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- W4385655798 abstract "In this work, we propose and experimentally validate a polarization basis alignment method based on a hybrid approach, using the Particle Swarm Optimization (PSO) and a gradient search algorithm. This method uses the Quantum Bit Error Ratio (QBER) as metric to be minimized. At first, the PSO runs one iteration to perform a coarse basis alignment, followed by the gradient search algorithm which performs a fine basis alignment, until the QBER is below the threshold. The polarization basis alignment method here proposed is implemented and tested at the receiver of a Quantum Key Distribution (QKD) system testbed. With this proposed hybrid basis alignment approach, a QBER below 1% can be achieved after, on average, nineteen QBER estimations, when using five particles for the PSO. These nineteen QBER estimations represent a 24% improvement regarding the method mostly used in the state of the art which." @default.
- W4385655798 created "2023-08-09" @default.
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- W4385655798 date "2023-07-02" @default.
- W4385655798 modified "2023-10-16" @default.
- W4385655798 title "AI-Assisted Polarization Basis Alignment for Quantum Key Distribution System Receivers" @default.
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- W4385655798 doi "https://doi.org/10.1109/icton59386.2023.10207321" @default.
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