Hybrid quantum–classical generative adversarial networks for image generation via learning discrete distribution

• Published in: Signal processing. Image communication Vol. 110; p. 116891
• Main Authors: Zhou, Nan-Run, Zhang, Tian-Feng, Xie, Xin-Wen, Wu, Jun-Yun
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2023
• Subjects: Hybrid quantum–classical algorithms; Image generation; Quantum computation; Quantum generative adversarial network; Quantum machine learning; Quantum computation; Quantum machine learning; Quantum generative adversarial network; Hybrid quantum–classical algorithms; Image generation
• ISSN: 0923-5965; 1879-2677
• DOI: 10.1016/j.image.2022.116891

It has been reported that quantum generative adversarial networks have a potential exponential advantage over classical generative adversarial networks. However, quantum machine learning is difficult to find real applications in the near future due to the limitation of quantum devices. The structure of quantum generator is optimized to reduce the required parameters and make use of quantum devices to a greater extent. And an image generation scheme is designed based on quantum generative adversarial networks. Two structures of quantum generative adversarial networks are simulated on Bars and Stripes dataset, and the results corroborate that the quantum generator with reduced parameters has no visible performance loss. The original complex multimodal distribution of an image can be converted into a simple unimodal distribution by the remapping method. The MNIST images and the Fashion-MNIST images are successfully generated by the optimized quantum generator with the remapping method, which verified the feasibility of the proposed image generation scheme. •The structure of quantum generator is optimized to reduce the required parameters.•An image generation scheme is designed based on quantum generative adversarial networks.•The multimodal distribution of an image is converted into a unimodal distribution by the remapping method.•The MNIST images and the Fashion-MNIST ones are generated by the optimized quantum generator.