标题： 联合参数估计与多维校正用于连续变量量子密钥分发 显示英文标题

标题： Joint parameter estimation and multidimensional reconciliation for CV-QKD

摘要： 精确的量子信道参数估计对于连续变量量子密钥分发(CV-QKD)中的有效信息重合至关重要。然而，传统的最大似然(ML)估计器依赖于大量被丢弃的数据(或导频符号)，导致符号效率显著下降。此外，估计和重合阶段之间的分离可能会引入误差传播。在本文中，我们提出了一种新颖的联合消息传递方案，在贝叶斯框架内统一了信道参数估计和信息重合。通过利用期望最大化(EM)算法，所提出的方法在解码过程中同时估计未知参数，消除了对单独ML估计的需求。此外，我们引入了一种混合多维旋转方案，消除了对归一化反馈的要求，显著降低了经典信道开销。据我们所知，这是第一篇将多维重合和信道参数估计统一在CV-QKD中的工作，为使用最少导频的高效率重合提供了实用解决方案。 显示英文摘要

摘要： 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.