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 resou...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.展开更多
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).
文摘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.
