QuERLoc: Towards Next-Generation Localization with Quantum-Enhanced Ranging
<|reference_start|>QuERLoc: Towards Next-Generation Localization with Quantum-Enhanced Ranging: Remarkable advances have been achieved in localization techniques in past decades, rendering it one of the most important technologies indispensable to our daily lives. In this paper, we investigate a novel localization approach for future computing by presenting QuERLoc, the first study on localization using quantum-enhanced ranging. By fine-tuning the evolution of an entangled quantum probe, quantum ranging can output the information integrated in the probe as a specific mapping of distance-related parameters. QuERLoc is inspired by this unique property to measure a special combination of distances between a target sensor and multiple anchors within one single physical measurement. Leveraging this capability, QuERLoc settles two drawbacks of classical localization approaches: (i) the target-anchor distances must be measured individually and sequentially, and (ii) the resulting optimization problems are non-convex and are sensitive to noise. We first present the theoretical formulation of preparing the probing quantum state and controlling its dynamic to induce a convexified localization problem, and then solve it efficiently via optimization. We conduct extensive numerical analysis of QuERLoc under various settings. The results show that QuERLoc consistently outperforms classical approaches in accuracy and closely follows the theoretical lowerbound, while maintaining low time complexity. It achieves a minimum reduction of 73% in RMSE and 97.6% in time consumption compared to baselines. By introducing range-based quantum localization to the mobile computing community and showing its superior performance, QuERLoc sheds light on next-generation localization technologies and opens up new directions for future research.<|reference_end|>
@article{he2024querloc:,
    title={QuERLoc: Towards Next-Generation Localization with Quantum-Enhanced
  Ranging},
    author={Entong He, Yuxiang Yang, Chenshu Wu},
    journal={arXiv preprint arXiv:2404.16895},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.16895},
    primaryClass={cs.ET}
}