Experimental demonstration of memory-enhanced quantum communication

被引:0
|
作者
M. K. Bhaskar
R. Riedinger
B. Machielse
D. S. Levonian
C. T. Nguyen
E. N. Knall
H. Park
D. Englund
M. Lončar
D. D. Sukachev
M. D. Lukin
机构
[1] Harvard University,Department of Physics
[2] Harvard University,John A. Paulson School of Engineering and Applied Sciences
[3] Harvard University,Department of Chemistry and Chemical Biology
[4] Research Laboratory of Electronics,undefined
[5] MIT,undefined
来源
Nature | 2020年 / 580卷
关键词
D O I
暂无
中图分类号
学科分类号
摘要
The ability to communicate quantum information over long distances is of central importance in quantum science and engineering1. Although some applications of quantum communication such as secure quantum key distribution2,3 are already being successfully deployed4–7, their range is currently limited by photon losses and cannot be extended using straightforward measure-and-repeat strategies without compromising unconditional security8. Alternatively, quantum repeaters9, which utilize intermediate quantum memory nodes and error correction techniques, can extend the range of quantum channels. However, their implementation remains an outstanding challenge10–16, requiring a combination of efficient and high-fidelity quantum memories, gate operations, and measurements. Here we use a single solid-state spin memory integrated in a nanophotonic diamond resonator17–19 to implement asynchronous photonic Bell-state measurements, which are a key component of quantum repeaters. In a proof-of-principle experiment, we demonstrate high-fidelity operation that effectively enables quantum communication at a rate that surpasses the ideal loss-equivalent direct-transmission method while operating at megahertz clock speeds. These results represent a crucial step towards practical quantum repeaters and large-scale quantum networks20,21.
引用
收藏
页码:60 / 64
页数:4
相关论文
共 50 条
  • [21] Memory-Enhanced Dynamic Evolutionary Control of Reconfigurable Intelligent Surfaces
    Zardi, Francesco
    Oliveri, Giacomo
    Massa, Andrea
    [J]. IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, 2024, 72 (07) : 5754 - 5766
  • [22] Attend to Knowledge: Memory-Enhanced Attention Network for Image Captioning
    Chen, Hui
    Ding, Guiguang
    Lin, Zijia
    Guo, Yuchen
    Han, Jungong
    [J]. ADVANCES IN BRAIN INSPIRED COGNITIVE SYSTEMS, BICS 2018, 2018, 10989 : 161 - 171
  • [23] Shape memory-enhanced water sensing of conductive polymer composites
    Luo, Hongsheng
    Ma, Yuanyuan
    Li, Wenjin
    Yi, Guobin
    Cheng, Xiaoling
    Ji, Wenjin
    Zu, Xihong
    Yuan, Shengjie
    Li, Jiahui
    [J]. MATERIALS LETTERS, 2015, 161 : 189 - 192
  • [24] Memory-Enhanced Evolutionary Robotics: The Echo State Network Approach
    Hartland, Cedric
    Bredeche, Nicolas
    Sebag, Michele
    [J]. 2009 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-5, 2009, : 2788 - 2795
  • [25] Memory-enhanced energetic stability for a fractional oscillator with fluctuating frequency
    Mankin, Romi
    Rekker, Astrid
    [J]. PHYSICAL REVIEW E, 2010, 81 (04):
  • [26] Memory-Enhanced Transformer for Representation Learning on Temporal Heterogeneous Graphs
    Li, Longhai
    Duan, Lei
    Wang, Junchen
    He, Chengxin
    Chen, Zihao
    Xie, Guicai
    Deng, Song
    Luo, Zhaohang
    [J]. DATA SCIENCE AND ENGINEERING, 2023, 8 (02) : 98 - 111
  • [27] Memory-enhanced text style transfer with dynamic style learning and calibration
    Fuqiang LIN
    Yiping SONG
    Zhiliang TIAN
    Wangqun CHEN
    Diwen DONG
    Bo LIU
    [J]. Science China(Information Sciences), 2024, 67 (04) : 181 - 196
  • [28] Memory-enhanced text style transfer with dynamic style learning and calibration
    Lin, Fuqiang
    Song, Yiping
    Tian, Zhiliang
    Chen, Wangqun
    Dong, Diwen
    Liu, Bo
    [J]. SCIENCE CHINA-INFORMATION SCIENCES, 2024, 67 (04)
  • [29] Shape Memory-Enhanced Electrical Self-Healing of Stretchable Electrodes
    Luo, Hongsheng
    Wang, Huaquan
    Zhou, Huankai
    Zhou, Xingdong
    Hu, Jinlian
    Yi, Guobin
    Hao, Zhifeng
    Lin, Wenjing
    [J]. APPLIED SCIENCES-BASEL, 2018, 8 (03):
  • [30] Memory-enhanced univariate marginal distribution algorithms for dynamic optimization problems
    Yang, SX
    [J]. 2005 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-3, PROCEEDINGS, 2005, : 2560 - 2567