Hybrid Quantum Neural Network Advantage for Radar-Based Drone Detection and Classification in Low Signal-to-Noise Ratio

2024/03/04 by Aiswariya Sweety Malarvanan, Malarvanan, Aiswariya Sweety
Biochemistry, Genetics and Molecular Biology · Engineering · #Applied Physics (physics.app-ph) #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Physical sciences #Infrared Target Detection Methodologies #Machine Learning (cs.LG) #Quantum Physics (quant-ph) #Signal Processing (eess.SP) #Spectroscopy Techniques in Biomedical and Chemical Research #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2403.02080

openalex publication_date 2024/03/04 · openalex created_date 2024/03/06 · openalex updated_date 2026/07/28

Abstract

In this paper, we investigate the performance of a Hybrid Quantum Neural Network (HQNN) and a comparable classical Convolution Neural Network (CNN) for detection and classification problem using a radar. Specifically, we take a fairly complex radar time-series model derived from electromagnetic theory, namely the Martin-Mulgrew model, that is used to simulate radar returns of objects with rotating blades, such as drones. We find that when that signal-to-noise ratio (SNR) is high, CNN outperforms the HQNN for detection and classification. However, in the low SNR regime (which is of greatest interest in practice) the performance of HQNN is found to be superior to that of the CNN of a similar architecture.

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