An Optimization Framework for Monitor Placement in Quantum Network Tomography
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Abstract
Quantum Network Tomography (QNT) offers a framework for end-to-end quantum channel characterization by strategically placing monitor nodes within the network. Building upon prior work on single-monitor placement, we study optimal monitor placement and measurement assignments for channel parameter estimation in arbitrary quantum networks. Using an n-node star network as a baseline, we analyze multi-monitor configurations and show that distributing monitors across end nodes can achieve estimation performance comparable to a monitor placed at the hub. Estimation precision is quantified using the Quantum Fisher Information Matrix (QFIM), with channel parameters inferred via Maximum Likelihood Estimation (MLE) and benchmarked against the Quantum Cramer-Rao Bound (QCRB). To generalize, we develop two Integer Linear Program (ILP) formulations: one maximizing estimation accuracy (QF), and another jointly optimizing accuracy and monitoring overhead (QMF). Unlike QF, QMF prevents monitor overloading, enabling scalability and parallelism. We prove optimality for star and analyze applicability to tree-structured quantum networks.