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Is quantum optimization ready? An effort towards neural network compression using adiabatic quantum computing

文献类型：期刊论文

作者Wang, Zhehui2; Choong, Benjamin Chen Ming2; Huang, Tian1; Gerlinghoff, Daniel2; Goh, Rick Siow Mong2; Liu, Cheng3; Luo, Tao2
刊名FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
出版日期2026
卷号174页码:11
关键词Adiabatic quantum computing Quantum annealing Neural network optimization Model compression
ISSN号0167-739X
DOI10.1016/j.future.2025.107908
英文摘要Quantum optimization is the most mature quantum computing technology to date, providing a promising approach towards efficiently solving complex combinatorial problems. Methods such as adiabatic quantum computing (AQC) have been employed in recent years on important optimization problems across various domains. In deep learning, deep neural networks (DNN) have reached immense sizes to support new predictive capabilities. Optimization of large-scale models is critical for sustainable deployment, but becomes increasingly challenging with ever-growing model sizes and complexity. While quantum optimization is suitable for solving complex problems, its application to DNN optimization is not straightforward, requiring thorough reformulation for compatibility with commercially available quantum devices. In this work, we explore the potential of adopting AQC for fine-grained pruning-quantization of convolutional neural networks. We rework established heuristics to formulate model compression as a quadratic unconstrained binary optimization (QUBO) problem, and assess the solution space offered by commercial quantum annealing devices. Through our exploratory efforts of reformulation, we demonstrate that AQC can achieve effective compression of practical DNN models. Experiments demonstrate that adiabatic quantum computing (AQC) not only outperforms classical algorithms like genetic algorithms and reinforcement learning in terms of time efficiency but also excels at identifying global optima.
资助项目National Research Foundation Singapore, under its Quantum Engineering Programme 2.0 (National Quantum Computing Hub)[NRF2021-QEP2-02-P01] ; A*STAR[C230917003]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001511454300003
出版者ELSEVIER
源URL[http://119.78.100.204/handle/2XEOYT63/42361]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Luo, Tao
作者单位1.Huadian Coal Ind Grp Co Ltd, Beijing, Peoples R China
2.ASTAR, Inst High Performance Comp IHPC, 1 Fusionopolis Way,16-16 Connexis, Singapore 138632, Singapore
3.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Zhehui,Choong, Benjamin Chen Ming,Huang, Tian,et al. Is quantum optimization ready? An effort towards neural network compression using adiabatic quantum computing[J]. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE,2026,174:11.
APA Wang, Zhehui.,Choong, Benjamin Chen Ming.,Huang, Tian.,Gerlinghoff, Daniel.,Goh, Rick Siow Mong.,...&Luo, Tao.(2026).Is quantum optimization ready? An effort towards neural network compression using adiabatic quantum computing.FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE,174,11.
MLA Wang, Zhehui,et al."Is quantum optimization ready? An effort towards neural network compression using adiabatic quantum computing".FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 174(2026):11.

入库方式： OAI收割

来源：计算技术研究所

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