id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
fcis-30504	Liu, Siqi	Design and Optimization of Multimodal Hybrid Architectures Based on Quantum Computing	2025	3	.pdf	application/pdf	1419	70	44	Traditional prediction models such as AR models[1] suffer from high complexity of parameter optimization in time-series analysis, while classical classification algorithms such as SVM[2] face computational efficiency bottlenecks when dealing with high- dimensional data. In this paper, for the tasks of resource demand prediction and data classification, we propose a hybrid framework integrating classical models and quantum optimization techniques[5], which breaks through the performance boundaries of traditional methods by transforming the traditional model parameter optimization into the form of Quantum Unconstrained Binary Optimization (QUBO) and combining with the parallel searching capability of quantum computing[6].	cache/fcis-30504.pdf	txt/fcis-30504.txt