Facilitating Cursive Chinese Calligraphy Generation with Quantum Computing Technology

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Abstract:

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 Due to the high degree of variation and the fact that most cursive script is derived from historical documents, the available data is relatively limited, posing a significant challenge for AI (artificial intelligence) models.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.

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Engineering Headway (Volume 27)

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49-57

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October 2025

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© 2025 Trans Tech Publications Ltd. All Rights Reserved

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[1] J. Liang, W. -H. Liao and Y. -C. Wu, "Toward Automatic Recognition of Cursive Chinese Calligraphy: An Open Dataset For Cursive Chinese Calligraphy Text," 2020 14th International Conference on Ubiquitous Information Management and Communication (IMCOM), Taichung, Taiwan, 2020, pp.1-5.

DOI: 10.1109/imcom48794.2020.9001777

Google Scholar

[2] P. Isola, J. -Y. Zhu, T. Zhou and A. A. Efros, "Image-to-Image Translation with Conditional Adversarial Networks," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp.5967-5976.

DOI: 10.1109/cvpr.2017.632

Google Scholar

[3] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial nets, Adv. Neural Inf. Process. Syst. 27 (2014).

DOI: 10.1145/3422622

Google Scholar

[4] K. Nikolaidou, G. Retsinas, V. Christlein, M. Seuret, G. Sfikas, E.B. Smith, H. Mokayed, M. Liwicki, Wordstylist: Styled verbatim handwritten text generation with latent diffusion models, International Conference on Document Analysis and Recognition, Springer, 2023, p.384–401.

DOI: 10.1007/978-3-031-41679-8_22

Google Scholar

[5] K.S. Chung, Wade–Giles romanization system, The Routledge Encyclopedia of the Chinese Language, Routledge, 2016, p.794–814.

Google Scholar

[6] M. Brooks, Quantum computers: what are they good for? (Online; accessed 24-May-2023), Available: https://www.nature.com/articles/d41586-023-01692-9

Google Scholar

[7] CompTIA's AI Advisory Council, 10 things you should know about quantum computing and AI, (Online; accessed 20-October-2022), Available: https://connect.comptia.org/blog/10-things-you-should-know-about-quantum-computing-and-ai

DOI: 10.1302/3114-221590

Google Scholar

[8] M. Kölle, G. Stenzel, J. Stein, S. Zielinski, B. Ommer, C. Linnhoff-Popien, Quantum denoising diffusion models, arXiv preprint arXiv:2401.07049, 2024.

DOI: 10.1109/qsw62656.2024.00023

Google Scholar

[9] J. Ho, A. Jain, P. Abbeel, Denoising diffusion probabilistic models, Adv. Neural Inf. Process. Syst. 33 (2020) 6840–6851.

Google Scholar

[10] R. Rombach, A. Blattmann, D. Lorenz, P. Esser and B. Ommer, "High-Resolution Image Synthesis with Latent Diffusion Models," 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, 2022, pp.10674-10685.

DOI: 10.1109/cvpr52688.2022.01042

Google Scholar

[11] A. Barenco, C.H. Bennett, R. Cleve, D.P. DiVincenzo, N. Margolus, P. Shor, T. Sleator, J.A. Smolin, H. Weinfurter, Elementary gates for quantum computation, Phys. Rev. A 52 (5) (1995) 3457.

DOI: 10.1103/physreva.52.3457

Google Scholar

[12] J. Biamonte, P. Wittek, N. Pancotti, et al. Quantum machine learning. Nature 549, 195–202 (2017).

DOI: 10.1038/nature23474

Google Scholar

[13] P.-L. Dallaire-Demers, N. Killoran, Quantum generative adversarial networks, Phys. Rev. A 98 (1) (2018) 012324.

DOI: 10.1103/physreva.98.012324

Google Scholar

[14] C. Chen, Q. Zhao, Quantum generative diffusion model, arXiv preprint arXiv:2401.07039, 2024.

Google Scholar