An Efficient Quantum Binary-Neuron Algorithm for Accurate Multi-Story
  Floor Localization
Accurate floor localization in a multi-story environment is an important but
challenging task. Among the current floor localization techniques,
fingerprinting is the mainstream technology due to its accuracy in noisy
environments. To achieve accurate floor localization in a building with many
floors, we have to collect sufficient data on each floor, which needs
significant storage and running time; preventing fingerprinting techniques from
scaling to support large multi-story buildings, especially on a worldwide
scale. In this paper, we propose a quantum algorithm for accurate multi-story
localization. The proposed algorithm leverages quantum computing concepts to
provide an exponential enhancement in both space and running time compared to
the classical counterparts. In addition, it builds on an efficient
binary-neuron implementation that can be implemented using fewer qubits
compared to the typical non-binary neurons, allowing for easier deployment with
near-term quantum devices. We implement the proposed algorithm on a real IBM
quantum machine and evaluate it on three real indoor testbeds. Results confirm
the exponential saving in both time and space for the proposed quantum
algorithm, while keeping the same localization accuracy compared to the
traditional classical techniques, and using half the number of qubits required
for other quantum localization algorithms.
Yousef Zook Ahmed Shokry Moustafa Youssef