Exploring the Power of Entangled Data in Quantum Machine Learning
作者机构：Institute of Artificial IntelligenceSchool of Computer ScienceWuhan UniversityWuhan 430072HubeiChina National Engineering Research Center for Multimedia SoftwareWuhan UniversityWuhan 430072HubeiChina JD Explore AcademyBeijing 101111China School of Computer Science and EngineeringNanyang Technological UniversitySingapore 639798Singapore School of Computer ScienceFaculty of EngineeringUniversity of SydneyNSW 2008Australia Center on Frontiers of Computing StudiesPeking UniversityBeijing 100871China School of Computer SciencePeking UniversityBeijing 100871China
出 版 物：《Wuhan University Journal of Natural Sciences》 (武汉大学学报（自然科学英文版）)
年 卷 期：2024年第29卷第3期
页 面：193-194页
核心收录：
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 081104[工学-模式识别与智能系统] 08[工学] 070201[理学-理论物理] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0702[理学-物理学]
基 金：support from the National Natural Science Foundation of China(U23A20318 and 62276195) support from the National Natural Science Foundation of China(12175003,12361161602) NSAF(U2330201)
主 题：quantum specified integrate
摘 要：Quantum entanglement is a key resource for achieving superiority of quantum ***,scientists are extensively focusing on how to integrate quantum entanglement into various components of quantum machine learning(QML)models,aiming to surpass the performance of traditional machine learning *** successes include the use of entangled measurements^([1-3])and entangled channels^([4]),which have been shown to reduce query complexity or improve the prediction precision for specified QML *** entangled data,capable of encoding more information compared to classical data of the same size,is recognized for its potential to achieve quantum ***,the impact of the entanglement degree in quantum data on model performance remains a challenging and unresolved research question.
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