Soft Reverse Reconciliation for Discrete Modulations

TL;DR

This paper introduces a soft reverse reconciliation method for discrete modulation CV-QKD that enhances error correction efficiency by enabling soft decoding without compromising security, leading to improved key rates.

Contribution

The work proposes a novel RRS procedure that provides soft metrics for CV-QKD with discrete modulations, improving mutual information and error correction performance.

Findings

RRS improves mutual information compared to hard decoding RR

Numerical simulations show over 1dB SNR gain with RRS

RRS achieves near soft decoding performance with hard decoding security

Abstract

The performance of the information reconciliation phase is crucial for quantum key distribution (QKD). Reverse reconciliation (RR) is typically preferred over direct reconciliation (DR) because it yields higher secure key rates. However, a significant challenge in continuous-variable (CV) QKD with discrete modulations (such as QAM) is that Alice lacks soft information about the symbol decisions made by Bob. This limitation restricts error correction to hard-decoding methods, with low reconciliation efficiency. This work introduces a reverse reconciliation softening (RRS) procedure designed for CV-QKD scenarios employing discrete modulations. This procedure generates a soft metric that Bob can share with Alice over a public channel, enabling her to perform soft-decoding error correction without disclosing any information to a potential eavesdropper. After detailing the RRS procedure, we investigate how the mutual information between Alice's and Bob's variables changes when the additional metric is shared. We show numerically that RRS improves the mutual information with respect to RR with hard decoding, practically achieving the same mutual information as DR with soft decoding. Finally, we test the proposed RRS for PAM-4 signalling with a rate 1/2 binary LDPC code and bit-wise decoding through numerical simulations, obtaining more than 1dB SNR improvement compared to hard-decoding RR.