Abstract

Entanglement and correlation of quantum light can enhance LiDAR sensitivity in the presence of strong background noise. However, the power of such quantum sources is fundamentally limited to a stream of single photons and cannot compete with the detection range of high-power classical LiDAR transmitters. To circumvent this, we develop and demonstrate a quantum-inspired LiDAR prototype based on coherent measurement of classical time-frequency correlation. This system uses a high-power classical source and maintains the high noise rejection advantage of quantum LiDARs. In particular, we show that it can achieve over 100dB rejection (with 100ms integration time) of indistinguishable (with statistically identical properties in every degree of freedom) in-band noise while still being sensitive to single photon signals. In addition to the LiDAR demonstration, we also discuss the potential of the proposed LiDAR receiver for quantum information applications. In particular, we propose the chaotic quantum frequency conversion technique for coherent manipulation of high dimensional quantum states of light. It is shown that this technique can provide improved performance in terms of selectivity and efficiency as compared to pulse-based quantum frequency conversion.

LiDARs exploiting quantum correlations provide enhancement in noise resilience and sensitivity, but high-power classical sources offer much higher operating distances. Here, the authors show how to exploit high power classical time-frequency correlations to keep the best of both worlds.

Details

Title
Compact all-fiber quantum-inspired LiDAR with over 100 dB noise rejection and single photon sensitivity
Author
Liu, Han 1   VIAFID ORCID Logo  ; Qin, Changhao 1   VIAFID ORCID Logo  ; Papangelakis, Georgios 1   VIAFID ORCID Logo  ; Iu, Meng Lon 1 ; Helmy, Amr S. 1 

 University of Toronto, The Edward S. Rogers Department of Electrical and Computer Engineering, Toronto, Canada (GRID:grid.17063.33) (ISNI:0000 0001 2157 2938) 
Pages
5344
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2859993032
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.