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A quantum algorithm for training wide and deep classical neural networks


Jul 19, 2021
Alexander Zlokapa, Hartmut Neven, Seth Lloyd

* 10 pages + 13 page appendix, 10 figures; code available at https://github.com/quantummind/quantum-deep-neural-network 

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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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Learnability and Complexity of Quantum Samples


Oct 22, 2020
Murphy Yuezhen Niu, Andrew M. Dai, Li Li, Augustus Odena, Zhengli Zhao, Vadim Smelyanskyi, Hartmut Neven, Sergio Boixo


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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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Learning Non-Markovian Quantum Noise from Moiré-Enhanced Swap Spectroscopy with Deep Evolutionary Algorithm


Dec 09, 2019
Murphy Yuezhen Niu, Vadim Smelyanskyi, Paul Klimov, Sergio Boixo, Rami Barends, Julian Kelly, Yu Chen, Kunal Arya, Brian Burkett, Dave Bacon, Zijun Chen, Ben Chiaro, Roberto Collins, Andrew Dunsworth, Brooks Foxen, Austin Fowler, Craig Gidney, Marissa Giustina, Rob Graff, Trent Huang, Evan Jeffrey, David Landhuis, Erik Lucero, Anthony Megrant, Josh Mutus, Xiao Mi, Ofer Naaman, Matthew Neeley, Charles Neill, Chris Quintana, Pedram Roushan, John M. Martinis, Hartmut Neven


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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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Quantum-Assisted Genetic Algorithm


Jun 24, 2019
James King, Masoud Mohseni, William Bernoudy, Alexandre Fréchette, Hossein Sadeghi, Sergei V. Isakov, Hartmut Neven, Mohammad H. Amin

* 13 pages, 5 figures, presented at AQC 2019 

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Universal discriminative quantum neural networks


May 22, 2018
Hongxiang Chen, Leonard Wossnig, Simone Severini, Hartmut Neven, Masoud Mohseni

* 19 pages, 10 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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Probabilistic Label Relation Graphs with Ising Models


Dec 22, 2015
Nan Ding, Jia Deng, Kevin Murphy, Hartmut Neven

* International Conference on Computer Vision (2015) 

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Totally Corrective Boosting with Cardinality Penalization


Apr 07, 2015
Vasil S. Denchev, Nan Ding, Shin Matsushima, S. V. N. Vishwanathan, Hartmut Neven


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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

* 15 pages, 6 figures 

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Training a Large Scale Classifier with the Quantum Adiabatic Algorithm


Dec 04, 2009
Hartmut Neven, Vasil S. Denchev, Geordie Rose, William G. Macready

* 14 pages, 5 figures 

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