HASM quantum machine learning
文献类型：期刊论文
| 作者 | Yue, Tianxiang; Wu, Chenchen; Liu, Yi; Du, Zhengping; Zhao, Na; Jiao, Yimeng; Xu, Zhe; Shi, Wenjiao |
| 刊名 | SCIENCE CHINA-EARTH SCIENCES
 |
| 出版日期 | 2023-08-07 |
| 卷号 | N/A |
| ISSN号 | 1674-7313 |
| 关键词 | Quantum computing Machine learning Eco-environmental surface High accuracy surface modelling Quantum computational advantage Practical quantum advantage |
| DOI | 10.1007/s11430-022-1144-7 |
| 产权排序 | 1 |
| 文献子类 | Review ; Early Access |
| 英文摘要 | 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 eco-environmental 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. |
| WOS关键词 | HIGH-ACCURACY METHOD ; CARBON STOCKS ; FILLING VOIDS ; MODEL ; UNCERTAINTY ; COMPUTATION ; INTEGRATION ; ALGORITHMS ; ADVANTAGE ; SATELLITE |
| WOS研究方向 | Geology |
| 语种 | 英语 |
| 出版者 | SCIENCE PRESS |
| WOS记录号 | WOS:001045000900001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/194522]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 作者单位 | 1.University of Chinese Academy of Sciences, CAS 2.Chinese Academy of Sciences 3.Institute of Geographic Sciences & Natural Resources Research, CAS 4.Jiangxi Agricultural University |
| 推荐引用方式 GB/T 7714 | Yue, Tianxiang,Wu, Chenchen,Liu, Yi,et al. HASM quantum machine learning[J]. SCIENCE CHINA-EARTH SCIENCES,2023,N/A. |
| APA | Yue, Tianxiang.,Wu, Chenchen.,Liu, Yi.,Du, Zhengping.,Zhao, Na.,...&Shi, Wenjiao.(2023).HASM quantum machine learning.SCIENCE CHINA-EARTH SCIENCES,N/A. |
| MLA | Yue, Tianxiang,et al."HASM quantum machine learning".SCIENCE CHINA-EARTH SCIENCES N/A(2023). |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。