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Quantum advantage in learning from experiments


Dec 01, 2021
Hsin-Yuan Huang, Michael Broughton, Jordan Cotler, Sitan Chen, Jerry Li, Masoud Mohseni, Hartmut Neven, Ryan Babbush, Richard Kueng, John Preskill, Jarrod R. McClean

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* 6 pages, 17 figures + 46 page appendix; open-source code available at https://github.com/quantumlib/ReCirq/tree/master/recirq/qml_lfe 

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Variational Quantum Algorithms


Dec 16, 2020
M. Cerezo, Andrew Arrasmith, Ryan Babbush, Simon C. Benjamin, Suguru Endo, Keisuke Fujii, Jarrod R. McClean, Kosuke Mitarai, Xiao Yuan, Lukasz Cincio, Patrick J. Coles

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* Review Article. 29 pages, 6 figures 

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Power of data in quantum machine learning


Nov 03, 2020
Hsin-Yuan Huang, Michael Broughton, Masoud Mohseni, Ryan Babbush, Sergio Boixo, Hartmut Neven, Jarrod R. McClean

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Learning to learn with quantum neural networks via classical neural networks


Jul 11, 2019
Guillaume Verdon, Michael Broughton, Jarrod R. McClean, Kevin J. Sung, Ryan Babbush, Zhang Jiang, Hartmut Neven, Masoud Mohseni

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* 12 pages, 4 figures 

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Barren plateaus in quantum neural network training landscapes


Mar 29, 2018
Jarrod R. McClean, Sergio Boixo, Vadim N. Smelyanskiy, Ryan Babbush, Hartmut Neven

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Bayesian Network Structure Learning Using Quantum Annealing


Oct 02, 2014
Bryan O'Gorman, Alejandro Perdomo-Ortiz, Ryan Babbush, Alan Aspuru-Guzik, Vadim Smelyanskiy

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* Eur. Phys. J. Spec. Top., 225 (1), 163 (2015) 

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


Jun 17, 2014
Ryan Babbush, Vasil Denchev, Nan Ding, Sergei Isakov, Hartmut Neven

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* 15 pages, 6 figures 

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