Hybrid Quantum Neural Network Advantage for Radar-Based Drone Detection and Classification in Low Signal-to-Noise Ratio
This addresses drone detection for security applications, but it is incremental as it focuses on a specific domain and scenario.
The paper tackled drone detection and classification using radar in low signal-to-noise ratio (SNR) conditions, finding that a Hybrid Quantum Neural Network (HQNN) outperformed a classical CNN in low SNR, while CNN was better in high SNR.
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.