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Quantum machine learning (QML) has been identified as one of the key fields that could reap advantages from near-term quantum devices, next to optimization and quantum chemistry.
Learning from delayed rewards
Christopher John Cornish Hellaby Watkins · 1989
Earlier work this paper cites.
Self-supervised Learning by Reinforcement and Artificial Neural Networks
Long-Ji Lin · 1992
Earlier work this paper cites.
Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 1998
Earlier work this paper cites.
Policy gradient methods for reinforcement learning with function approximation
Richard S Sutton, David A McAllester, Satinder P Singh, Yishay Mansour, et al · 1999
Earlier work this paper cites.
Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer
Peter W Shor · 1999
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Actor-critic algorithms
Vijay R Konda and John N Tsitsiklis · 2000
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Convergence of q-learning: A simple proof
Francisco S Melo · 2001
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Variance reduction techniques for gradient estimates in reinforcement learning
Evan Greensmith, Peter L Bartlett, and Jonathan Baxter · 2004
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Q-learning with linear function approximation
Francisco S Melo and M Isabel Ribeiro · 2007
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Comparison of different neural network architectures for digit image recognition
Hao Yu, Tiantian Xie, Michael Hamilton, and Bogdan Wilamowski · 2011
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Earlier work this paper cites.
Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
Earlier work this paper cites.
Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Cyclical learning rates for training neural networks
Leslie N Smith · 2017
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Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets
Abhinav Kandala, Antonio Mezzacapo, Kristan Temme, Maika Takita, Markus Brink, Jerry M Chow, and Jay M Gambetta · 2017
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Exponential improvements for quantum-accessible reinforcement learning
Vedran Dunjko, Yi-Kai Liu, Xingyao Wu, and Jacob M Taylor · 2017
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Quantum computing in the nisq era and beyond
John Preskill · 2018
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Quantum circuit learning
Kosuke Mitarai, Makoto Negoro, Masahiro Kitagawa, and Keisuke Fujii · 2018
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Classification with quantum neural networks on near term processors
Edward Farhi and Hartmut Neven · 2018
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Quantum boltzmann machine
Mohammad H Amin, Evgeny Andriyash, Jason Rolfe, Bohdan Kulchytskyy, and Roger Melko · 2018
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Quantum generative adversarial learning
Seth Lloyd and Christian Weedbrook · 2018
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Leslie N Smith · 2018
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Activation functions: Comparison of trends in practice and research for deep learning
Chigozie Nwankpa, Winifred Ijomah, Anthony Gachagan, and Stephen Marshall · 2018
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Neural network architectures and activation functions: A gaussian process approach
Sebastian Urban · 2018
Cited alongside, same era.
Barren plateaus in quantum neural network training landscapes
Jarrod R McClean, Sergio Boixo, Vadim N Smelyanskiy, Ryan Babbush, and Hartmut Neven · 2018
Cited alongside, same era.
Quantum reinforcement learning in continuous action space
Shaojun Wu, Shan Jin, Dingding Wen, and Xiaoting Wang · 2020
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A comparison of modern deep neural network architectures for energy spot price forecasting
F Cordoni · 2020
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Learning unitaries by gradient descent
Bobak Toussi Kiani, Seth Lloyd, and Reevu Maity · 2020
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Exploring entanglement and optimization within the hamiltonian variational ansatz
Roeland Wiersema, Cunlu Zhou, Yvette de Sereville, Juan Felipe Carrasquilla, Yong Baek Kim, and Henry Yuen · 2020
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Data re-uploading for a universal quantum classifier
Adrián Pérez-Salinas, Alba Cervera-Lierta, Elies Gil-Fuster, and José I Latorre · 2020
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Differentiable learning of quantum circuit born machines
Jin-Guo Liu and Lei Wang · 2018
Cited alongside, same era.
Quantum machine learning in feature hilbert spaces
Maria Schuld and Nathan Killoran · 2019
Cited alongside, same era.
Supervised learning with quantum-enhanced feature spaces
Vojtěch Havlíček, Antonio D Córcoles, Kristan Temme, Aram W Harrow, Abhinav Kandala, Jerry M Chow, and Jay M Gambetta · 2019
Cited alongside, same era.
Quantum generative adversarial networks for learning and loading random distributions
Christa Zoufal, Aurélien Lucchi, and Stefan Woerner · 2019
Cited alongside, same era.
Quantum wasserstein generative adversarial networks
Shouvanik Chakrabarti, Huang Yiming, Tongyang Li, Soheil Feizi, and Xiaodi Wu · 2019
Cited alongside, same era.
Dota 2 with large scale deep reinforcement learning
Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemyslaw Debiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Chris Hesse, et al · 2019
Cited alongside, same era.
Grandmaster level in starcraft ii using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al · 2019
Cited alongside, same era.
Tensorflow quantum: A software framework for quantum machine learning
Michael Broughton, Guillaume Verdon, Trevor McCourt, Antonio J Martinez, Jae Hyeon Yoo, Sergei V Isakov, Philip Massey, Murphy Yuezhen Niu, Ramin Halavati, Evan Peters, et al · 2020
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Noisy intermediate-scale quantum (nisq) algorithms
Kishor Bharti, Alba Cervera-Lierta, Thi Ha Kyaw, Tobias Haug, Sumner Alperin-Lea, Abhinav Anand, Matthias Degroote, Hermanni Heimonen, Jakob S Kottmann, Tim Menke, et al · 2021
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Variational quantum boltzmann machines
Christa Zoufal, Aurélien Lucchi, and Stefan Woerner · 2021
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Quantum enhancements for deep reinforcement learning in large spaces
Sofiene Jerbi, Lea M Trenkwalder, Hendrik Poulsen Nautrup, Hans J Briegel, and Vedran Dunjko · 2021
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Parametrized quantum policies for reinforcement learning
Sofiene Jerbi, Casper Gyurik, Simon Marshall, Hans Briegel, and Vedran Dunjko · 2021
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Review and comparison of commonly used activation functions for deep neural networks
Tomasz Szandała · 2021
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Cost function dependent barren plateaus in shallow parametrized quantum circuits
M Cerezo, Akira Sone, Tyler Volkoff, Lukasz Cincio, and Patrick J Coles · 2021
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Noise-induced barren plateaus in variational quantum algorithms
Samson Wang, Enrico Fontana, Marco Cerezo, Kunal Sharma, Akira Sone, Lukasz Cincio, and Patrick J Coles · 2021
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Layerwise learning for quantum neural networks
Andrea Skolik, Jarrod R McClean, Masoud Mohseni, Patrick van der Smagt, and Martin Leib · 2021
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Entanglement-induced barren plateaus
Carlos Ortiz Marrero, Mária Kieferová, and Nathan Wiebe · 2021
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Layer vqe: A variational approach for combinatorial optimization on noisy quantum computers
Xiaoyuan Liu, Anthony Angone, Ruslan Shaydulin, Ilya Safro, Yuri Alexeev, and Lukasz Cincio · 2021
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Effect of data encoding on the expressive power of variational quantum-machine-learning models
Maria Schuld, Ryan Sweke, and Johannes Jakob Meyer · 2021
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Training larger networks for deep reinforcement learning
Kei Ota, Devesh K Jha, and Asako Kanezaki · 2021
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A rigorous and robust quantum speed-up in supervised machine learning
Yunchao Liu, Srinivasan Arunachalam, and Kristan Temme · 2021
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Parameterized quantum circuits as machine learning models
Marcello Benedetti, Erika Lloyd, Stefan Sack, and Mattia Fiorentini · 2058
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Adaptive pruning-based optimization of parameterized quantum circuits
Sukin Sim, Jhonathan Romero Fontalvo, Jérôme F Gonthier, and Alexander A Kunitsa · 2058
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