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Workshop on Electronics Communication Engineering (WECE)

Parameter estimation for free-space continuous variable quantum key distribution based on PointNet

Cheng Guan, Rui Chen, Hao Zhang, Sheng Wang

Published Jan 16, 2024• 0 citations
Publisher

Abstract

Free-space continuous variable quantum key distribution has attracted attention due to its ability to provide a more widely available transmission channel. However, the key rate of free-space continuous variable quantum key distribution itself is not high, and a portion of the original key needs to be allocated for parameter estimation. In this article, we analyze the spatial correlation of free-space continuous variable quantum key distribution and propose the use of a nearby system to transmit an inexpensive signal. Parameter estimation is performed using a deep learning model to predict and reduce the amount of original key used for parameter estimation, thereby improving the key rate.

DOI
10.1117/12.3015359

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Free-space continuous variable quantum key distribution has attracted attention due to its ability to provide a more widely available transmission channel. However, the key rate of free-space continuous variable quantum key distribution itself is not high, and a portion of the original key needs to be allocated for parameter estimation. In this article, we analyze the spatial correlation of free-space continuous variable quantum key distribution and propose the use of a nearby system to transmit an inexpensive signal. Parameter estimation is performed using a deep learning model to predict and reduce the amount of original key used for parameter estimation, thereby improving the key rate.
In satellite-based free-space continuous-variable QKD (CV-QKD), the parameter estimation for the atmospheric channel fluctuations due to the turbulence effects and attenuation is crucial for analyzing and improving the protocol performance. In this paper, compressive sensing (CS) theory is applied to free-space CV-QKD to achieve the channel parameter estimation with low computational complexity and small amount of data. According to CS theory, the possibility of the sparse representation for free-space channel is analyzed and the two types of sparse reconstruction models for the channel parameters are constructed combining with the stability of the sub-channels. The most part of variable for parameter estimation is saved by using the model relying on the variables in the quantum signals, while all the variables can be used to generate the secret key by using the model relying on the second-order statistics of the variables. The methods are well adapted for the cases with the limited communication time since a little or no variable is sacrificed for parameter estimation. Finally, simulation results are given to verify the effectiveness of the proposed methods.
In satellite-based free-space continuous-variable QKD (CV-QKD), the parameter estimation for the atmospheric channel fluctuations due to the turbulence effects and attenuation is crucial for analyzing and improving the protocol performance. However, the partial key data usually need to be sacrificed for the parameter estimation leading to the secret key reduction and the possible information leakage, especially when the channel is varying. In this paper, compressive sensing (CS) theory is applied to free-space CV-QKD to achieve the channel parameter estimation with small amount of key data sacrifice and low computational complexity. According to CS theory, the possibility of the sparse representation for free-space channel is analyzed and the two types of sparse reconstruction models for the channel parameters are constructed combining with the stability of the sub-channels. The most part of key data for parameter estimation is saved by using the model constructed by the variables in the quantum signals, while all the key data can be saved and be used to generate the secret key by using the model constructed by the second-order statistics of the variables. Thus, the methods can generate more secret key, improve the secret key rate, and be well adapted for the cases with the limited communication time since fewer or no key data (variables) is sacrificed for parameter estimation. Finally, simulation results are given to verify the effectiveness of the proposed methods.
In principle, the number of orbital angular momentum (OAM) eigenstates is unbounded; therefore, OAM multiplexing could greatly increase the transmission rate. Compared with optical fibers, free-space channels fluctuate randomly over time due to the effects of atmospheric turbulence. Free-space channel estimation is necessary. Here, we study the channel parameter estimation of continuous-variable quantum key distribution (CVQKD) based on OAM, especially the influence of atmospheric turbulence on transmittance and excess noise. By comparing the secret key rate in the case of single link and multiplexing, the impact of the estimated parameters on the performance of the CVQKD system is evaluated. Simulation results show that the value of the estimated parameter will affect the secret key rate.
The effectiveness and accuracy of parameter estimation are guarantees for the high-performance and high-safety operation of practical continuous-variable quantum key distribution systems. A scheme based on Bayesian estimation is proposed to tackle the fluctuations of channels. In free-space channel, the Bayesian random-effects model is employed to construct complete prior information, and the channel state information extracted through pilot signals is applied to rectify the prior information. Compressed sensing technology is applied due to the sparsity of the free-space channel to reduce the cost of the system. Experimental results show that the proposed scheme has higher estimation accuracy under the free space, and the system performance is also enhanced. Moreover, the results under fiber show higher estimation accuracy and better stability than that of traditional methods. In conclusion, the scheme can create practical conditions for the construction of future global quantum networks.
For the maximum-likelihood parameter estimation (MPE) of continuous-variable quantum key distribution (CVQKD), it sacrifices partial key when estimating the channel parameters, and when the channel is varying, even slowly, legitimate communication parties must constantly exchange partial input signal and output signal to update the channel estimation. Therefore, the MPE leads to the following problems in practical application: (a) MPE achieved by randomly sampling the states can not accurately reflect the dynamic channel; (b) there exist a trade-off between the secret key rate (SKR) and the accuracy of parameter estimation in the finite-size regime; and (c) information leakage may be caused by the correlation between key variables. In this article, a novel parameter estimation method for CVQKD is proposed without affecting the security, and the consistency between the method and the physical model is given. Experimental simulation results show that the method can effectively improve the SKR and secure propagation distance. The experimental verification of the proposed method is given through the fiber system. Besides, the security analysis demonstrates that the method is secure against entanglement-distillation attack.
Continuous-variable quantum key distribution (CVQKD) allows legitimate parties to extract and exchange secret keys. However, the tradeoff between the secret key rate and the accuracy of parameter estimation still around the present CVQKD system. In this paper, we suggest an approach for parameter estimation of the CVQKD system via artificial neural networks (ANN), which can be merged in post-processing with less additional devices. The ANN-based training scheme, enables key prediction without exposing any raw key. Experimental results show that the error between the predicted values and the true ones is in a reasonable range. The CVQKD system can be improved in terms of the secret key rate and the parameter estimation, which involves less additional devices than the traditional CVQKD system.