Experimental Quantum Generative Adversarial Networks for Image Generation

@article{Huang2021ExperimentalQG,
  title={Experimental Quantum Generative Adversarial Networks for Image Generation},
  author={He Huang and Yuxuan Du and Ming Gong and Youwei Zhao and Yulin Wu and Chaoyue Wang and Shaowei Li and Futian Liang and Jin Lin and Yu Xu and Rui Yang and Tongliang Liu and Min-Hsiu Hsieh and Hui Deng and Hao Rong and Cheng-Zhi Peng and Chao Lu and Yu-Ao Chen and Dacheng Tao and Xiaobo Zhu and Jian-Wei Pan},
  journal={ArXiv},
  year={2021},
  volume={abs/2010.06201}
}
Quantum machine learning is expected to be one of the first practical applications of near-term quantum devices. Pioneer theoretical works suggest that quantum generative adversarial networks (GANs) may exhibit a potential exponential advantage over classical GANs, thus attracting widespread attention. However, it remains elusive whether quantum GANs implemented on near-term quantum devices can actually solve real-world learning tasks. Here, we devise a flexible quantum GAN scheme to narrow… Expand

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