Gender Recognition from Facial Images using Hybrid Classical-Quantum Neural Network

Mittal, Shubham; Dana, S.K.

doi:10.1109/sces50439.2020.9236711

Cited by 6 publications

(1 citation statement)

References 29 publications

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“…The use of HQNNs has been explored for a diverse set of problems including drug response prediction [7], detection of dementia [8], multi-classification tasks [9] [10], remote sensing imagery classification [11], detection of cyberattacks [12], gender recognition from facial images [13], and for the classification of finance and MNIST data [14] [15].…”

Section: Introductionmentioning

confidence: 99%

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

Malarvanan

2024

Preprint

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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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