用量子神经网络提升室内定位精度，实测优于传统方法。
Hybrid Quantum Neural Network based Indoor User Localization using Cloud Quantum Computing
- 设计可训练参数的混合量子神经网络，优化信号定位
- 在真实量子硬件上测试，定位误差低于量子指纹法
- 首次结合真实数据与实际量子设备验证算法实用性
本文提出一种基于接收信号强度指示（RSSI）的混合量子神经网络（HQNN），用于室内用户定位。利用公开的WiFi、蓝牙和Zigbee RSSI数据集评估所提HQNN性能，并与近期提出的量子指纹定位方法进行对比。结果表明，由于HQNN在量子电路中具有可训练参数，其性能优于使用固定量子电路的量子指纹算法。不同于以往工作，本文还通过云量子计算服务，在真实的IBM量子计算机上测试了HQNN与量子指纹算法的性能。因此，本研究在噪声中等规模（NISQ）量子设备上，基于真实世界RSSI定位数据集，检验了HQNN的实际表现。方法创新在于采用简单特征映射与少节点数的变分形式，并在真实量子硬件上验证，展示了其在现实场景中的可行性。
原文摘要 · Abstract (English)
This paper proposes a hybrid quantum neural network (HQNN) for indoor user localization using received signal strength indicator (RSSI) values. We use publicly available RSSI datasets for indoor localization using WiFi, Bluetooth, and Zigbee to test the performance of the proposed HQNN. We also compare the performance of the HQNN with the recently proposed quantum fingerprinting-based user localization method. Our results show that the proposed HQNN performs better than the quantum fingerprinting algorithm since the HQNN has trainable parameters in the quantum circuits, whereas the quantum fingerprinting algorithm uses a fixed quantum circuit to calculate the similarity between the test data point and the fingerprint dataset. Unlike prior works, we also test the performance of the HQNN and quantum fingerprint algorithm on a real IBM quantum computer using cloud quantum computing services. Therefore, this paper examines the performance of the HQNN on noisy intermediate scale (NISQ) quantum devices using real-world RSSI localization datasets. The novelty of our approach lies in the use of simple feature maps and ansatz with fewer neurons, alongside testing on actual quantum hardware using real-world data, demonstrating practical applicability in real-world scenarios.
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