Tong Li1, Shibin Zhang1, *, Jinyue Xia2
CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 401-438, 2020, DOI: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… More >
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