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1992
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D. Dong, C. Chen, H. Li, and T.-J. Tarn, “Quantum reinforcement learning,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
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V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu, “Asynchronous methods for deep reinforcement learning,” in International conference on machine learning
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G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba, “Openai gym,” arXiv preprint arXiv:1606.01540
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2016
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M. Chevalier-Boisvert, L. Willems, and S. Pal, “Minimalistic gridworld environment for openai gym.” https://github.com/maximecb/gym-minigrid , 2018
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V. Bergholm, J. Izaac, M. Schuld, C. Gogolin, C. Blank, K. McKiernan, and N. Killoran, “Pennylane: Automatic differentiation of hybrid quantum-classical computations,” arXiv preprint arXiv:1811.04968
Original
2018
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S. Y.-C. Chen, C.-H. H. Yang, J. Qi, P.-Y. Chen, X. Ma, and H.-S. Goan, “Variational quantum circuits for deep reinforcement learning,” IEEE Access
2020
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O. Lockwood and M. Si, “Reinforcement learning with quantum variational circuit,” in Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment
2020
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C.-C. CHEN, K. SHIBA, M. SOGABE, K. SAKAMOTO, and T. SOGABE, “Hybrid quantum-classical ulam-von neumann linear solver-based quantum dynamic programing algorithm,” Proceedings of the Annual Conference of JSAI
2020
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S. Wu, S. Jin, D. Wen, and X. Wang, “Quantum reinforcement learning in continuous action space,” arXiv preprint arXiv:2012.10711
Original
2020
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Similar

M. Cerezo, A. Arrasmith, R. Babbush, S. C. Benjamin, S. Endo, K. Fujii, J. R. McClean, K. Mitarai, X. Yuan, L. Cincio, et al
2021
Cited alongside, same era.
S. Jerbi, C. Gyurik, S. Marshall, H. J. Briegel, and V. Dunjko, “Variational quantum policies for reinforcement learning,” arXiv preprint arXiv:2103.05577
Original
2021
Cited alongside, same era.
Q. Lan, “Variational quantum soft actor-critic,” arXiv preprint arXiv:2112.11921
Original
2021
Cited alongside, same era.
S. Y.-C. Chen, C.-M. Huang, C.-W. Hsing, and Y.-J. Kao, “An end-to-end trainable hybrid classical-quantum classifier,” Machine Learning: Science and Technology
2021
Cited alongside, same era.
J. Qi, C.-H. H. Yang, and P.-Y. Chen, “Qtn-vqc: An end-to-end learning framework for quantum neural networks,” arXiv preprint arXiv:2110.03861

Then

S. Jerbi, A. Cornelissen, M. Ozols, and V. Dunjko, “Quantum policy gradient algorithms,” arXiv preprint arXiv:2212.09328
Original
2022
Later among the works it cites.
N. Meyer, D. D. Scherer, A. Plinge, C. Mutschler, and M. J. Hartmann, “Quantum policy gradient algorithm with optimized action decoding,” arXiv preprint arXiv:2212.06663
Original
2022
Later among the works it cites.
M. Schenk, E. F. Combarro, M. Grossi, V. Kain, K. S. B. Li, M.-M. Popa, and S. Vallecorsa, “Hybrid actor-critic algorithm for quantum reinforcement learning at cern beam lines,” arXiv preprint arXiv:2209.11044
Original
2022
Later among the works it cites.
A. Acuto, P. Barillà, L. Bozzolo, M. Conterno, M. Pavese, and A. Policicchio, “Variational quantum soft actor-critic for robotic arm control,” arXiv preprint arXiv:2212.11681
Original
2022
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

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Reading the bibliography…

2023

Asynchronous training of quantum reinforcement learning

Chen, Samuel Yen-Chi

Understand

The development of quantum machine learning (QML) has received a lot of interest recently thanks to developments in both quantum computing (QC) and machine learning (ML).

One of the ML paradigms that can be utilized to address challenging sequential decision-making issues is reinforcement learning (RL).
It has been demonstrated that classical RL can successfully complete many difficult tasks.
A leading method of building quantum RL agents relies on the variational quantum circuits (VQC).

Built on

R. J. Williams, “Simple statistical gradient-following algorithms for connectionist reinforcement learning,” Machine learning
1992
Earlier work this paper cites.
D. Dong, C. Chen, H. Li, and T.-J. Tarn, “Quantum reinforcement learning,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
2008
Earlier work this paper cites.
M. A. Nielsen and I. L. Chuang, “Quantum computation and quantum information,” 2010
2010
Earlier work this paper cites.
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al
2015
Earlier work this paper cites.
V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu, “Asynchronous methods for deep reinforcement learning,” in International conference on machine learning
2016
Earlier work this paper cites.
G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba, “Openai gym,” arXiv preprint arXiv:1606.01540
Original
2016
Earlier work this paper cites.
D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton, et al
2017
Earlier work this paper cites.
J. Preskill, “Quantum computing in the nisq era and beyond,” Quantum
2018
Earlier work this paper cites.
MIT press, 2018
R. S. Sutton and A. G. Barto, Reinforcement learning: An introduction · 2018
Earlier work this paper cites.
K. Mitarai, M. Negoro, M. Kitagawa, and K. Fujii, “Quantum circuit learning,” Physical Review A
2018
Earlier work this paper cites.
M. Chevalier-Boisvert, L. Willems, and S. Pal, “Minimalistic gridworld environment for openai gym.” https://github.com/maximecb/gym-minigrid , 2018
2018
Earlier work this paper cites.
V. Bergholm, J. Izaac, M. Schuld, C. Gogolin, C. Blank, K. McKiernan, and N. Killoran, “Pennylane: Automatic differentiation of hybrid quantum-classical computations,” arXiv preprint arXiv:1811.04968
Original
2018
Earlier work this paper cites.
S. Y.-C. Chen, C.-H. H. Yang, J. Qi, P.-Y. Chen, X. Ma, and H.-S. Goan, “Variational quantum circuits for deep reinforcement learning,” IEEE Access
2020
Earlier work this paper cites.
O. Lockwood and M. Si, “Reinforcement learning with quantum variational circuit,” in Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment
2020
Earlier work this paper cites.
C.-C. CHEN, K. SHIBA, M. SOGABE, K. SAKAMOTO, and T. SOGABE, “Hybrid quantum-classical ulam-von neumann linear solver-based quantum dynamic programing algorithm,” Proceedings of the Annual Conference of JSAI
2020
Earlier work this paper cites.
S. Wu, S. Jin, D. Wen, and X. Wang, “Quantum reinforcement learning in continuous action space,” arXiv preprint arXiv:2012.10711
Original
2020
Earlier work this paper cites.

Similar

M. Cerezo, A. Arrasmith, R. Babbush, S. C. Benjamin, S. Endo, K. Fujii, J. R. McClean, K. Mitarai, X. Yuan, L. Cincio, et al
2021
Cited alongside, same era.
S. Jerbi, C. Gyurik, S. Marshall, H. J. Briegel, and V. Dunjko, “Variational quantum policies for reinforcement learning,” arXiv preprint arXiv:2103.05577
Original
2021
Cited alongside, same era.
Q. Lan, “Variational quantum soft actor-critic,” arXiv preprint arXiv:2112.11921
Original
2021
Cited alongside, same era.
S. Y.-C. Chen, C.-M. Huang, C.-W. Hsing, and Y.-J. Kao, “An end-to-end trainable hybrid classical-quantum classifier,” Machine Learning: Science and Technology
2021
Cited alongside, same era.
J. Qi, C.-H. H. Yang, and P.-Y. Chen, “Qtn-vqc: An end-to-end learning framework for quantum neural networks,” arXiv preprint arXiv:2110.03861

Then

S. Jerbi, A. Cornelissen, M. Ozols, and V. Dunjko, “Quantum policy gradient algorithms,” arXiv preprint arXiv:2212.09328
Original
2022
Later among the works it cites.
N. Meyer, D. D. Scherer, A. Plinge, C. Mutschler, and M. J. Hartmann, “Quantum policy gradient algorithm with optimized action decoding,” arXiv preprint arXiv:2212.06663
Original
2022
Later among the works it cites.
M. Schenk, E. F. Combarro, M. Grossi, V. Kain, K. S. B. Li, M.-M. Popa, and S. Vallecorsa, “Hybrid actor-critic algorithm for quantum reinforcement learning at cern beam lines,” arXiv preprint arXiv:2209.11044
Original
2022
Later among the works it cites.
A. Acuto, P. Barillà, L. Bozzolo, M. Conterno, M. Pavese, and A. Policicchio, “Variational quantum soft actor-critic for robotic arm control,” arXiv preprint arXiv:2212.11681
Original
2022
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Original

2021

Cited alongside, same era.

S. Y.-C. Chen and S. Yoo, “Federated quantum machine learning,” Entropy

2021

Cited alongside, same era.

C.-H. H. Yang, J. Qi, S. Y.-C. Chen, P.-Y. Chen, S. M. Siniscalchi, X. Ma, and C.-H. Lee, “Decentralizing feature extraction with quantum convolutional neural network for automatic speech recognition,” in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

2021

Cited alongside, same era.

K. Bharti, A. Cervera-Lierta, T. H. Kyaw, T. Haug, S. Alperin-Lea, A. Anand, M. Degroote, H. Heimonen, J. S. Kottmann, T. Menke, et al

2022

Cited alongside, same era.

A. Skolik, S. Jerbi, and V. Dunjko, “Quantum agents in the gym: a variational quantum algorithm for deep q-learning,” Quantum

2022

Cited alongside, same era.

J.-Y. Hsiao, Y. Du, W.-Y. Chiang, M.-H. Hsieh, and H.-S. Goan, “Unentangled quantum reinforcement learning agents in the openai gym,” arXiv preprint arXiv:2203.14348

Original

2022

Cited alongside, same era.

N. Meyer, C. Ufrecht, M. Periyasamy, D. D. Scherer, A. Plinge, and C. Mutschler, “A survey on quantum reinforcement learning,” arXiv preprint arXiv:2211.03464

Original

2022

Cited alongside, same era.

S. Wiedemann, D. Hein, S. Udluft, and C. Mendl, “Quantum policy iteration via amplitude estimation and grover search–towards quantum advantage for reinforcement learning,” arXiv preprint arXiv:2206.04741

Original

2022

Cited alongside, same era.

A. Sequeira, L. P. Santos, and L. S. Barbosa, “Variational quantum policy gradients with an application to quantum control,” arXiv preprint arXiv:2203.10591

Original

2022