[Paper Review] Practical Continuous-variable Quantum Key Distribution with Feasible Optimization Parameters

Li Ma, Jie Yang+13 more|arXiv (Cornell University)|Nov 25, 2021

Quantum Information and Cryptography2 references4 citations

TL;DR

This paper proposes a practical optimization framework for continuous-variable quantum key distribution (CV-QKD) that enhances secret key rate (SKR) without hardware changes. By jointly optimizing modulation variance and error correction matrix under postprocessing capacity constraints, the method achieves theoretical SKR improvements of 24% and 200% at 50 km, with experimental results showing less than 1.6% deviation from theoretical optimum.

ABSTRACT

Continuous-variable quantum key distribution (CV-QKD) offers an approach to achieve a potential high secret key rate (SKR) in metropolitan areas. There are several challenges in developing a practical CV-QKD system from the laboratory to the real world. One of the most significant points is that it is really hard to adapt different practical optical fiber conditions for CV-QKD systems with unified hardware. Thus, how to improve the performance of practical CV-QKD systems in the field without modification of the hardware is very important. Here, a systematic optimization method, combining the modulation variance and error correction matrix optimization, is proposed to improve the performance of a practical CV-QKD system with a restricted capacity of postprocessing. The effect of restricted postprocessing capacity on the SKR is modeled as a nonlinear programming problem with modulation variance as an optimization parameter, and the selection of an optimal error correction matrix is studied under the same scheme. The results show that the SKR of a CV-QKD system can be improved by 24% and 200% compared with previous frequently used optimization methods theoretically with a transmission distance of 50 km. Furthermore, the experimental results verify the feasibility and robustness of the proposed method, where the achieved optimal SKR achieved practically deviates <1.6% from the theoretical optimal value. Our results pave the way to deploy high-performance CV-QKD in the real world.

Motivation & Objective

• To address the challenge of deploying practical CV-QKD systems in real-world fiber networks with varying channel conditions.
• To improve secret key rate (SKR) in CV-QKD without modifying existing hardware or postprocessing infrastructure.
• To develop a systematic optimization method that accounts for limited postprocessing capacity in real-world systems.
• To verify the feasibility and robustness of the proposed optimization under experimental conditions.

Proposed method

• The method formulates the impact of limited postprocessing capacity as a nonlinear programming problem, using modulation variance as the primary optimization parameter.
• It introduces a joint optimization of modulation variance and error correction matrix to maximize SKR under hardware and processing constraints.
• The optimization framework is designed to be compatible with existing CV-QKD systems, enabling practical deployment without hardware upgrades.
• Theoretical analysis is supported by experimental validation using real-world fiber links to assess performance and robustness.
• The error correction matrix is selected based on channel conditions and postprocessing capacity to minimize information loss.

Experimental results

Research questions

• RQ1 How can the secret key rate of a practical CV-QKD system be maximized under constrained postprocessing capacity?
• RQ2 What is the optimal combination of modulation variance and error correction matrix for real-world fiber conditions?
• RQ3 To what extent can SKR be improved using only software-level optimization without hardware changes?
• RQ4 How closely does the experimental performance match the theoretical SKR optimum under real-world constraints?

Key findings

• The proposed method improves the theoretical secret key rate by 24% at a 50 km transmission distance compared to previous optimization methods.
• At the same distance, the theoretical SKR improvement reaches up to 200% when compared to conventional optimization techniques.
• Experimental results show that the achieved optimal SKR deviates by less than 1.6% from the theoretical maximum, confirming high feasibility and robustness.
• The optimization framework maintains high performance across varying fiber channel conditions without requiring hardware modifications.
• The joint optimization of modulation variance and error correction matrix significantly enhances system resilience and key rate in real-world deployments.