Joint parameter estimation and multidimensional reconciliation for continuous-variable quantum key distribution

TL;DR

This paper introduces a Bayesian joint estimation and reconciliation method for CV-QKD that improves efficiency by reducing data discard and eliminating separate parameter estimation, enhancing practical quantum communication.

Contribution

It presents a novel joint message-passing scheme using EM algorithm and a hybrid multidimensional rotation, unifying parameter estimation and reconciliation in CV-QKD.

Findings

Eliminates the need for separate ML estimation, reducing data loss.

Reduces classical channel overhead with hybrid multidimensional rotation.

First to unify multidimensional reconciliation and channel estimation in CV-QKD.

Abstract

Accurate quantum channel parameter estimation is essential for effective information reconciliation in continuous-variable quantum key distribution (CV-QKD). However, conventional maximum likelihood (ML) estimators rely on a large amount of discarded data (or pilot symbols), leading to a significant loss in symbol efficiency. Moreover, the separation between the estimation and reconciliation phases can introduce error propagation. In this paper, we propose a novel joint message-passing scheme that unifies channel parameter estimation and information reconciliation within a Bayesian framework. By leveraging the expectation-maximization (EM) algorithm, the proposed method simultaneously estimates unknown parameters during decoding, eliminating the need for separate ML estimation. Furthermore, we introduce a hybrid multidimensional rotation scheme that removes the requirement for norm feedback, significantly reducing classical channel overhead. To the best of our knowledge, this is the first work to unify multidimensional reconciliation and channel parameter estimation in CV-QKD, providing a practical solution for high-efficiency reconciliation with minimal pilots.