摘要
讨论了量子近似优化算法(QAOA)在投资组合优化问题上的应用,而后者在离散的约束条件下是NP难的;介绍了QAOA的基本框架以及相应的投资组合优化问题的建模;阐述了数个可用于解决投资组合优化问题的QAOA方法。通过数值模拟及假设检验比较这些方法与经典方法的表现,各量子算法在平均近似比上相较经典方法均有7%以上的提升。
In this paper,we discuss the application of Quantum Approximation Optimization Algorithm(QAOA)in portfolio optimization problems,which,under discrete constraints,is proved to be NP-hard.We introduce the fundamental framework of QAOA and the corresponding modeling of portfolio optimization problems.We illustrate several variants of QAOA applicable to portfolio optimization problems.Next,we examine their performances and the performance of the classical method with numerical simulation and hypothesis testing.The average approximation ratio of each quantum algorithm is at least 7%higher than that of the classical algorithm.
作者
吴涵卿
袁淏木
陈柄任
吴磊
李鑫
李晓瑜
WU Hanqing;YUAN Haomu;CHEN Bingren;WU Lei;LI Xin;LI Xiaoyu(CCB Fintech Co.,Ltd.Pudong Shanghai 200120;Sichuan Yuanjiang Technology Co.,Ltd.Chengdu 611730;School of Information and Software Engineering,University of Electronic Science and Technology of China Chengdu 610054)
出处
《电子科技大学学报》
EI
CAS
CSCD
北大核心
2023年第5期642-648,共7页
Journal of University of Electronic Science and Technology of China
基金
建信金融科技有限责任公司研究性课题(KT2000050)
成都市重点研发支撑计划重大科技应用示范项目(2021-YF09-00114-GX)。
关键词
离散优化
投资组合优化
量子近似优化算法
量子计算
discrete optimization
portfolio optimization
quantum approximation optimization algorithm
quantum computing
