Hybrid quantum–classical generative adversarial networks for image generation via learning discrete distribution
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...
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| Published in | Signal processing. Image communication Vol. 110; p. 116891 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.01.2023
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0923-5965 1879-2677 |
| DOI | 10.1016/j.image.2022.116891 |
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| Abstract | 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. |
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| AbstractList | 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. |
| ArticleNumber | 116891 |
| Author | Wu, Jun-Yun Xie, Xin-Wen Zhang, Tian-Feng Zhou, Nan-Run |
| Author_xml | – sequence: 1 givenname: Nan-Run orcidid: 0000-0002-5080-2189 surname: Zhou fullname: Zhou, Nan-Run email: znr21@163.com organization: School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Songjiang 201620, Shanghai, China – sequence: 2 givenname: Tian-Feng surname: Zhang fullname: Zhang, Tian-Feng organization: Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, Jiangxi, China – sequence: 3 givenname: Xin-Wen orcidid: 0000-0001-5870-6565 surname: Xie fullname: Xie, Xin-Wen email: xinwen.xie@jju.edu.cn organization: School of Management, Nanchang University, Nanchang 330031, Jiangxi, China – sequence: 4 givenname: Jun-Yun surname: Wu fullname: Wu, Jun-Yun organization: Department of Computer Science and Technology, Nanchang University, Nanchang 330031, China |
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| Keywords | Quantum computation Quantum machine learning Quantum generative adversarial network Hybrid quantum–classical algorithms Image generation |
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| SubjectTerms | Hybrid quantum–classical algorithms Image generation Quantum computation Quantum generative adversarial network Quantum machine learning |
| Title | Hybrid quantum–classical generative adversarial networks for image generation via learning discrete distribution |
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