HASM quantum machine learning

作     者：Tianxiang YUE Chenchen WU Yi LIU Zhengping DU Na ZHAO Yimeng JIAO Zhe XU Wenjiao SHI Tianxiang YUE;Chenchen WU;Yi LIU;Zhengping DU;Na ZHAO;Yimeng JIAO;Zhe XU;Wenjiao SHI

作者机构：State Key Laboratory of Resources and Environment Information SystemInstitute of Geographical Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China Yanqi Lake Beijing Institute of Mathematical Sciences and ApplicationsBeijing 101408China College of Resources and EnvironmentUniversity of Chinese Academy of SciencesBeijing 101499China College of Land Resources and EnvironmentJiangxi Agricultural UniversityNanchang 330045China 

出 版 物：《Science China Earth Sciences》 (中国科学（地球科学英文版）)

年 卷 期：2023年第66卷第9期

页      面：1937-1945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] 09[农学] 0903[农学-农业资源与环境] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0713[理学-生态学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the Open Research Program of the International Research Center of Big Data for Sustainable Development Goals(Grant No.CBAS2022ORP02) the National Natural Science Foundation of China(Grant Nos.41930647,72221002) the Key Project of Innovation LREIS(Grant No.KPI005) 

主　　题：Quantum computing Machine learning Eco-environmental surface High accuracy surface modelling Quantum computational advantage Practical quantum advantage 

摘      要：The miniaturization of transistors led to advances in computers mainly to speed up their *** miniaturization has approached its fundamental ***,many practices require better computational resources than the capabilities of existing ***,the development of quantum computing brings light to solve this *** briefly review the history of quantum computing and highlight some of its advanced *** on current studies,the Quantum Computing Advantage(QCA)seems *** challenge is how to actualize the practical quantum advantage(PQA).It is clear that machine learning can help with this *** method used for high accuracy surface modelling(HASM)incorporates reinforced machine *** can be transformed into a large sparse linear system and combined with the Harrow-Hassidim-Lloyd(HHL)quantum algorithm to support quantum machine *** has been successfully used with classical computers to conduct spatial interpolation,upscaling,downscaling,data fusion and model-data assimilation of ecoenvironmental ***,a training experiment on a supercomputer indicates that our HASM-HHL quantum computing approach has a similar accuracy to classical HASM and can realize exponential acceleration over the classical algorithms.A universal platform for hybrid classical-quantum computing would be an obvious next step along with further work to improve the approach because of the many known limitations of the HHL *** addition,HASM quantum machine learning might be improved by:(1)considerably reducing the number of gates required for operating HASM-HHL;(2)evaluating cost and benchmark problems of quantum machine learning;(3)comparing the performance of the quantum and classical algorithms to clarify their advantages and disadvantages in terms of accuracy and computational speed;and(4)the algorithms would be added to a cloud platform to support applications and gather active feedback from users