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Variational quantum machine learning algorithms have become the focus of recent research on how to utilize near-term quantum devices for machine learning tasks.
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An analysis of noise in recurrent neural networks: convergence and generalization
Kam-Chuen Jim, C Lee Giles, and Bill G Horne · 1996
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Using confidence bounds for exploitation-exploration trade-offs
Peter Auer · 2002
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Michael A. Nielsen and Isaac L. Chuang · 2010
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Theoretical Statistics: Topics for a Core Course
Robert W. Keener (auth.) · 2010
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Practical variational inference for neural networks
Alex Graves · 2011
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Characterizing quantum gates via randomized benchmarking
Easwar Magesan, Jay M. Gambetta, and Joseph Emerson · 2012
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Speech recognition with deep recurrent neural networks
Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton · 2013
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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A quantum approximate optimization algorithm
Edward Farhi, Jeffrey Goldstone, and Sam Gutmann · 2014
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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
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Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
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Neural combinatorial optimization with reinforcement learning
Irwan Bello, Hieu Pham, Quoc V Le, Mohammad Norouzi, and Samy Bengio · 2016
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Deep reinforcement learning with double q-learning
Hado Van Hasselt, Arthur Guez, and David Silver · 2016
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Regularizing deep neural networks by noise: Its interpretation and optimization
Hyeonwoo Noh, Tackgeun You, Jonghwan Mun, and Bohyung Han · 2017
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Mitigating adversarial effects through randomization
Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, and Alan Yuille · 2017
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Adversarial attacks on neural network policies
Sandy Huang, Nicolas Papernot, Ian Goodfellow, Yan Duan, and Pieter Abbeel · 2017
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Delving into adversarial attacks on deep policies
Jernej Kos and Dawn Song · 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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Symbolic integration with respect to the haar measure on the unitary groups
Z. Puchała and J.A. Miszczak · 2017
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Towards sample efficient reinforcement learning
Yang Yu · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Quantum circuit learning
K. Mitarai, M. Negoro, M. Kitagawa, and K. Fujii · 2018
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Barren plateaus in quantum neural network training landscapes
Jarrod R. McClean, Sergio Boixo, Vadim N. Smelyanskiy, Ryan Babbush, and Hartmut Neven · 2018
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Efficient classical simulation of noisy quantum computation
Xun Gao and Luming Duan · 2018
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Adversarial examples are a natural consequence of test error in noise
Justin Gilmer, Nicolas Ford, Nicholas Carlini, and Ekin Cubuk · 2019
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Mahabubul Alam, Abdullah Ash-Saki, and Swaroop Ghosh · 2019
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Introduction to multi-armed bandits
Aleksandrs Slivkins et al · 2019
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Evaluating analytic gradients on quantum hardware
Maria Schuld, Ville Bergholm, Christian Gogolin, Josh Izaac, and Nathan Killoran · 2019
Cited alongside, same era.
Three-qubit randomized benchmarking
David C. McKay, Sarah Sheldon, John A. Smolin, Jerry M. Chow, and Jay M. Gambetta · 2019
Cited alongside, same era.
RTNI—a symbolic integrator for haar-random tensor networks
Motohisa Fukuda, Robert König, and Ion Nechita · 2019
Cited alongside, same era.
Noise-resilient variational hybrid quantum-classical optimization
Laura Gentini, Alessandro Cuccoli, Stefano Pirandola, Paola Verrucchi, and Leonardo Banchi · 2020
Higher order derivatives of quantum neural networks with barren plateaus
M Cerezo and Patrick J Coles · 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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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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Simulations of quantum circuits with approximate noise using qsim and cirq, 2021
Sergei V. Isakov, Dvir Kafri, Orion Martin, Catherine Vollgraff Heidweiller, Wojciech Mruczkiewicz, Matthew P. Harrigan, Nicholas C. Rubin, Ross Thomson, Michael Broughton, Kevin Kissell, Evan Peters, Erik Gustafson, Andy C. Y. Li, Henry Lamm, Gabriel Perdue, Alan K. Ho, Doug Strain, and Sergio Boixo · 2021
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Simple mitigation of global depolarizing errors in quantum simulations
Joseph Vovrosh, Kiran E. Khosla, Sean Greenaway, Christopher Self, M. S. Kim, and Johannes Knolle · 2021
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Cited alongside, same era.
Adversarial examples in deep neural networks: An overview
Emilio Rafael Balda, Arash Behboodi, and Rudolf Mathar · 2020
Cited alongside, same era.
Robust data encodings for quantum classifiers
Ryan LaRose and Brian Coyle · 2020
Cited alongside, same era.
Reinforcement learning with perturbed rewards
Jingkang Wang, Yang Liu, and Bo Li · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Reinforcement learning with quantum variational circuit
Owen Lockwood and Mei Si · 2020
Cited alongside, same era.
Quantum reinforcement learning in continuous action space
Shaojun Wu, Shan Jin, Dingding Wen, and Xiaoting Wang · 2020
Cited alongside, same era.
Limitations of optimization algorithms on noisy quantum devices
Daniel Stilck França and Raul Garcia-Patron · 2021
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Can error mitigation improve trainability of noisy variational quantum algorithms?, 2021
Samson Wang, Piotr Czarnik, Andrew Arrasmith, M. Cerezo, Lukasz Cincio, and Patrick J. Coles · 2021
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Characterizing the loss landscape of variational quantum circuits
Patrick Huembeli and Alexandre Dauphin · 2021
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Noisy intermediate-scale quantum 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, Wai-Keong Mok, Sukin Sim, Leong-Chuan Kwek, and Alán Aspuru-Guzik · 2022
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Noise can be helpful for variational quantum algorithms
Junyu Liu, Frederik Wilde, Antonio Anna Mele, Liang Jiang, and Jens Eisert · 2022
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Quantum agents in the gym: a variational quantum algorithm for deep q-learning
Andrea Skolik, Sofiene Jerbi, and Vedran Dunjko · 2022
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Variational quantum policy gradients with an application to quantum control
André Sequeira, Luis Paulo Santos, and Luís Soares Barbosa · 2022
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Uncovering instabilities in variational-quantum deep q-networks
Maja Franz, Lucas Wolf, Maniraman Periyasamy, Christian Ufrecht, Daniel D Scherer, Axel Plinge, Christopher Mutschler, and Wolfgang Mauerer · 2022
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Equivariant quantum circuits for learning on weighted graphs
Andrea Skolik, Michele Cattelan, Sheir Yarkoni, Thomas Bäck, and Vedran Dunjko · 2022
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Code that was used for training of noisy quantum agents
Andrea Skolik and Stefano Mangini · 2022
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https://github.com/openai/gym/wiki
Openai gym · 2022
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https://www.tensorflow.org/quantum/tutorials/quantum_reinforcement_learning
Tensorflow quantum rl tutorial · 2022
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Connecting ansatz expressibility to gradient magnitudes and barren plateaus
Zoë Holmes, Kunal Sharma, M. Cerezo, and Patrick J. Coles · 2022
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Documentation of depolarizing channel in cirq
Google Inc · 2022
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Scalable randomized benchmarking of quantum computers using mirror circuits
Timothy Proctor, Stefan Seritan, Kenneth Rudinger, Erik Nielsen, Robin Blume-Kohout, and Kevin Young · 2022
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Implementing fault-tolerant entangling gates on the five-qubit code and the color code, 2022
C. Ryan-Anderson, N. C. Brown, M. S. Allman, B. Arkin, G. Asa-Attuah, C. Baldwin, J. Berg, J. G. Bohnet, S. Braxton, N. Burdick, J. P. Campora, A. Chernoguzov, J. Esposito, B. Evans, D. Francois, J. P. Gaebler, T. M. Gatterman, J. Gerber, K. Gilmore, D. Gresh, A. Hall, A. Hankin, J. Hostetter, D. Lucchetti, K. Mayer, J. Myers, B. Neyenhuis, J. Santiago, J. Sedlacek, T. Skripka, A. Slattery, R. P. Stutz, J. Tait, R. Tobey, G. Vittorini, J. Walker, and D. Hayes · 2022
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https://quantum-computing.ibm.com/ , 2022
Ibmquantum · 2022
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Quantum volume in practice: What users can expect from nisq devices
Elijah Pelofske, Andreas Bärtschi, and Stephan Eidenbenz · 2022
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IBM Quantum Experience
IBM Quantum Experience · 2022
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Mitiq: A software package for error mitigation on noisy quantum computers
Ryan LaRose, Andrea Mari, Sarah Kaiser, Peter J. Karalekas, Andre A. Alves, Piotr Czarnik, Mohamed El Mandouh, Max H. Gordon, Yousef Hindy, Aaron Robertson, Purva Thakre, Misty Wahl, Danny Samuel, Rahul Mistri, Maxime Tremblay, Nick Gardner, Nathaniel T. Stemen, Nathan Shammah, and William J. Zeng · 2022
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Testing platform-independent quantum error mitigation on noisy quantum computers, 2022
Vincent Russo, Andrea Mari, Nathan Shammah, Ryan LaRose, and William J. Zeng · 2022
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