Quantum Sensor Network Algorithms for Transmitter Localization
Quantum Sensor Network Algorithms for Transmitter Localization
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DOI：
10.1109/qce57702.2023.00081
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Caitao Zhan;Himanshu Gupta
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文献类型：
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作者：
Caitao Zhan;Himanshu Gupta
量子传感器（QS）能够以极高的灵敏度测量各种物理现象。量子传感器网络已被应用于原子干涉仪等领域，但目前很少有人提出或开发量子传感器网络的应用。我们将研究事件（特别是无线信号发射机）的qsn定位的自然应用。在本文中，我们开发了有效的基于量子的技术，用于使用量子序列号定位发射机。我们的方法将局域化问题作为一个被充分研究的量子态判别（QSD）问题，并解决了将其应用于局域化问题中的挑战。特别是，量子态判别解决方案可能会遭受高概率的错误，特别是当状态的数量（即，在我们的情况下，潜在的发射机位置的数量）可能很高时。我们通过开发两级定位方法来解决这一挑战，该方法在第一级以较粗的粒度定位发射机，然后在第二级以较细的粒度定位发射机。我们通过开发新的方案来解决一般测量不实用的额外挑战，该方案将QSD的测量算子替换为经过训练的参数化混合量子-经典电路。我们在一个定制的模拟器上的评估结果表明，我们的最佳方案能够达到米级（1-5m）的定位精度；在离散位置的情况下，它达到了近乎完美（99-100%）的分类精度。
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-nerfect (99-100%) classification accuracy.