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Recent advances in quantum computing (QC) and machine learning (ML) have drawn significant attention to the development of quantum machine learning (QML).
“Long short-term memory,”
Sepp Hochreiter and Jürgen Schmidhuber, · 1997
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“Quantum reinforcement learning,”
Daoyi Dong, Chunlin Chen, Hanxiong Li, and Tzyh-Jong Tarn, · 2008
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“Quantum computation and quantum information,”
Michael A Nielsen and Isaac L Chuang, · 2010
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“A quantum approximate optimization algorithm,”
Edward Farhi, Jeffrey Goldstone, and Sam Gutmann, · 2014
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“Entanglement in a quantum annealing processor,”
Trevor Lanting, Anthony J Przybysz, A Yu Smirnov, Federico M Spedalieri, Mohammad H Amin, Andrew J Berkley, Richard Harris, Fabio Altomare, Sergio Boixo, Paul Bunyk, et al., · 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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“Deep recurrent q-learning for partially observable mdps,”
Matthew Hausknecht and Peter Stone, · 2015
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“The theory of variational hybrid quantum-classical algorithms,”
Jarrod R McClean, Jonathan Romero, Ryan Babbush, and Alán Aspuru-Guzik, · 2016
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba, · 2016
Earlier work this paper cites.
“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, et al., · 2017
Earlier work this paper cites.
“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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“Quantum computing in the nisq era and beyond,”
John Preskill, · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto, · 2018
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“The expressive power of parameterized quantum circuits,”
Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, and Dacheng Tao, · 2018
Earlier work this paper cites.
“Quantum circuit learning,”
Kosuke Mitarai, Makoto Negoro, Masahiro Kitagawa, and Keisuke Fujii, · 2018
Cited alongside, same era.
“Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms,”
Sukin Sim, Peter D Johnson, and Alán Aspuru-Guzik, · 2019
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.
“Hybrid quantum-classical ulam-von neumann linear solver-based quantum dynamic programing algorithm,”
Chih-Chieh CHEN, Koudai SHIBA, Masaru SOGABE, Katsuyoshi SAKAMOTO, and Tomah SOGABE, · 2020
Cited alongside, same era.
“Quantum reinforcement learning in continuous action space,”
“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, et al., · 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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“Quantum deep reinforcement learning for robot navigation tasks,”
Dirk Heimann, Hans Hohenfeld, Felix Wiebe, and Frank Kirchner, · 2022
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“Unentangled quantum reinforcement learning agents in the openai gym,”
Jen-Yueh Hsiao, Yuxuan Du, Wei-Yin Chiang, Min-Hsiu Hsieh, and Hsi-Sheng Goan, · 2022
Closest in time.
“Hybrid actor-critic algorithm for quantum reinforcement learning at cern beam lines,”
Michael Schenk, Elías F Combarro, Michele Grossi, Verena Kain, Kevin Shing Bruce Li, Mircea-Marian Popa, and Sofia Vallecorsa, · 2022
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Shaojun Wu, Shan Jin, Dingding Wen, and Xiaoting Wang, · 2020
Cited alongside, same era.
“Recurrent quantum neural networks,”
Johannes Bausch, · 2020
Cited alongside, same era.
“Variational quantum policies for reinforcement learning,”
Sofiene Jerbi, Casper Gyurik, Simon Marshall, Hans J Briegel, and Vedran Dunjko, · 2021
Cited alongside, same era.
“Variational quantum soft actor-critic,”
Qingfeng Lan, · 2021
Cited alongside, same era.
“The power of quantum neural networks,”
Amira Abbas, David Sutter, Christa Zoufal, Aurélien Lucchi, Alessio Figalli, and Stefan Woerner, · 2021
Cited alongside, same era.
“Qtn-vqc: An end-to-end learning framework for quantum neural networks,”
Jun Qi, Chao-Han Huck Yang, and Pin-Yu Chen, · 2021
Cited alongside, same era.
“Federated quantum machine learning,”
Samuel Yen-Chi Chen and Shinjae Yoo, · 2021
Cited alongside, same era.
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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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“Quantum multi-agent reinforcement learning via variational quantum circuit design,”
Won Joon Yun, Yunseok Kwak, Jae Pyoung Kim, Hyunhee Cho, Soyi Jung, Jihong Park, and Joongheon Kim, · 2022
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“Variational quantum reinforcement learning via evolutionary optimization,”
Samuel Yen-Chi Chen, Chih-Min Huang, Chia-Wei Hsing, Hsi-Sheng Goan, and Ying-Jer Kao, · 2022
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“Generalization in quantum machine learning from few training data,”
Matthias C Caro, Hsin-Yuan Huang, Marco Cerezo, Kunal Sharma, Andrew Sornborger, Lukasz Cincio, and Patrick J Coles, · 2022
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“Quantum federated learning with quantum data,”
Mahdi Chehimi and Walid Saad, · 2022
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“When bert meets quantum temporal convolution learning for text classification in heterogeneous computing,”
Chao-Han Huck Yang, Jun Qi, Samuel Yen-Chi Chen, Yu Tsao, and Pin-Yu Chen, · 2022
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“The dawn of quantum natural language processing,”
Riccardo Di Sipio, Jia-Hong Huang, Samuel Yen-Chi Chen, Stefano Mangini, and Marcel Worring, · 2022
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“Quantum long short-term memory,”
Samuel Yen-Chi Chen, Shinjae Yoo, and Yao-Lung L Fang, · 2022
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