摘要
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.
基金
This work is supported by the National Natural Science Foundation of China(No.61572086,No.61402058)
the Key Research and Development Project of Sichuan Province(Nos.20ZDYF2324,2019ZYD027 and 2018TJPT0012)
the Innovation Team of Quantum Security Communication of Sichuan Province(No.17TD0009)
the Academic and Technical Leaders Training Funding Support Projects of Sichuan Province(No.2016120080102643)
the Application Foundation Project of Sichuan Province(No.2017JY0168)
the Science and Technology Support Project of Sichuan Province(Nos.2018GZ0204 and 2016FZ0112).