https://github.com/caitaozhan/quantumlocalization

Quantum Sensor Network Algorithms for Transmitter Localization
https://github.com/caitaozhan/quantumlocalization

localization quantum quantum-sensing transmitter

Last synced: about 1 month ago
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Quantum Sensor Network Algorithms for Transmitter Localization

• Host: GitHub
• URL: https://github.com/caitaozhan/quantumlocalization
• Owner: caitaozhan
• Created: 2022-08-12T19:24:11.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-11-02T14:27:19.000Z (about 1 year ago)
• Last Synced: 2023-11-02T15:32:59.300Z (about 1 year ago)
• Topics: localization, quantum, quantum-sensing, transmitter
• Language: Python
• Homepage: https://arxiv.org/abs/2211.02260
• Size: 12.1 MB
• Stars: 4
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## Quantum Sensor Network Algorithms for Transmitter Localization

A quantum sensor (QS) is able to measure various physical phenomena with extreme sensitivity. QSs have been used in several applications such as atomic interferometers, but few applications of a quantum sensor network (QSN) have been proposed or developed. We look at a natural application of QSN -- localization of an event (in particular, of a wireless signal transmitter). In this paper, we develop effective quantum-based techniques for the localization of a transmitter using a QSN. Our approaches pose the localization problem as a well-studied quantum state discrimination (QSD) problem and address the challenges in its application to the localization problem. In particular, a quantum state discrimination solution can suffer from a high probability of error, especially when the number of states (i.e., the number of potential transmitter locations in our case) can be high. We address this challenge by developing a two-level localization approach, which localizes the transmitter at a coarser granularity in the first level, and then, in a finer granularity in the second level. We address the additional challenge of the impracticality of general measurements by developing new schemes that replace the QSD's measurement operator with a trained parameterized hybrid quantum-classical circuit. Our evaluation results using a custom-built simulator show that our best scheme is able to achieve meter-level (1-5m) localization accuracy; in the case of discrete locations, it achieves near-perfect (99-100\%) classification accuracy.

Paper: https://arxiv.org/abs/2211.02260

IEEE QCE 2023 Presentation: [YouTube](https://www.youtube.com/watch?v=Mq49DCdVdIs)

```

@misc{zhan2023optimizing,

      title={Optimizing Initial State of Detector Sensors in Quantum Sensor Networks}, 

      author={Caitao Zhan and Himanshu Gupta and Mark Hillery},

      year={2023},

      eprint={2306.17401},

      archivePrefix={arXiv},

      primaryClass={quant-ph}

}

```