Efficient learning of mixed-state tomography for photonic quantum walk

Wang, Qin-Qin1,2; Dong, Shaojun3; Li, Xiao-Wei4; Xu, Xiao-Ye1,2,5; Wang, Chao3; Han, Shuai6; Yung, Man-Hong7; Han, Yong-Jian1,2,3,5; Li, Chuan-Feng1,2,5; Guo, Guang-Can1,2,5

2024-03-15

10.1126/sciadv.adl4871

SCIENCE ADVANCES   影响因子和分区

2375-2548

Noise-enhanced applications in open quantum walk (QW) has recently seen a surge due to their ability to improve performance. However, verifying the success of open QW is challenging, as mixed-state tomography is a resource-intensive process, and implementing all required measurements is almost impossible due to various physical constraints. To address this challenge, we present a neural-network-based method for reconstructing mixed states with a high fidelity (similar to 97.5%) while costing only 50% of the number of measurements typically required for open discrete-time QW in one dimension. Our method uses a neural density operator that models the system and environment, followed by a generalized natural gradient descent procedure that significantly speeds up the training process. Moreover, we introduce a compact interferometric measurement device, improving the scalability of our photonic QW setup that enables experimental learning of mixed states. Our results demonstrate that highly expressive neural networks can serve as powerful alternatives to traditional state tomography.

SCI ; EI

英语

通讯

Innovation Program for Quantum Science and Technology[2021Zd0301200] ; National Natural Science Foundation of China["12022401","62075207","11874343","12104433","12374336","12304552","11821404","12204468"] ; Fundamental Research Funds for the Central Universities["WK2470000030","WK2030000081"] ; CAS Youth Innovation Promotion Association[2020447] ; China Postdoctoral Science Foundation[2021 M703108]

Science & Technology - Other Topics

Multidisciplinary Sciences

WOS:001190089500008

AMER ASSOC ADVANCEMENT SCIENCE

Web of Science

1.Univ Sci & Technol China, CAS Key Lab Quantum Informat, Hefei 230026, Peoples R China
2.Univ Sci & Technol China, CAS Ctr Excellence Quantum Informat & Quantum Phys, Hefei 230026, Peoples R China
3.Hefei Comprehens Natl Sci Ctr, Inst Artificial Intelligence, Hefei 230031, Peoples R China
4.Southern Univ Sci & Technol, Dept Phys, Shenzhen 518055, Peoples R China
5.Univ Sci & Technol China, Hefei Natl Lab, Hefei 230088, Peoples R China
6.Yangtze Delta Reg Ind Innovat Ctr Quantum & Inform, Suzhou 215100, Peoples R China
7.Southern Univ Sci & Technol, Inst Quantum Sci & Engn, Shenzhen 518055, Peoples R China

Wang, Qin-Qin,Dong, Shaojun,Li, Xiao-Wei,et al. Efficient learning of mixed-state tomography for photonic quantum walk[J]. SCIENCE ADVANCES,2024,10(11).

Wang, Qin-Qin.,Dong, Shaojun.,Li, Xiao-Wei.,Xu, Xiao-Ye.,Wang, Chao.,...&Guo, Guang-Can.(2024).Efficient learning of mixed-state tomography for photonic quantum walk.SCIENCE ADVANCES,10(11).

Wang, Qin-Qin,et al."Efficient learning of mixed-state tomography for photonic quantum walk".SCIENCE ADVANCES 10.11(2024).