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Journal of Physics Conference Series

Polarization control algorithm for QKD systems

E E Mekhtiev, Ilya Gerasin, Nikita Rudavin, Alexey V. Duplinsky, Yury Kurochkin

Published Dec 1, 2021• 9 citations
Publisher

Abstract

Abstract The crucial task for polarization-encoding fiber QKD is to compensate polarization drift occurring in a quantum channel. To solve this problem, the receiver usually uses a polarization controller. For proper operation, this device must be efficiently managed in real-time. In this work, a gradient-descent-based algorithm is proposed to solve this problem. The algorithm was implemented and tested on a QRate commercial QKD fiber system, that utilizes BB84-protocol. Low and stable QBER has been obtained during a day of continuous operation.

DOI
10.1088/1742-6596/2086/1/012092

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A novel method for simultaneous compensation of polarization drift in a fiber-optic quantum channel and a fully fiber-optic receiver has been successfully developed, implemented, and tested using a commercial QKD system. The method is based on the exact analytical solution we derived which gives the necessary corrective transformation for polarization. Noise and imperfections in any realistic system require iterative application of the computed corrections. In our case, polarization drift compensation was completed on average in 2-3 iterations. This is an improvement by a factor of 10-100 compared to the common methods based on hill-climbing algorithms.
A novel method for simultaneous compensation of polarization drift in a fiber-optic quantum channel and a fully fiber-optic receiver has been successfully developed, implemented, and tested using a commercial QKD system. The method is based on the exact analytical solution we derived which gives the necessary corrective transformation for polarization. Noise and imperfections in any realistic system require iterative application of the computed corrections. In our case, polarization drift compensation was completed on average in 2-3 iterations. This is an improvement by a factor of 10-100 compared to the common methods based on hill-climbing algorithms.
Abstract In various applications of optical communications, such as quantum key distribution (QKD) systems, the task of polarization control in optical fiber arises. A polarization controller (PC) on the receiver side is used to solve this problem. In our work, we investigate two machine learning approaches to polarization control: supervised learning (SL) and reinforcement learning (RL). We generalised analytical solution of the polarization control problem using SL-approach. Supervised learning algorithm was trained on simulations and then was validated in experimental setup. We compared supervised approach with analytical solution. The advantage of RL approach based on the capability of approximating nonlinear effects related to the imperfections of detection process. For the effective operation of polarization control algorithms, knowledge of the parameters of the PC is required. Reinforcement learning agent also eliminates control hardware calibration procedures, since the agent recognize necessary system parameters based on the experience of interacting with the environment. RL-agent was firstly pretrained on simulations and then was fine-tuned on the practical QKD system. We demonstrated that RL-agent’s tuning provides the 10% quantum bit error rate (QBER) on our setup, and converges in 10 steps.
Polarization drift due to propagation medium birefringence is a primary cause of increased quantum bit error ratio (QBER) in polarization-encoded quantum key distribution (QKD). The random nature of polarization drift and environmental instabilities cause it to vary over time, necessitating algorithms for effective polarization drift compensation in polarization-encoded optical fiber QKD links. I propose an algorithm based on processing time-multiplexed control pulses by unscented Kalman filter that minimizes the number of additional measurements that interrupt key exchange. I evaluated the robustness of the algorithm under various conditions, including random polarization drift velocities, and additional noise due to depolarization for weak-coherent BB84-like protocols, to determine the applicability limits of the proposed algorithm and its overhead.