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Noise-tolerant LiDAR approaching the standard quantum-limited precision.

Haochen Li1, Kaimin Zheng2, Rui Ge1,2

  • 1Research Institute of Superconductor Electronics & Key Laboratory of Optoelectronic Devices and Systems with Extreme Performances of MOE, Nanjing University, Nanjing, 210023, China.

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Summary

This study introduces a quantum-inspired LiDAR system using photon-number-resolving detectors to overcome background noise. The novel approach achieves near-standard quantum-limited performance for enhanced target detection and material identification.

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Area of Science:

  • Quantum optics and photonics
  • LiDAR technology
  • Computational imaging

Background:

  • Quantum-inspired imaging enhances LiDAR performance, but outdoor noise limits current systems.
  • Achieving standard quantum-limited performance in LiDAR remains a challenge due to background noise and signal variations.

Purpose of the Study:

  • To propose and demonstrate a LiDAR system that approaches standard quantum-limited performance.
  • To overcome heavy background noise in outdoor environments for improved LiDAR functionality.

Main Methods:

  • Utilizing photon-number-resolving detectors to record echo signal photon numbers.
  • Implementing an active photon number filter to mitigate background noise.
  • Comparing performance with traditional on/off detection methods.

Main Results:

  • The proposed LiDAR approaches the standard quantum limit in intensity estimation across a broad photon-flux range.
  • Achieved Fisher information is only 0.04 dB less than quantum Fisher information at a mean signal photon number of 10.
  • Demonstrated noise-free daytime target reconstruction and imaging with superior reflectivity resolution using significantly fewer measurements.

Conclusions:

  • The developed LiDAR system effectively overcomes background noise, approaching quantum-limited performance.
  • This technology offers a fundamental strategy for rapid target extraction and material identification in complex environments.
  • The findings are crucial for advancing intelligent agents, including autonomous vehicles.