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Quantum computing has shown the potential to substantially speed up machine learning applications, in particular for supervised and unsupervised learning.
A fast quantum mechanical algorithm for database search
Lov K. Grover · 1996
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An approach to fuzzy control of nonlinear systems: stability and design issues
Hua O. Wang, Kazuo Tanaka, and Michael F. Griffin · 1996
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A quantum algorithm for finding the minimum
Christoph Dürr and Peter Høyer · 1996
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Quantum computation and quantum information, 2002
Michael A Nielsen and Isaac Chuang · 2002
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Least-squares policy iteration
Michail G. Lagoudakis and Ronald E. Parr · 2003
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Error bounds for approximate policy iteration
Rémi Munos · 2003
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Reinforcement learning: An introduction
Richard S. Sutton and Andrew G. Barto · 2005
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Quantum reinforcement learning
Daoyi Dong, Chunlin Chen, Hanxiong Li, and Tzyh-Jong Tarn · 2008
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Approximate policy iteration: a survey and some new methods
Dimitri P. Bertsekas · 2011
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Value function approximation in reinforcement learning using the fourier basis
George Konidaris, Sarah Osentoski, and Philip Thomas · 2011
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Approximate modified policy iteration
Bruno Scherrer, Victor Gabillon, Mohammad Ghavamzadeh, and Matthieu Geist · 2012
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, D. Erhan, Ian J. Goodfellow, and Rob Fergus · 2014
Cited alongside, same era.
Quantum principal component analysis
Seth Lloyd, Masoud Mohseni, and Patrick Rebentrost · 2014
Cited alongside, same era.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin A. Riedmiller, Andreas Fidjeland, Georg Ostrovski, Stig Petersen, Charlie Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
Cited alongside, same era.
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
Cited alongside, same era.
Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy P. Lillicrap, Fan Hui, L. Sifre, George van den Driessche, Thore Graepel, and Demis Hassabis · 2017
Quantum algorithms for deep convolutional neural networks
Iordanis Kerenidis, Jonas Landman, and Anupam Prakash · 2019
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Quantum algorithms for solving dynamic programming problems
Pooya Ronagh · 2019
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Reinforcement learning and optimal control
Dimitri P. Bertsekas · 2019
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Quantum gradient descent for linear systems and least squares
Iordanis Kerenidis and Anupam Prakash · 2020
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Variational quantum circuits for deep reinforcement learning
Samuel Yen-Chi Chen, Chao-Han Huck Yang, Jun Qi, Pin-Yu Chen, Xiaoli Ma, and Hsi-Sheng Goan · 2020
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Cited alongside, same era.
Quantum recommendation systems
Iordanis Kerenidis and Anupam Prakash · 2017
Cited alongside, same era.
Quantum machine learning
Jacob D. Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd · 2017
Cited alongside, same era.
Quantum generative adversarial learning
Seth Lloyd and Christian Weedbrook · 2018
Cited alongside, same era.
Quantum gradient estimation and its application to quantum reinforcement learning
Arjan Cornelissen · 2018
Cited alongside, same era.
Shantanav Chakraborty, András Gilyén, and Stacey Jeffery · 2018
Cited alongside, same era.
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 G. S. L. Brandão, David A. Buell, Brian Burkett, Yu Chen, Zijun Chen, Benjamin Chiaro, Roberto Collins, William Courtney, Andrew Dunsworth, Edward Farhi, Brooks Foxen, Austin G. Fowler, Craig Gidney, Marissa Giustina, Rob Graff, Keith Guerin, Steve Habegger, Matthew P. Harrigan, Michael J. Hartmann, Alan K. Ho, Markus Hoffmann, Trent Huang, T. Humble, Sergei V. Isakov, Evan Jeffrey, Zhang Jiang, Dvir Kafri, Kostyantyn Kechedzhi, Julian Kelly, Paul Klimov, Sergey Knysh, Alexander N. Korotkov, Fedor Kostritsa, David Landhuis, Mike Lindmark, Erik Lucero, Dmitry I. Lyakh, Salvatore Mandrà, Jarrod R. McClean, Matthew J. McEwen, Anthony Megrant, Xiao Mi, Kristel Michielsen, Masoud Mohseni, Josh Mutus, Ofer Naaman, Matthew Neeley, Charles J. Neill, Murphy Yuezhen Niu, Eric P. Ostby, Andre Petukhov, John C. Platt, Chris Quintana, Eleanor Gilbert Rieffel, Pedram Roushan, Nicholas C Rubin, Daniel Thomas Sank, Kevin J Satzinger, Vadim N. Smelyanskiy, Kevin J. Sung, Matthew D Trevithick, Amit Vainsencher, Benjamin Villalonga, Theodore White, Z. Jamie Yao, P. Yeh, Adam Zalcman, Hartmut Neven, and John M. Martinis
Cited in the paper.
Optimizing quantum optimization algorithms via faster quantum gradient computation
András Gilyén, Srinivasan Arunachalam, and Nathan Wiebe
Cited in the paper.
Owen Lockwood and M. Si · 2020
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Classical and quantum algorithms for orthogonal neural networks
Iordanis Kerenidis, Jonas Landman, and Natansh Mathur · 2021
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Quantum agents in the gym: a variational quantum algorithm for deep q-learning
Andrea Skolik, Sofiène Jerbi, and Vedran Dunjko · 2021
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Variational quantum policies for reinforcement learning
Sofiène Jerbi, Casper Gyurik, Simon Marshall, Hans J. Briegel, and Vedran Dunjko · 2021
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Quantum algorithms for reinforcement learning with a generative model
Daochen Wang, Aarthi Sundaram, Robin Kothari, Ashish Kapoor, and Martin Rötteler · 2021
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