Composably secure data processing for Gaussian-modulated continuous variable quantum key distribution

TL;DR

This paper presents a comprehensive, practical framework for secure data processing in Gaussian-modulated continuous-variable quantum key distribution, including simulation, parameter estimation, error correction, and privacy amplification, optimized for experimental setups.

Contribution

It introduces a Python library that models and optimizes the entire data processing pipeline for CV-QKD under finite-size, composable security assumptions, advancing practical implementation.

Findings

Validated the protocol's security in high signal-to-noise regimes

Developed an open-source tool for protocol parameter optimization

Demonstrated feasibility for short-range CV-QKD experiments

Abstract

Continuous-variable (CV) quantum key distribution (QKD) employs the quadratures of a bosonic mode to establish a secret key between two remote parties, and this is usually achieved via a Gaussian modulation of coherent states. The resulting secret key rate depends not only on the loss and noise in the communication channel, but also on a series of data processing steps that are needed for transforming shared correlations into a final string of secret bits. Here we consider a Gaussian-modulated coherent-state protocol with homodyne detection in the general setting of composable finite-size security. After simulating the process of quantum communication, the output classical data is post-processed via procedures of parameter estimation, error correction, and privacy amplification. In particular, we analyze the high signal-to-noise regime which requires the use of high-rate (non-binary) low-density parity check codes. We implement all these steps in a Python-based library that allows one to investigate and optimize the protocol parameters to be used in practical experimental implementations of short-range CV-QKD.