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Quantum Generative Adversarial Network: A Survey

Tong Li1, Shibin Zhang1, *, Jinyue Xia2

1 School of Cybersecurity, Chengdu University of Information Technology, Chengdu, 610225, China.
2 International Business Machines Corporation, New York, 14201, USA.

* Corresponding Author: Shibin Zhang. Email: email.

Computers, Materials & Continua 2020, 64(1), 401-438. https://doi.org/10.32604/cmc.2020.010551

Abstract

Generative adversarial network (GAN) is one of the most promising methods for unsupervised learning in recent years. GAN works via adversarial training concept and has shown excellent performance in the fields image synthesis, image super-resolution, video generation, image translation, etc. Compared with classical algorithms, quantum algorithms have their unique advantages in dealing with complex tasks, quantum machine learning (QML) is one of the most promising quantum algorithms with the rapid development of quantum technology. Specifically, Quantum generative adversarial network (QGAN) has shown the potential exponential quantum speedups in terms of performance. Meanwhile, QGAN also exhibits some problems, such as barren plateaus, unstable gradient, model collapse, absent complete scientific evaluation system, etc. How to improve the theory of QGAN and apply it that have attracted some researcher. In this paper, we comprehensively and deeply review recently proposed GAN and QAGN models and their applications, and we discuss the existing problems and future research trends of QGAN.

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APA Style
Li, T., Zhang, S., Xia, J. (2020). Quantum generative adversarial network: A survey. Computers, Materials & Continua, 64(1), 401-438. https://doi.org/10.32604/cmc.2020.010551
Vancouver Style
Li T, Zhang S, Xia J. Quantum generative adversarial network: A survey. Comput Mater Contin. 2020;64(1):401-438 https://doi.org/10.32604/cmc.2020.010551
IEEE Style
T. Li, S. Zhang, and J. Xia "Quantum Generative Adversarial Network: A Survey," Comput. Mater. Contin., vol. 64, no. 1, pp. 401-438. 2020. https://doi.org/10.32604/cmc.2020.010551



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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