Facilitating Cursive Chinese Calligraphy Generation with Quantum Computing Technology

Cursive Chinese calligraphy in Taiwan is a traditional yet still prevalent art form. Due to the high degree of variation and the fact that most cursive script is derived from historical documents, the available data is relatively limited, posing a significant challenge for AI (artificial intelligence) models. In this study, we combine quantum computing with diffusion models to generate images of Chinese cursive characters. Diffusion models have recently been proven to perform better than other generative models when working with small datasets. Moreover, the integration of quantum computing reduces training costs and enhances generation performance. The results in this paper demonstrate the potential of quantum computing in conjunction with generative AI, which is applicable to interdisciplinary needs in design, artistic creation, and cultural preservation. In future work, we will delve deeper into noise-related issues and propose possible solutions.