https://doi.org/10.1140/epjqt/s40507-025-00372-z
Research
Quantum generative adversarial network with automated noise suppression mechanism based on WGAN-GP
1
School of Information and Control Engineering, Qingdao University of Technology, Qingdao, China
2
School of Science, Qingdao University of Technology, Qingdao, China
Received:
19
October
2024
Accepted:
21
May
2025
Published online:
23
June
2025
Quantum Machine Learning (QML) has attracted significant attention for its potential to deliver exponential advantages over classical machine learning approaches, particularly in classification and recognition tasks. Quantum Generative Adversarial Networks (QGANs), a form of quantum machine learning, provide promising advantages in image processing and generation tasks when compared to classical technologies. However, the limitations of current quantum devices have led to suboptimal image quality and limited robustness in earlier methods. To overcome these challenges, we developed a hybrid quantum-classical approach, introducing CAQ, a quantum-classical Generative Adversarial Network (GAN) framework. Leveraging the latest WGAN-gradient penalty (GP) strategy, we trained and optimized the quantum generator, reduced the complexity of parameters, and implemented an adaptive noise input system that dynamically adjusts noise levels, thereby improving the model’s robustness. Additionally, we employed a remapping technique to transform the original image’s multimodal distribution into a unimodal one, thereby reducing the complexity of the learned distribution. Experiments on MNIST and Fashion-MNIST datasets show that CAQ generates grayscale images effectively, demonstrating its feasibility on near-term intermediate-scale quantum (NISQ) computers.
Key words: Machine learning algorithms / Quantum circuit / Quantum computing
© The Author(s) 2025
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