  The recent release of the third generation partnership project, Release 17,
calls for sub-meter cellular positioning accuracy with reduced latency in
calculation. To provide such high accuracy on a worldwide scale, leveraging the
received signal strength (RSS) for positioning promises ubiquitous availability
in the current and future equipment. RSS Fingerprint-based techniques have
shown a great potential for providing high accuracy in both indoor and outdoor
environments. However, fingerprint-based positioning faces the challenge of
providing a fast matching algorithm that can scale worldwide. In this paper, we
propose a cosine similarity-based quantum algorithm for enabling
fingerprint-based high accuracy and worldwide positioning that can be
integrated with the next generation of 5G and 6G networks and beyond. By
entangling the test RSS vector with the fingerprint RSS vectors, the proposed
quantum algorithm has a complexity that is exponentially better than its
classical version as well as the state-of-the-art quantum fingerprint
positioning systems, both in the storage space and the running time. We
implement the proposed quantum algorithm and evaluate it in a cellular testbed
on a real IBM quantum machine. Results show the exponential saving in both time
and space for the proposed quantum algorithm while keeping the same positioning
accuracy compared to the traditional classical fingerprinting techniques and
the state-of-the-art quantum algorithms.
