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Quantum computing holds great potential for advancing the limitations of machine learning algorithms to handle higher dimensions of data and reduce overall training parameters in deep learning (DL) models.
J. Biamonte, P. Wittek, N. Pancotti, et al. “Quantum machine learning.” Nature , 549, 195–202 (2017). https://doi.org/10.1038/nature23474
2017
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2019
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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,” in IEEE Access , vol. 8, pp. 141007-141024, 2020, doi: 10.1109/ACCESS.2020.3010470
2020
Earlier work this paper cites.
K. Beer, D. Bondarenko, T. Farrelly, T. J. Osborne, R. Salzmann, D. Scheiermann, R. Wolf, “Training deep quantum neural networks.” Nat Commun , 11(1):808, 2020. doi: 10.1038/s41467-020-14454-2
2020
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2021
Earlier work this paper cites.
O. Lockwood and M. Si, “Playing Atari with Hybrid Quantum-Classical Reinforcement Learning.” NeurIPS 2020 Workshop on Pre-registration in Machine Learning, in Proceedings of Machine Learning Research 148:285-301, 2021
2021
Earlier work this paper cites.
Quantum reinforcement learning: the maze problem
N. Dalla Pozza, L. Buffoni, S. Martina, et al · 2022
Cited alongside, same era.
2022
Cited alongside, same era.
S. Lokes, C. S. J. Mahenthar, S. P. Kumaran, P. Sathyaprakash, V. Jayakumar, “Implementation of Quantum Deep Reinforcement Learning Using Variational Quantum Circuits,” 2022 International Conference on Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), Pune, India, 2022, pp. 1-4, doi: 10.1109/TQCEBT54229.2022.10041479
2022
Cited alongside, same era.
2022
Cited alongside, same era.
L. Kunczik, “Future Steps in Quantum Reinforcement Learning for Complex Scenarios.” In: Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context. Springer Vieweg, Wiesbaden, 2022
2022
Later among the works it cites.
D. Arthur and P. Date, “Hybrid Quantum-Classical Neural Networks,” 2022 IEEE International Conference on Quantum Computing and Engineering (QCE), Broomfield, CO, USA, 2022, pp. 49-55, doi: 10.1109/QCE53715.2022.00023
2022
Later among the works it cites.
N. Schetakis, D. Aghamalyan, P. Griffin, et al. “Review of some existing QML frameworks and novel hybrid classical–quantum neural networks realising binary classification for the noisy datasets.” Sci Rep , 12, 11927 (2022)
2022
Later among the works it cites.
A. Sannia, A. Giordano, N. L. Gullo, et al. “A hybrid classical-quantum approach to speed-up Q-learning.” Sci Rep , 13, 3913 (2023). https://doi.org/10.1038/s41598-023-30990-5
2023
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L. Kunczik, “Quantum Reinforcement Learning—Connecting Reinforcement Learning and Quantum Computing.” In: Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context. Springer Vieweg, Wiesbaden, 2022
2022
Cited alongside, same era.
L. Kunczik, “Evaluating Quantum REINFORCE on IBM’s Quantum Hardware.” In: Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context. Springer Vieweg, Wiesbaden, 2022
2022
Cited alongside, same era.
S. Park, D. K. Park, J. K. K. Rhee, “Variational quantum approximate support vector machine with inference transfer.” Sci Rep , 13, 3288 (2023)
2023
Closest in time.