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Multicore fiber-based quantum access network

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arxiv 1905.05932 v1 pith:IPPWZFU4 submitted 2019-05-15 quant-ph

classification quant-ph
keywords quantumaccesscorecostdivisionmultiplexingnetworknumber
verification ladder T0 review T1 audit T2 compute T3 formal
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We propose a quantum access network based on multicore fiber (MCF) to scale up the number of users in quantum key distribution (QKD) networks. The MCF is used as feeder fiber and single core single mode fibers (SSMFs) are used as drop fibers. Quantum signals (QSs) are integrated with classical signals (CSs) in both MCF and SSMFs to save deployment cost since access networks are cost sensitive. Due to the integration, spontaneous Raman scattering (SRS) and intercore crosstalk (ICXT) are the main impairment sources to QKD. To alleviate the noise, we propose a core and wavelength assignment scheme (CWAS) that a dedicated core of MCF is used to transmit QSs and the wavelengths of QSs are set lower than those of upstream signals. Also, we demonstrate that wavelength time division multiplexing (WTDM) is suitable for QSs which are required to support large number of quantum users, since WTDM can realize higher secure key rate (SKR) than time division multiplexing (TDM) and require lower cost than wavelength division multiplexing (WDM). Finally, the proposed quantum access network is verified experimentally. The experiment results are consistent with our analysis. The properties of SRS in the proposed architecture are shown in the experiments through the quantum bit error rates (QBERs) in different experimental case, which verifies the superiority of the proposed CWAS. Also, the characteristics of the SKRs prove that the number of receivers has a great impact on the performance of QSs using WTDM.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. From Provable to Practical: A Problem-Driven Survey of Classical and Machine-Learning Defenses for DV/CV Quantum Key Distribution

    quant-ph 2026-05 unverdicted novelty 2.0 of 10

    A problem-driven survey comparing classical and ML defenses for DV/CV QKD across nine problem classes, reporting selected performance metrics from prior work and proposing a benchmarking framework.

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