HQSP-GAN: A hybrid quantum-classical architecture for high-quality image generation

Quantum machine learning has attracted considerable attention due to its potential in classification and recognition tasks. As a significant branch of this field, quantum generative adversarial networks (QGANs) have achieved notable progress in image generation. Although existing models have improved in terms of generation quality and efficiency, their performance remains suboptimal. To overcome these limitations, we propose HQSP-GAN, a hybrid quantum-classical generative adversarial model integrating a shared parameter mechanism. This model innovatively designs a quantum parameter sharing scheme to reduce the number of trainable parameters in the quantum module. Additionally, by incorporating attention mechanisms, the Wasserstein distance, and gradient penalty techniques, HQSP-GAN enhances training stability and generation performance. Extensive experiments on standard datasets such as MNIST and Fashion MNIST demonstrate that HQSP-GAN can generate high-quality grayscale images without additional dimensionality reduction or preprocessing. Moreover, it outperforms SOTA models including QINR-GAN and PQWGAN in metrics such as SSIM and PSNR, highlighting the efficiency and practicality of its architectural design.