Neural network for excess noise estimation in continuous-variable quantum key distribution under composable finite-size security

TL;DR

This paper demonstrates that neural networks can be used for parameter estimation in continuous-variable quantum key distribution, providing tighter confidence intervals and higher secret-key rates with security guarantees, especially in finite-size regimes.

Contribution

It introduces a neural network-based parameter estimation method for CV-QKD that offers quantifiable security and improved key rates compared to traditional approaches.

Findings

Neural networks produce tighter confidence intervals in CV-QKD parameter estimation.

The method achieves higher secret-key rates under collective Gaussian attacks.

Security guarantees are maintained with quantifiable failure probabilities.

Abstract

Parameter estimation is a critical step in continuous-variable quantum key distribution (CV-QKD), especially in the finite-size regime where worst-case confidence intervals can significantly reduce the achievable secret-key rate. We provide a finite-size security analysis demonstrating that neural networks can be reliably employed for parameter estimation in CV-QKD with quantifiable failure probabilities $\epsilon_{PE}$, endowed with an operational interpretation and composable security guarantees. Using a protocol that is operationally equivalent to standard approaches, our method produces significantly tighter confidence intervals, unlocking higher key rates even under collective Gaussian attacks. The proposed approach yields tighter confidence intervals, leading to a quantifiable increase in the secret-key rate under collective Gaussian attacks. These results open up new perspectives for integrating modern machine learning techniques into quantum cryptographic protocols, particularly in practical resource-constrained scenarios.