Exploiting Quantum Computing for Indoor Localization

Quantum computing is a new efficient way of computing that utilizes the fundamental principles of quantum mechanics. It promises a huge leap forward in the time needed for solving a handful of important applied problems such as prime factorization, machine learning, quantum simulations, search, optimization, and many more. We tried to relate quantum computing methods to indoor localization problems. It is a problem of estimating objects’ location where the ordinary outdoor systems can not be easily deployed because of obstacles for a transmitted signal [1]. The potential improvements of exploiting quantum computing in solving this problem could Increase accuracy and decrease the time required for location determination. We have found a preprint with a cosine similarity-based quantum algorithm for indoor localization that is exponentially better in both time and space than its classical version [2]. In this report, we present our preliminary results in improving upon this algorithm that gives constant improvements with respect to the number of shots required.
 

[1] M. Chelly and N. Samama, “New techniques for indoor positioning, combining
deterministic and estimation methods,” in ENC-GNSS 2009: European Navigation
Conference-Global Navigation Satellite Systems, 2009, pp. 1–12.
[2] A. Shokry and M. Youssef, Quantum computing for location determination, 2021.
arXiv: 2106.11751 [quant-ph].