Enhancing AI Image Generation through Integration of Quantum Computing and Deep Learning

Text-prompt-based artificial intelligence image generation plays a crucial role in visualizing concepts during early design and prototyping stages; however, existing approaches often exhibit limited semantic consistency between input prompts and generated images. To overcome this limitation, this study introduces a hybrid quantum computing–assisted deep learning framework for targeted text-to-image generation. The proposed method enhances prompt–image association by jointly modeling textual semantics and visual attributes using quantum-encoded representations. At the same time, deep learning is employed to validate and optimize the integration of objects, backgrounds, and attribute combinations. By improving semantic alignment at the representation level, the framework effectively reduces generation inconsistency. Experimental evaluation on publicly available text-to-image prompts and synthetic image datasets demonstrates that the proposed approach achieves a 11.4% improvement in similarity score and a 13.13% increase in F1-score compared with existing methods.