Quantum-Classical Coexistence Network Tomography
2026-08-10 • Information Theory
Information TheoryNetworking and Internet Architecture
AI summaryⓘ
The authors study networks that carry both quantum and classical signals together over the same optical fibers, which is useful for quantum communication without new infrastructure. They create a method to figure out how each part of the network affects quantum signals by measuring only at the network ends, even though classical signals cause noise differently depending on their direction. Their approach includes models and calculations for simple links and complex network shapes, tested with experiments and simulations. They also improve the model to better represent noise sources and extend it to larger and more complicated network layouts.
quantum communicationwavelength-division multiplexingdepolarization noisequantum channel tomographyphoton lossoptical fiberstar topologyRaman noisetensor productnetwork topology
Authors
Xuchuang Wang, Joseph C. Chapman, Aneesh Ramaswamy, Matheus Guedes de Andrade, Yu-Zhen Janice Chen, Joseph M. Lukens, Gayane Vardoyan, Don Towsley
Abstract
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.