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Finite-Size CV-QKD for Satellite Quantum Communication under a Hybrid Noise Model
arXiv eess.SP0 views
arXiv:2610.28353v1 Announce Type: new Abstract: Satellite-based continuous-variable quantum key distribution (CV-QKD) is fundamentally constrained by finite communication windows, channel attenuation, and statistical fluctuations arising from a limited number of exchanged quantum states. This work develops a finite-size CV-QKD framework for satellite quantum communication under a hybrid noise model that jointly incorporates satellite geometry, atmospheric and propagation losses, quantum Poissonian noise coexisting with classical additive white Gaussian noise, detector imperfections, and composable finite-key security under collective attacks. A single canonical finite-size secret key rate (SKR) expression is derived in which the finite-size correction is stated in a strictly decreasing form in the block size, removing the ambiguity between the segmented and the block-level formulations used in earlier treatments. The finite-size accounting is further extended to the quantum bit error ratio (QBER) through an explicit parameter estimation penalty, so that the feasibility constraint inherits the same block-size dependence as the key rate. The framework quantifies the influence of reconciliation efficiency, transmission efficiency, detection efficiency, receiver aperture, beam waist, zenith angle, wavelength, and effective communication time. Numerical results reveal a significant performance gap between finite-size and asymptotic operation, indicating that asymptotic analysis can overestimate the feasible satellite altitude by a factor of several, and that the achievable range is governed by the number of signals actually exchanged rather than by any single physical channel parameter. These results establish block size as a first-class design variable for satellite CV-QKD mission dimensioning.
Source:https://arxiv.org/abs/2610.28353PreviousStabilizing Filters for Graph Signal GenerationNextTopology-Adaptive Seismic Response Prediction of Interconnected Structural Systems Using Reduced Order Model Embedded Graph Neural Network
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