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Provably efficient variational generative modeling of quantum many-body systems via quantum-probabilistic information geometry


Jun 09, 2022
Faris M. Sbahi, Antonio J. Martinez, Sahil Patel, Dmitri Saberi, Jae Hyeon Yoo, Geoffrey Roeder, Guillaume Verdon

* 24 + 49 pages, 5 + 4 figures 

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Group-Invariant Quantum Machine Learning


May 04, 2022
Martin Larocca, Frederic Sauvage, Faris M. Sbahi, Guillaume Verdon, Patrick J. Coles, M. Cerezo

* 17 + 11 pages, 9 + 1 figures 

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A semi-agnostic ansatz with variable structure for quantum machine learning


Mar 11, 2021
M. Bilkis, M. Cerezo, Guillaume Verdon, Patrick J. Coles, Lukasz Cincio

* 15 pages, 12 figures, 1 table 

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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

* 39 pages, 24 figures 

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Quantum Hamiltonian-Based Models and the Variational Quantum Thermalizer Algorithm


Oct 04, 2019
Guillaume Verdon, Jacob Marks, Sasha Nanda, Stefan Leichenauer, Jack Hidary

* 13 + 8 pages, 9 figures 

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Quantum Graph Neural Networks


Sep 26, 2019
Guillaume Verdon, Trevor McCourt, Enxhell Luzhnica, Vikash Singh, Stefan Leichenauer, Jack Hidary

* 8 pages 

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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

* 12 pages, 4 figures 

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