An efficient and scalable variational quantum circuits approach for deep reinforcement learning-Reference-Cited by-同舟云学术

An efficient and scalable variational quantum circuits approach for deep reinforcement learning

Published:2023-08-02 Issue:8 Volume:22 Page:
ISSN:1573-1332
Container-title:Quantum Information Processing
language:en
Short-container-title:Quantum Inf Process

Author:

Bar Niyazi Furkan^ORCID,Yetis Hasan^ORCID,Karakose Mehmet^ORCID

Funder

Türkiye Bilimsel ve Teknolojik Araştirma Kurumu

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Modeling and Simulation,Signal Processing,Theoretical Computer Science,Statistical and Nonlinear Physics,Electronic, Optical and Magnetic Materials

Link

https://link.springer.com/content/pdf/10.1007/s11128-023-04051-9.pdf

Reference22 articles.

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2. Francois-Lavet, V., Henderson, P., Islam, R., Bellemare, M.G., Pineau, J.: An introduction to deep reinforcement learning. Found Trends Mach Learn. 11(3–4), 219–354 (2018). https://doi.org/10.1561/2200000071

3. Kwak Y., Yun W. J., Jung S., Kim J.: Quantum neural networks: concepts, applications, and challenges, (2021)

4. Chen S. Y.-C., Yang C.-H. H., Qi J., Chen P.-Y., Ma X., Goan H.-S.: Variational quantum circuits for deep reinforcement learning. Jun. 2019.

5. Hernández H. I. G., Ruiz R. T., and Sun G.-H.: Image classification via quantum machine learning. (2020)

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