponential quantum speedup in simulating coupled classical oscillators*. FOCS 2023: 405-414 - 2021
- [j2]Nicholas C. Rubin, Klaas Gunst, Alec F. White
The Fermionic Quantum Emulator. Quantum 5: 568 (2021) - [i7]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) - 2020
- [j1]Ian D. Kivlichan
Improved Fault-Tolerant Quantum Simulation of Condensed-Phase Correlated Electrons via Trotterization. Quantum 4: 296 (2020) - [i6]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) - [i5]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
- [i4]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
- [i3]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) - 2014
- [c1]Nan Ding, Youhan Fang, Ryan Babbush, Changyou Chen, Robert D. Skeel, Hartmut Neven:
Bayesian Sampling Using Stochastic Gradient Thermostats. NIPS 2014: 3203-3211 - [i2]Ryan Babbush, Vasil S. Denchev, Nan Ding, Sergei Isakov, Hartmut Neven:
Construction of non-convex polynomial loss functions for training a binary classifier with quantum annealing. CoRR abs/1406.4203 (2014) - [i1]Bryan O'Gorman, Alejandro Perdomo-Ortiz, Ryan Babbush, Alán Aspuru-Guzik, Vadim Smelyanskiy:
Bayesian Network Structure Learning Using Quantum Annealing. CoRR abs/1407.3897 (2014)
Coauthor Index
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