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 ε₄, 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.
Galvão et al. (Wed,) studied this question.