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Revisiting dequantization and quantum advantage in learning tasks


Dec 06, 2021
Jordan Cotler, Hsin-Yuan Huang, Jarrod R. McClean

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* 6 pages, 1 figure; v2: further exposition added 

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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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Layerwise learning for quantum neural networks


Jun 26, 2020
Andrea Skolik, Jarrod R. McClean, Masoud Mohseni, Patrick van der Smagt, Martin Leib

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* 11 pages, 7 figures 

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TensorFlow Quantum: A Software Framework for Quantum Machine Learning


Mar 06, 2020
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

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* 39 pages, 24 figures 

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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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