An Efficient Quantum Euclidean Similarity Algorithm for Worldwide Localization
Ahmed Shokry, Moustafa Youssef

TL;DR
This paper introduces a quantum algorithm for wireless localization that significantly reduces storage and computation time by leveraging quantum entanglement and parallelism, enabling efficient global positioning.
Contribution
It presents a novel quantum Euclidean similarity algorithm that outperforms classical and existing quantum methods in both complexity and resource efficiency for localization.
Findings
Exponential improvement in time and space complexity over classical methods.
Successful implementation on IBM quantum hardware and simulations.
Accurate localization achieved with quantum algorithm in real-world tests.
Abstract
Fingerprinting techniques are widely used for localization because of their accuracy, especially in the presence of wireless channel noise. However, the fingerprinting techniques require significant storage and running time, which is a concern when implementing such systems on a global worldwide scale. In this paper, we propose an efficient quantum Euclidean similarity algorithm for wireless localization systems. The proposed quantum algorithm offers exponentially improved complexity compared to its classical counterpart and even the state-of-the-art quantum localization systems, in terms of both storage space and running time. The basic idea is to entangle the test received signal strength (RSS) vector with the fingerprint vectors at different locations and perform the similarity calculation in parallel to all fingerprint locations. We give the details of how to construct the quantum…
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Taxonomy
TopicsBlind Source Separation Techniques
