量子-经典共存网络层析成像
Quantum-Classical Coexistence Network Tomography
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中文总结 AI 辅助
本研究针对量子-经典共存网络的信道表征难题，提出仅用端到端测量即可推断链路参数的层析成像框架，经实验和仿真验证了其有效性，并扩展了框架的适用拓扑与信道模型。
中文摘要 AI 辅助
量子-经典共存网络（QCNs）通过波分复用在量子信号与经典信号间共享光纤，为在现有电信基础设施上实现量子通信提供了可行路径。然而，同向和反向传播的经典业务会引入不同的退极化噪声，使信道表征变得复杂。我们开发了一种层析成像框架，仅通过端到端测量即可推断QCN的每条链路信道参数。我们首先对每条共存光纤建模，将信号演化分解为光子损耗、成功传输和三个与方向相关的退极化分量；随后推导闭式链路级估计器，并通过端节点对之间的乘法方程组将该方法扩展到星型拓扑网络，同时采用简单的经典信号方向切换协议来解决剩余未知量。在单链路实验测试台数据上，我们准确恢复了每条链路的退极化概率，在多种光纤长度和波长下，估计的过程保真度与贝叶斯过程层析成像基准密切吻合；剩余差距反映了仅考虑退极化的近似假设。由于缺乏多链路共存测试台，我们通过基于实测单链路信道构建的仿真路径验证了星型网络估计器。我们还从两个方向扩展了该框架：（i）将共存光纤分解为带损耗的退极化信号信道和拉曼噪声注入信道的信道模型，两者处于独立光学模式，构成完全正定且迹保持的张量积，其链路可观测量可精确简化为我们的基础模型；（ii）通过剥离算法（适用于树型网络）和最小二乘估计器（适用于网状网络）推广到任意拓扑，已通过树型和循环网状网络的蒙特卡洛模拟验证。
英文摘要
Quantum-classical coexistence networks (QCNs) share optical fiber between quantum and classical signals via wavelength-division multiplexing, offering a practical path to quantum communication over existing telecom infrastructure. However, co- and counter-propagating classical traffic introduce distinct depolarization noise, complicating channel characterization. We develop a tomography framework that infers per-link channel parameters of a QCN from end-to-end measurements alone. We first model each coexisting fiber by decomposing the signal evolution into photon loss, successful transmission, and three direction-dependent depolarization components. We then derive closed-form link-level estimators, and extend the approach to star-topology networks through a system of multiplicative equations across end-node pairs, together with a simple classical-signal-direction-switching protocol that resolves the remaining unknowns. On single-link experimental testbed data, we recover per-link depolarization probabilities accurately, with estimated process fidelities closely tracking the Bayesian-process-tomography baseline across multiple fiber lengths and wavelengths; residual gaps reflect the depolarization-only approximation. Absent a multi-link coexistence testbed, we validate the star-network estimators on emulated paths built from measured single-link channels. We further extend the framework in two directions: (i) a channel model that factorizes the coexisting fiber into a depolarizing-with-loss signal channel and a Raman-noise-injection channel on separate optical modes -- a completely-positive, trace-preserving tensor product -- whose link observables reduce exactly to our basic model; and (ii) a generalization to arbitrary topologies via a peeling algorithm (trees) and a least-squares estimator (meshes), validated by Monte-Carlo simulations on tree and cyclic-mesh networks.
