Tighter Asymptotic Key Rates for Intensity-Correlated Decoy-State QKD via Nonlinear Programming

TL;DR

This paper introduces a nonlinear programming approach to improve the asymptotic key rate bounds in intensity-correlated decoy-state QKD, addressing realistic source imperfections and outperforming traditional linearization methods.

Contribution

It proposes a two-stage nonlinear optimization method using IPOPT to tighten key rate bounds in correlated decoy-state QKD, surpassing standard linearization techniques.

Findings

Tighter key-rate bounds achieved with the new nonlinear approach.

Method can certify optimality in certain scenarios.

Outperforms traditional linearization around reference points.

Abstract

Decoy-state QKD with phase-randomized weak coherent pulses is typically analyzed assuming independent, precisely prepared intensities. Real sources, however, can exhibit correlated intensity drift across rounds, potentially leaking intensity information and breaking the standard decoy-state reduction to linear programs. Cauchy--Schwarz (CS) constraints can restore security by coupling $n$-photon yields across intensities, but they introduce nonlinear square-root constraints that are commonly handled via outer linearisation around channel-model-based reference points. We propose a reproducible alternative: first solve the full CS-constrained parameter-estimation problems using the interior-point nonlinear solver IPOPT, then use the resulting candidate solution as the linearisation point for the outer optimisation that certifies a valid lower bound on the asymptotic key rate. Simulations for both coarse-grained model-independent correlations and fine-grained truncated-Gaussian models show consistently tighter key-rate bounds than canonical reference points, and in some cases allow certifying optimality when both optimisation stages coincide.