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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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Nonequilibrium Monte Carlo for unfreezing variables in hard combinatorial optimization


Nov 26, 2021
Masoud Mohseni, Daniel Eppens, Johan Strumpfer, Raffaele Marino, Vasil Denchev, Alan K. Ho, Sergei V. Isakov, Sergio Boixo, Federico Ricci-Tersenghi, Hartmut Neven

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* 28 pages, 18 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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A Probability Density Theory for Spin-Glass Systems


Jan 10, 2020
Gavin S. Hartnett, Masoud Mohseni

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* 20 pages, 5 figures 

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Self-Supervised Learning of Generative Spin-Glasses with Normalizing Flows


Jan 10, 2020
Gavin S. Hartnett, Masoud Mohseni

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* 16 pages, 7 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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