Abstract
We investigate estimation of fluctuating channels and its effect on security of continuous-variable quantum key distribution. We propose a novel estimation scheme which is based on the clusterization of the estimated transmittance data. We show that uncertainty about whether the transmittance is fixed or not results in a lower key rate. However, if the total number of measurements is large, one can obtain using our method a key rate similar to the non-fluctuating channel even for highly fluctuating channels. We also verify our theoretical assumptions using experimental data from an atmospheric quantum channel. Our method is therefore promising for secure quantum communication over strongly fluctuating turbulent atmospheric channels.
Highlights
Quantum cryptography is well known to be the method for secure communication based on mathematically secure cryptosystems
Any implementation of free-space CV Quantum key distribution (QKD) which does not rely on fiber-optical infrastructure has to deal with the estimation of fading channels, especially QKD implementations aiming for long-distance extraterrestrial QKD through a satellite [21, 1]
We propose the clusterization of the transmitted data, which can practically compensate for the negative impact of channel fading on CV QKD for sufficiently large data sets
Summary
Quantum cryptography is well known to be the method for secure communication based on mathematically secure cryptosystems (such as the one-time pad [31]). Gaussian CV QKD protocols based on coherent [17] and squeezed states [22] were successfully tested in the channels with fixed transmittance and were studied for free-space atmospheric channels with transmittance fluctuations [35]. The issue was addressed for coherent [20, 33], squeezed-state [29] protocols and even for the measurement-device-independent settings [24] in fixed-type channels, i.e., channels in which the transmittance is typically stable (e.g., fiber-optical links). The paper is organized as follows: Section II describes the model applied in the article and the experiment which was used to validate it; in Section III we introduce the theory of the channel parameter estimation, mainly focusing on the fluctuating transmittance and examine how well the predicted results of the model coincide with the experimental data; in Section IV we explore the limitations and basic properties of the clusterization method and in Section V we discuss the results and give our conclusions
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