  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.
