Quantum-enhanced Network Tomography

TL;DR

This paper introduces quantum-enhanced techniques for network tomography, using quantum probes to improve the estimation of link transmissivities in optical networks, with algorithms ensuring identifiability and performance metrics.

Contribution

It proposes a novel quantum probe construction algorithm for network tomography that guarantees link identifiability and maximizes information gain.

Findings

Quantum probes carry information about channel transmissivity.

A probe construction algorithm guarantees link identifiability.

Performance metrics evaluate quantum advantage in transmissivity estimation.

Abstract

Network tomography refers to the use of inference techniques for inferring internal network states from end-to-end probes. Quantum probes, implemented by sending blocks of $n$ coherent-state pulses augmented with continuous-variable (CV) squeezing ($n=1$) or weak temporal-mode entanglement ($n>1$) over a lossy channel to a receiver with homodyne detection capabilities, are known to carry information about the channel transmissivity. Assuming a subset of nodes in an optical network is capable of sending and receiving such probes through intermediate nodes with all-optical switching capabilities, we leverage these quantum probes to estimate link transmissivities. To determine how to route the probes in a network, we propose a probe construction algorithm that guarantees link identifiability, while maximizing the number of information orthogonal sets of transmissivities. A set of probes induces a Fisher information matrix (FIM). We then derive two metrics, the determinant of the FIM and the trace of its inverse, to evaluate the performance of the probes. In particular, our results can be used to characterize the quantum improvement in estimating link transmissivities in a general optical network.