@Article{cmc.2020.010551,
AUTHOR = {Tong Li, Shibin Zhang, Jinyue Xia},
TITLE = {Quantum Generative Adversarial Network: A Survey},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {1},
PAGES = {401--438},
URL = {http://www.techscience.com/cmc/v64n1/39150},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.010551}
}