Hybrid Quantum Neural Network based Indoor User Localization using Cloud Quantum Computing

TL;DR

This paper introduces a hybrid quantum neural network for indoor user localization that outperforms existing quantum fingerprinting methods, tested on real quantum hardware with real-world RSSI data, demonstrating practical viability.

Contribution

It presents a novel hybrid quantum neural network with trainable parameters, tested on actual quantum hardware, advancing practical quantum machine learning for indoor localization.

Findings

HQNN outperforms quantum fingerprinting algorithm

Tested on real IBM quantum hardware

Effective with real-world RSSI datasets

Abstract

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.