Quantum Sensor Network Algorithms for Transmitter Localization

TL;DR

This paper introduces quantum sensor network algorithms for precise transmitter localization, employing quantum state discrimination and hybrid quantum-classical methods to achieve meter-level accuracy.

Contribution

It develops a two-level localization method and hybrid measurement schemes to improve quantum-based transmitter localization accuracy.

Findings

Achieves 1-5 meter localization accuracy.

Near-perfect classification accuracy for discrete locations.

Demonstrates effectiveness of hybrid quantum-classical circuits.

Abstract

A quantum sensor (QS) is able to measure various physical phenomena with extreme sensitivity. QSs have been used in several applications such as atomic interferometers, but few applications of a quantum sensor network (QSN) have been proposed or developed. We look at a natural application of QSN -- localization of an event (in particular, of a wireless signal transmitter). In this paper, we develop effective quantum-based techniques for the localization of a transmitter using a QSN. Our approaches pose the localization problem as a well-studied quantum state discrimination (QSD) problem and address the challenges in its application to the localization problem. In particular, a quantum state discrimination solution can suffer from a high probability of error, especially when the number of states (i.e., the number of potential transmitter locations in our case) can be high. We address this challenge by developing a two-level localization approach, which localizes the transmitter at a coarser granularity in the first level, and then, in a finer granularity in the second level. We address the additional challenge of the impracticality of general measurements by developing new schemes that replace the QSD's measurement operator with a trained parameterized hybrid quantum-classical circuit. Our evaluation results using a custom-built simulator show that our best scheme is able to achieve meter-level (1-5m) localization accuracy; in the case of discrete locations, it achieves near-perfect (99-100\%) classification accuracy.