Quantum-Classical Coexistence Network Tomography

2026-08-10

AI Summary

In quantum-classical co-propagation over shared fiber, bidirectional classical light induces direction-dependent depolarizing noise, complicating accurate channel characterization. This work proposes a scalable, link-level closed-form estimator that relies solely on end-to-end measurements, integrating a direction-switching protocol with a physically realizable channel model based on optical mode decomposition to enable tomography in star and arbitrary network topologies. For tree-structured networks, a peeling algorithm solves the resulting multiplicative multi-endpoint equation system, while least-squares optimization is employed for mesh topologies. Experimental validation on a single link demonstrates high-precision recovery of depolarization parameters, with process fidelity closely matching Bayesian benchmarks. Simulations across multi-link networks further confirm the method’s effectiveness and scalability in complex topologies.

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.

Problem

• Quantum-Classical Coexistence
• Network Tomography
• Depolarization Noise
• Channel Characterization
• Wavelength-Division Multiplexing

Innovation

• quantum-classical coexistence
• network tomography
• depolarization noise
• direction-dependent channel modeling
• topology-agnostic estimation