Quantum Brain
← Back to papers

Generalized Numerical Framework for Improved Finite-Sized Key Rates with Rényi Entropy

Rebecca R. B. Chung, Nelly H. Y. Ng, Yu Cai·February 4, 2025·DOI: 10.1103/tyts-8v8j
Quantum Physics

AI Breakdown

Get a structured breakdown of this paper — what it's about, the core idea, and key takeaways for the field.

Abstract

Quantum key distribution requires tight and reliable bounds on the secret key rate to ensure robust security. This is particularly so for the regime of finite block sizes, where the optimization of generalized Rényi entropic quantities is known to provide tighter bounds on the key rate. However, such an optimization is often non-trivial, and the non-monotonicity of the key rate in terms of the Rényi parameter demands additional optimization to determine the optimal Rényi parameter as a function of block sizes. In this work, we present a tight analytical bound on the Rényi entropy in terms of the Rényi divergence and derive the analytical gradient of the Rényi divergence. This enables us to generalize existing state-of-the-art numerical frameworks for the optimization of the key rate. With this generalized framework, we show improvements in regimes of high loss and low block sizes, which are particularly relevant for long-distance satellite-based protocols.

Related Research

Quantum Intelligence

Ask about quantum research, companies, or market developments.