QRadar: A Deployable Quantum Euclidean Similarity Large-scale Localization System

Quantum computing is a fast-developing field that has the ability to tackle complex problems that conventional computers struggle with. Recently, quantum fingerprinting localization has been introduced, which allows for the implementation of large-scale location determination systems across the globe. In this paper, we introduce QRadar, a localization system that uses quantum similarity fingerprints based on the Euclidean similarity metric, which is an early and widely used measure. The computational complexity of QRadar is exponentially superior to classical systems. We explain how to generate the quantum fingerprint, how to encode the received signal strength (RSS) measurements as quantum particles, and we describe the quantum algorithm for computing the Euclidean similarity distance between the online RSS measurements and the fingerprint ones. Additionally, we investigate various sources of errors in quantum machines and how they affect the accuracy of QRadar. We then discuss how to choose an appropriate quantum machine to minimize localization errors.We installed QRadar on a real IBM Quantum Experience machine. The results we obtained from both the installation and simulations conducted on two real test sites confirm that QRadar can accurately determine the estimated location with a significant improvement in processing time when compared to conventional classical methods.