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Science China Earth Sciences 2023年第9期66卷 1937-1945页

作者： Tianxiang YUE Chenchen WU Yi LIU Zhengping DU Na ZHAO Yimeng JIAO Zhe XU Wenjiao SHI State Key Laboratory of Resources and Environment Information System Institute of Geographical Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China Yanqi Lake Beijing Institute of Mathematical Sciences and Applications Beijing 101408China College of Resources and Environment University of Chinese Academy of SciencesBeijing 101499China College of Land Resources and Environment Jiangxi Agricultural UniversityNanchang 330045China

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

关键词： quantum computing Machine learning Eco-environmental surface High accuracy surface modelling quantum computational advantage practical quantum advantage

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