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Electronics

Adjusting Optical Polarization with Machine Learning for Enhancing Practical Security of Continuous-Variable Quantum Key Distribution

Zicheng Zhou, Ying Guo

Published Apr 9, 2024• 5 citations
Publisher

Abstract

An available trick to mitigate the interference of environmental noise in quantum communications is to modulate signals with time-polarization multiplexing. Conversely, due to effects of the atmospheric turbulence in free space, the polarization of signals fluctuates randomly, resulting in feasible information leakage when direct polarization demultiplexing is carried out at the receiver, drowning out the noise-contained signals. For enhancing the practical security of the continuous-variable quantum key distribution (CVQKD), we propose a machine learning (ML) approach for optimization of the dynamic polarization control (DPC) of signals transmitted through atmospheric turbulence. An optimal DPC scheme can be adaptively adjusted with ML algorithms, which is based on the received signals at the receiver for solving the loophole problem of information leakage since it provides an accurate response to the polarization changes regarding the anamorphic signals. The performance of the CVQKD system can be increased in terms of secret key rates and maximal transmission distance as well. Numerical simulation shows the positive effect of the ML-based DPC while taking into account the secret key rate of the CVQKD system. The ML-based DPC effectively reduces the feasibility of information leakage and hence results in an increased secret key rate of the practical CVQKD system.

DOI
10.3390/electronics13081410

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An available trick to mitigate the interference of environmental noise in quantum communications is to modulate signals with time-polarization multiplexing. Conversely, due to effects of the atmospheric turbulence in free space, the polarization of signals fluctuates randomly, resulting in feasible information leakage when direct polarization demultiplexing is carried out at the receiver, drowning out the noise-contained signals. For enhancing the practical security of the continuous-variable quantum key distribution (CVQKD), we propose a machine learning (ML) approach for optimization of the dynamic polarization control (DPC) of signals transmitted through atmospheric turbulence. An optimal DPC scheme can be adaptively adjusted with ML algorithms, which is based on the received signals at the receiver for solving the loophole problem of information leakage since it provides an accurate response to the polarization changes regarding the anamorphic signals. The performance of the CVQKD system can be increased in terms of secret key rates and maximal transmission distance as well. Numerical simulation shows the positive effect of the ML-based DPC while taking into account the secret key rate of the CVQKD system. The ML-based DPC effectively reduces the feasibility of information leakage and hence results in an increased secret key rate of the practical CVQKD system.
Using time and polarization multiplexing technology for transmission can reduce the interference of free space light on quantum signals. However, because of the presence of the free space channel, during light propagation, the polarization of light undergoes random fluctuations, causing substantial light leakage during direct polarization demultiplexing at the receiver, which can overpower the quantum signal. In this paper, we present an enhanced polarization control strategy leveraging a sophisticated algorithm, improving upon an existing dynamic polarization control approach. Through simulations, our proposal shows promising performance improvements. Our work focuses on the optimization of the control algorithm within the context of the previous dynamic polarization control strategy. After identifying limitations and issues in the original algorithm, we implemented a new optimization algorithm to address these problems. Simulations indicate that our optimized algorithm effectively reduces light leakage and provides fast and accurate response to changes in the polarization state of light.
The goal of continuous variable quantum key distribution (CVQKD) is to be diffusely used and adopted in diverse scenarios, so the adhibition of atmospheric channel will play a crucial part in constituting global secure quantum communications. Atmospheric channel transmittance is affected by many factors and does not vary linearly, leading to great changes in signal-to-noise ratio. It is crucial to choose the appropriate modulation variance under different turbulence intensities to acquire the optimal secret key rate. In this paper, the four-state protocol, back-propagation neural network (BPNN) algorithm was discussed in the proposed scheme. We employ BPNN to CVQKD, which could adjust the modulation variance to an optimum value for ensuring the system security and making the system performance optimal. The numerical results show that the proposed scheme is equipped to improve the secret key rate efficiently.
This thesis investigates how machine learning (ML) can assist state-of-the-art continuous-variable quantum key distribution (CV-QKD) protocols to advance a global Quantum Internet. Relative wavefront errors between multiplexed classical reference pulses and quantum signals pose a significant challenge to the practical implementation of a satellite-Earth CV-QKD network across turbulent atmospheric channels, potentially leading to the destruction of secure key transfer. ML is proposed as a solution, where ML-based wavefront correction algorithms are designed to mitigate the potentially deleterious effects of relative wavefront errors on CV-QKD, as a result of excess noise affecting the quantum signals. This thesis provides the first analysis of two-dimensional spatial relative wavefront errors. The potential causes of relative wavefront errors are explored, their evolution across satellite-Earth channels is simulated, and their effect on achievable secure key rates is evaluated. ML-based wavefront correction algorithms are designed to facilitate enhanced key rates by encoding the wavefront corrections onto a real local oscillator at the receiver. The analysis of relative wavefront errors presented in this thesis culminates in a series of experimental campaigns involving the transmission of multiplexed stronger reference signals and weaker signals across a 2.4~km free-space optical link, providing the first experimental evidence of relative wavefront errors in the context of CV-QKD. Modified ML-based wavefront correction algorithms are then applied to the experimental data, using phase retrieval, resulting in up to a 2/3 reduction in relative phase error variance. Overall, it is shown that ML algorithms can augment state-of-the-art CV-QKD protocols across turbulent atmospheric channels, thereby progressing the practical feasibility of developing a truly global CV-QKD network.
The finite sampling bandwidth of an analog-to-digital converter has a negative effect on the continuous-variable quantum key distribution (CVQKD), which leaves a potential loophole for an eavesdropper and weakens the practical security of the system. To compensate for the loss in free space, we deploy an adaptive optics (AO) unit in the detector of the CVQKD system with discrete modulations. Since the closed-loop control bandwidth of the embedded AO unit can be optimized with the sampling frequency, the practical security of the system can be enhanced in terms of the secret key rate. The security analysis is demonstrated on the basis of the derived secret key rate with numerical simulations, providing a feasible implementation of CVQKD in realistic free-space environments.
Abstract The excess noise inducing in the process of the quantum communication procedure is the major obstacle restricting the performance of continuous-variable quantum key distribution (CVQKD). In order to effectively suppress the excess noise through correcting the propagation-induced distortions on the quality of the propagated quantum signal, we propose a general scheme of suppressing excess noise for CVQKD via adaptive optics (AO) approach. The analysis shows that phase-only AO compensation exhibits excellent performance in controlling the excess noise, which is embodied in substantially extending the secure propagation distance and improving the secret key rate of the system. And thereby the development and improvement of AO has the potential advantage to break the distance constraints due to the excess noise results from propagation-dominated factors. Our scheme provides a feasible method for further implementation of practical large-scale CVQKD.
Continuous variable quantum key distribution (CVQKD) has been implemented over the atmospheric channels over free space. However, atmospheric turbulence weakens the quality of the transmitting quantum signals and hence decreases the secret key rate of the system. Here, we suggest an atmospheric turbulence channel model that involves atmospheric turbulence bubbles and demonstrates the implementation feasibility of passive CVQKD with spectrum resources in the terahertz band over the atmospheric turbulence channel. We achieve the channel transmittance characterized by the refractive index and the wavefront distortions. Moreover, an adaptive optics (AO) unit is used for performance improvement while considering the effect of the thermal noise and excess noise on the atmospheric turbulence bubble-modeled channel. Numerical simulations show that the AO-involved detection scheme can result in reductions in excess noise when being faced with the floating clouds and mist in atmospheric turbulence, which results in performance improvements in terms of secret key rate, which confirms the utility of the high-rate and long-distance CVQKD in terahertz (THz) for practical implementations.