Tracking reference phase with a Kalman filter in continuous-variable quantum key distribution.

Area of Science:

• Quantum Information Science
• Quantum Cryptography
• Optical Communication Systems

Background:

• Continuous-variable quantum key distribution (CV-QKD) with a local local-oscillator is vulnerable to phase compensation noise from inaccurate reference phase estimation.
• Residual optical frequency differences can cause slow drifts, degrading the performance of CV-QKD systems.

Purpose of the Study:

• To develop a robust phase estimation method for CV-QKD systems.
• To mitigate the effects of both fast and slow phase drifts caused by optical frequency differences.
• To enhance the accuracy and security of quantum key distribution.

Main Methods:

• Utilized a vector Kalman filter to estimate and track the reference phase using pilot signals.
• Incorporated both fast and slow drifts into the reference phase variation model.
• Theoretically deduced the mean square error of reference phase estimation.
• Designed a real-time phase noise variance estimation method.

Main Results:

• The vector Kalman filter provides more accurate phase estimation compared to conventional scalar Kalman filters.
• Simulations demonstrated superior performance in estimation accuracy, reduced excess noise, and increased secret key rates.
• The vector Kalman filter proved effective even under significant phase noise and large optical frequency differences.

Conclusions:

• The vector Kalman filter is a highly effective method for reference phase estimation in CV-QKD systems.
• This approach significantly improves the robustness and security of CV-QKD against phase noise and frequency drifts.
• The proposed method offers a practical solution for enhancing the performance of CV-QKD in real-world scenarios.