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We develop a new quantum neural network layer designed to run efficiently on a quantum computer but that can be simulated on a classical computer when restricted in the way it entangles input states.
Quantum Computation and Quantum Information
M.E. Nielsen and I.L. Chuang · 2000
Earlier work this paper cites.
Efficient classical simulation of slightly entangled quantum computations
Guifré Vidal · 2003
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Variational optimization, 2012
Joe Staines and David Barber · 2012
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Quantum-Bayesian coherence
Christopher A. Fuchs and Rüdiger Schack · 2013
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Simulating a perceptron on a quantum computer
Maria Schuld, Ilya Sinayskiy, and Francesco Petruccione · 2014
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Nathan Wiebe, Ashish Kapoor, and Krysta M. Svore · 2014
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Variational dropout and the local reparameterization trick, 2015
Diederik P. Kingma, Tim Salimans, and Max Welling · 2015
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Complexity-theoretic foundations of quantum supremacy experiments, 2016
Scott Aaronson and Lijie Chen · 2016
Earlier work this paper cites.
Supervised Learning with Quantum-Inspired Tensor Networks
E. Miles Stoudenmire and David J. Schwab · 2016
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The Cellular Automaton Interpretation of Quantum Mechanics
G. ;t Hooft · 2016
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Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd · 2017
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Yudong Cao, Gian Giacomo Guerreschi, and Alán Aspuru-Guzik · 2017
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Quantum machine learning: a classical perspective
Carlo Ciliberto, Mark Herbster, Alessand ro Davide Ialongo, Massimiliano Pontil, Andrea Rocchetto, Simone Severini, and Leonard Wossnig · 2017
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Markov chains and mixing times , volume 107
David A Levin and Yuval Peres · 2017
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Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design
Yoav Levine, David Yakira, Nadav Cohen, and Amnon Shashua · 2017
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Machine Learning by Unitary Tensor Network of Hierarchical Tree Structure
Ding Liu, Shi-Ju Ran, Peter Wittek, Cheng Peng, Raul Blázquez García, Gang Su, and Maciej Lewenstein · 2017
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Learning discrete weights using the local reparameterization trick
Oran Shayer, Dan Levi, and Ethan Fetaya · 2017
Efficient Learning for Deep Quantum Neural Networks
Kerstin Beer, Dmytro Bondarenko, Terry Farrelly, Tobias J. Osborne, Robert Salzmann, and Ramona Wolf · 2019
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Quantum convolutional neural networks
Iris Cong, Soonwon Choi, and Mikhail D. Lukin · 2019
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An initialization strategy for addressing barren plateaus in parametrized quantum circuits
Edward Grant, Leonard Wossnig, Mateusz Ostaszewski, and Marcello Benedetti · 2019
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Quantum algorithms for deep convolutional neural networks, 2019
Iordanis Kerenidis, Jonas Landman, and Anupam Prakash · 2019
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Quantum entanglement in deep learning architectures
Yoav Levine, Or Sharir, Nadav Cohen, and Amnon Shashua · 2019
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Quantum algorithms for feedforward neural networks
Jonathan Allcock, Chang-Yu Hsieh, Iordanis Kerenidis, and Shengyu Zhang · 2018
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Neural Ordinary Differential Equations
Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud · 2018
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Classification with Quantum Neural Networks on Near Term Processors
Edward Farhi and Hartmut Neven · 2018
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Hierarchical quantum classifiers
Edward Grant, Marcello Benedetti, Shuxiang Cao, Andrew Hallam, Joshua Lockhart, Vid Stojevic, Andrew G Green, and Simone Severini · 2018
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Probabilistic binary neural networks, 2018
Jorn W. T. Peters and Max Welling · 2018
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A Universal Training Algorithm for Quantum Deep Learning
Guillaume Verdon, Jason Pye, and Michael Broughton · 2018
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Quantum supremacy using a programmable superconducting processor
Frank Arute, Kunal Arya, Ryan Babbush, Dave Bacon, Joseph C Bardin, Rami Barends, Rupak Biswas, Sergio Boixo, Fernando GSL Brandao, David A Buell, et al · 2019
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Ewin Tang · 2019
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Recurrent quantum neural networks
Johannes Bausch · 2020
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Supervised learning with projected entangled pair states, 2020
Song Cheng, Lei Wang, and Pan Zhang · 2020
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Power of data in quantum machine learning, 2020
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A rigorous and robust quantum speed-up in supervised machine learning
Yunchao Liu, Srinivasan Arunachalam, and Kristan Temme · 2020
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Towards quantum machine learning with tensor networks
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