KMS Of Academy of mathematics and systems sciences, CAS
| Adaptive quantum state tomography via linear regression estimation: Theory and two-qubit experiment | |
Qi, Bo1,2,3,4 ; Hou, Zhibo5,6; Wang, Yuanlong7; Dong, Daoyi7; Zhong, Han-Sen5,6; Li, Li3,4; Xiang, Guo-Yong5,6; Wiseman, Howard M.3,4; Li, Chuan-Feng5,6; Guo, Guang-Can5,6
| |
| 2017-04-24 | |
| Source Publication | NPJ QUANTUM INFORMATION
 |
| ISSN | 2056-6387 |
| Volume | 3Pages:7 |
| Abstract | Adaptive techniques have great potential for wide application in enhancing the precision of quantum parameter estimation. We present an adaptive quantum state tomography protocol for finite dimensional quantum systems and experimentally implement the adaptive tomography protocol on two-qubit systems. In this adaptive quantum state tomography protocol, an adaptive measurement strategy and a recursive linear regression estimation algorithm are performed. Numerical results show that our adaptive quantum state tomography protocol can outperform tomography protocols using mutually unbiased bases and the two-stage mutually unbiased bases adaptive strategy, even with the simplest product measurements. When nonlocal measurements are available, our adaptive quantum state tomography can beat the Gill-Massar bound for a wide range of quantum states with a modest number of copies. We use only the simplest product measurements to implement two-qubit tomography experiments. In the experiments, we use error-compensation techniques to tackle systematic error due to misalignments and imperfection of wave plates, and achieve about a 100-fold reduction of the systematic error. The experimental results demonstrate that the improvement of adaptive quantum state tomography over nonadaptive tomography is significant for states with a high level of purity. Our results also show that this adaptive tomography method is particularly effective for the reconstruction of maximally entangled states, which are important resources in quantum information. |
| DOI | 10.1038/s41534-017-0016-4 |
| Language | 英语 |
| Funding Project | National Natural Science Foundation of China[61222504] ; National Natural Science Foundation of China[11574291] ; National Natural Science Foundation of China[61374092] ; National Natural Science Foundation of China[61227902] ; Australian Research Council's Discovery Projects[DP130101658] ; Center of Excellence[CE110001027] |
| WOS Research Area | Physics |
| WOS Subject | Physics, Applied ; Physics, Atomic, Molecular & Chemical ; Physics, Condensed Matter |
| WOS ID | WOS:000401285300001 |
| Publisher | NATURE PUBLISHING GROUP |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/25485 |
| Collection | 系统科学研究所 |
| Corresponding Author | Xiang, Guo-Yong |
| Affiliation | 1.Chinese Acad Sci, Acad Math & Syst Sci, Key Lab Syst & Control, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Griffith Univ, Ctr Quantum Computat & Commun Technol, Brisbane, Qld 4111, Australia 4.Griffith Univ, Ctr Quantum Dynam, Brisbane, Qld 4111, Australia 5.Univ Sci & Technol China, Key Lab Quantum Informat, CAS, Hefei 230026, Peoples R China 6.Univ Sci & Technol China, Synerget Innovat Ctr Quantum Informat & Quantum P, Hefei 230026, Anhui, Peoples R China 7.Univ New South Wales, Sch Engn & Informat Technol, Canberra, ACT 2600, Australia |
| Recommended Citation GB/T 7714 | Qi, Bo,Hou, Zhibo,Wang, Yuanlong,et al. Adaptive quantum state tomography via linear regression estimation: Theory and two-qubit experiment[J]. NPJ QUANTUM INFORMATION,2017,3:7. |
| APA | Qi, Bo.,Hou, Zhibo.,Wang, Yuanlong.,Dong, Daoyi.,Zhong, Han-Sen.,...&Guo, Guang-Can.(2017).Adaptive quantum state tomography via linear regression estimation: Theory and two-qubit experiment.NPJ QUANTUM INFORMATION,3,7. |
| MLA | Qi, Bo,et al."Adaptive quantum state tomography via linear regression estimation: Theory and two-qubit experiment".NPJ QUANTUM INFORMATION 3(2017):7. |
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment