Ryan Babbush

According to our database, Ryan Babbush authored at least 11 papers between 2014 and 2023.

Collaborative distances:

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

2023

Exponential quantum speedup in simulating coupled classical oscillators^*.

Ryan Babbush, Dominic W. Berry, Robin Kothari, Rolando D. Somma, Nathan Wiebe

Proceedings of the 64th IEEE Annual Symposium on Foundations of Computer Science, 2023

2021

The Fermionic Quantum Emulator.

Nicholas C. Rubin, Klaas Gunst, Alec F. White, Leon Freitag, Kyle Throssell, Garnet Kin-Lic Chan, Ryan Babbush, Toru Shiozaki

Quantum, 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

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

2014

Bayesian Network Structure Learning Using Quantum Annealing.

Bryan O'Gorman, Alejandro Perdomo-Ortiz, Ryan Babbush, Alán Aspuru-Guzik, Vadim Smelyanskiy

CoRR, 2014

Construction of non-convex polynomial loss functions for training a binary classifier with quantum annealing.

Ryan Babbush, Vasil S. Denchev, Nan Ding, Sergei Isakov, Hartmut Neven

CoRR, 2014

Bayesian Sampling Using Stochastic Gradient Thermostats.

Nan Ding, Youhan Fang, Ryan Babbush, Changyou Chen, Robert D. Skeel, Hartmut Neven

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014