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This research introduces a novel joint message-passing scheme that unifies channel parameter estimation and information reconciliation within continuous-variable quantum key distribution. By leveraging the expectation-maximization algorithm and hybrid multidimensional rotation, the method significantly reduces classical overhead while minimizing data disclosure requirements for high-efficiency communication. This advancement directly addresses critical bottlenecks in CV-QKD practicality by eliminating error propagation between separate processing phases.
Joint parameter estimation and multidimensional reconciliation for continuous-variable quantum key distribution
arXiv:2508.05558v3 Announce Type: replace 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 disclosed data, 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 information disclosure.
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 disclosed data, 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 information disclosure.
Quantum Physics (quant-ph); Signal Processing (eess.SP)
arXiv:2508.05558 [quant-ph]
(or arXiv:2508.05558v3 [quant-ph] for this version)
https://doi.org/10.1103/7w6s-93ql DOI(s) linking to related resources
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