AAAI 2025technical0 citations
Hybrid Quantum-Classical Style Transfer (Student Abstract)
Emily Jimin Roh, Joo Yong Shim, Soohyun Park, Joongheon Kim
Abstract
This paper proposes a novel quantum style transfer (QST) in hybrid quantum-classical computing. QST leverages quantum computing's ability to process high-dimensional data efficiently. Our approach aims to decrease both inference time and complexity while maintaining performance, presenting a viable solution that enhances the scalability and efficiency of image generation technologies.
BibTeX
@article{Roh_Shim_Park_Kim_2025, title={Hybrid Quantum-Classical Style Transfer (Student Abstract)}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/35295}, DOI={10.1609/aaai.v39i28.35295}, abstractNote={This paper proposes a novel quantum style transfer (QST) in hybrid quantum-classical computing. QST leverages quantum computing’s ability to process high-dimensional data efficiently. Our approach aims to decrease both inference time and complexity while maintaining performance, presenting a viable solution that enhances the scalability and efficiency of image generation technologies.}, number={28}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Roh, Emily Jimin and Shim, Joo Yong and Park, Soohyun and Kim, Joongheon}, year={2025}, month={Apr.}, pages={29480-29481} }