Quantum Sensor Network Algorithms for Transmitter Localization

• Published in: 2023 IEEE International Conference on Quantum Computing and Engineering (QCE) Vol. 1; pp. 659 - 669
• Main Authors: Zhan, Caitao, Gupta, Himanshu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 17.09.2023
• Subjects: Hybrid Quantum Algorithms; Location awareness; Quantum algorithm; Quantum Sensor Network; Quantum state; Quantum State Discrimination; Sensitivity; Transmitter Localization; Transmitters; Wireless communication; Wireless sensor networks
• DOI: 10.1109/QCE57702.2023.00081

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-nerfect (99-100%) classification accuracy.