https://doi.org/10.1140/epjqt/s40507-024-00304-3
Research
Quantum adversarial generation of high-resolution images
1
School of Information Systems Engineering, Information Engineering University, 62 Science Avenue, 450001, ZhengZhou, HeNan, China
2
School of Computer Science and Engineering, University of Electronic Science and Technology of China, 2006 Xiyuan Avenue, 610054, Chengdu, SiChuan, China
3
College of Computer Science and Technology, Jilin University, 2699 Qianjin Street, 130012, ChangChun, JiLin, China
4
Laboratory for Advanced Computing and Intelligence Engineering, Shanshui East Road, 214000, WuXi, JiangSu, China
a
hcl_xdspeechlab@aliyun.com
b
qudan_xd@163.com
Received:
26
November
2024
Accepted:
29
December
2024
Published online:
8
January
2025
As a promising model in Quantum Machine Learning (QML), Quantum Generative Adversarial Networks (QGANs) are rapidly advancing, offering applications in image processing and generation. However, another emerging paradigm represents an image as a Quantum Implicit Neural Representation (QINR). In this work, we propose a novel architectural technique for building QINR-based QGAN to enhance the quality of images generated by QGANs. Additionally, we integrate classical techniques, such as Gradient Penalty and Wasserstein distance, to train QINR-QGAN. In image generation tasks, we demonstrated that QINR-QGAN can achieve performance comparable to state-of-the-art (SOTA) models while significantly reducing the number of trainable quantum parameters. Specifically, QINR-QGAN reduced the trainable quantum parameters by nearly 10 times compared to PQWGAN (Tsang et al. in IEEE Trans. Quantum Eng. 4:1–19, 2023) and Quantum AnoGAN (Herr et al. Quantum Sci. Technol. 6(4): 045004, 2021), demonstrating its superior efficiency in parameter optimization without sacrificing performance. Furthermore, we conducted experiments on the CelebA dataset to tackle a more complex task and generate larger images (
). The results indicate that our model is capable of successfully completing the face generation task.
Key words: Quantum machine learning / Quantum generative adversarial networks / Quantum Implicit Neural Representations
© The Author(s) 2025
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