HASM quantum machine learning  

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

机构地区：[1]State Key Laboratory of Resources and Environment Information System,Institute of Geographical Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China [2]Yanqi Lake Beijing Institute of Mathematical Sciences and Applications,Beijing 101408,China [3]College of Resources and Environment,University of Chinese Academy of Sciences,Beijing 101499,China [4]College of Land Resources and Environment,Jiangxi Agricultural University,Nanchang 330045,China

出　　处：《Science China Earth Sciences》2023年第9期1937-1945,共9页中国科学（地球科学英文版）

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).

摘　　要：The miniaturization of transistors led to advances in computers mainly to speed up their computation.Such miniaturization has approached its fundamental limits.However,many practices require better computational resources than the capabilities of existing computers.Fortunately,the development of quantum computing brings light to solve this problem.We briefly review the history of quantum computing and highlight some of its advanced achievements.Based on current studies,the Quantum Computing Advantage(QCA)seems indisputable.The challenge is how to actualize the practical quantum advantage(PQA).It is clear that machine learning can help with this task.The method used for high accuracy surface modelling(HASM)incorporates reinforced machine learning.It can be transformed into a large sparse linear system and combined with the Harrow-Hassidim-Lloyd(HHL)quantum algorithm to support quantum machine learning.HASM has been successfully used with classical computers to conduct spatial interpolation,upscaling,downscaling,data fusion and model-data assimilation of ecoenvironmental surfaces.Furthermore,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 algorithm.In 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 of the algorithms.

关 键 词：Quantum computing Machine learning Eco-environmental surface High accuracy surface modelling Quantum computational advantage Practical quantum advantage 

分 类 号：X171.1[环境科学与工程—环境科学] TP181[自动化与计算机技术—控制理论与控制工程]