A Hybrid Quantum-Classical GAN with Classical Critic for Multi-Conditional Image Generation

Quantum machine learning (QML) has garnered significant attention for its potential to outperform classical machine learning.A notable area within QML is quantum generative adversarial networks (QGANs), which serve as the quantum equivalent to classical GANs, commonly used in image processing and generation.However, the content of images generated by existing QGANs is still uncontrollable.In this work, we introduce a conditional encoder for the quantum generator, enabling it to controllably produce multiple image types.While this approach can, in principle, be extended to any number of conditional generations, its practical applicability is limited by the finite qubit resources available.Our results show that it can generate images of satisfactory quality, with a Frechet Inception Distance (FID) score of $201.6$, a Kernel Inception Distance (KID) score of $0.281$, and a Learned Perceptual Image Patch Similarity (LPIPS) score of $0.461$.Through quantum noise experiments, we show that when the noise intensity reaches $0.2$, the relative changes in FID, KID, and LPIPS remain below $5\%$, indicating that the model is robust to moderate noise levels.Furthermore, in comparative experiments against without multicondition or limitted multicondition, our method outperforms the strongest baseline by $3\%-11\%$ , thereby demonstrating its stable superiority over current approaches.