Quantum QR decomposition in the computational basis
文献类型:期刊论文
作者 | Ma, Guangsheng2; Li, Hongbo1![]() |
刊名 | QUANTUM INFORMATION PROCESSING
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出版日期 | 2020-07-29 |
卷号 | 19期号:8页码:16 |
关键词 | Quantum algorithms Computational basis Quantum QR decomposition |
ISSN号 | 1570-0755 |
DOI | 10.1007/s11128-020-02777-4 |
英文摘要 | In this paper, we propose a quantum algorithm for approximating the QR decomposition of any N x N matrix with a running time O(1/epsilon(2) N(2.5)polylog( N)), where epsilon is the desired precision. This quantum algorithm provides a polynomial speedup over the best classical algorithm, which has a running time O(N-3). Our quantum algorithm utilizes the quantum computation in the computational basis (QCCB) and a setting of updatable quantum memory. We further present a systematic approach to applying the QCCB to simulate any quantum algorithm. By this approach, the simulation time does not exceed O(N(2)polylog( N)) times the running time of the quantum algorithm originally designed with the amplitude encoding method, where N is the size of the problem. |
资助项目 | NSFC[11671388] ; National Key Research Program[2018YFA0704705] ; National Natural Science Foundation of China[11471040] ; National Natural Science Foundation of China[11761131002] |
WOS研究方向 | Physics |
语种 | 英语 |
WOS记录号 | WOS:000557536200002 |
出版者 | SPRINGER |
源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/51943] ![]() |
专题 | 系统科学研究所 |
通讯作者 | Zhao, Jiman |
作者单位 | 1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 2.Beijing Normal Univ, Key Lab Math & Complex Syst, Minist Educ, Sch Math Sci, Beijing 100875, Peoples R China |
推荐引用方式 GB/T 7714 | Ma, Guangsheng,Li, Hongbo,Zhao, Jiman. Quantum QR decomposition in the computational basis[J]. QUANTUM INFORMATION PROCESSING,2020,19(8):16. |
APA | Ma, Guangsheng,Li, Hongbo,&Zhao, Jiman.(2020).Quantum QR decomposition in the computational basis.QUANTUM INFORMATION PROCESSING,19(8),16. |
MLA | Ma, Guangsheng,et al."Quantum QR decomposition in the computational basis".QUANTUM INFORMATION PROCESSING 19.8(2020):16. |
入库方式: OAI收割
来源:数学与系统科学研究院
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