Discrete outcome quantum sensor networks

TL;DR

This paper models a quantum sensor network using quantum state discrimination, showing that entangled initial states can enhance detection probability, with the advantage diminishing as the number of detectors grows.

Contribution

It introduces a quantum state discrimination framework for modeling quantum sensor networks and analyzes the impact of entanglement on detection performance.

Findings

Entangled initial states improve detection probability.

The advantage of entanglement decreases with more detectors.

Global measurements can optimize detection outcomes.

Abstract

We model a quantum sensor network using techniques from quantum state discrimination. The interaction between a qubit detector and the environment is described by a unitary operator, and we will assume that at most one detector does interact. The task is to determine which one does or if none do. This involves choosing an initial state of the detectors and a measurement. We consider global measurements in which all detectors are measured simultaneously. We find that an entangled initial state can improve the detection probability, but this advantage decreases as the number of detectors increases.