AAAI 2026technical0 citations
Q-MoFusion: A Quantum Classifier for Masquito Species Classification (Student Abstract)
Vishesh Kumar, Ahana Chanda, Poulomi Bhattacharya, Akshay Agarwal
Abstract
Automated mosquito species identification is critical for combating vector-borne diseases. We introduce Q-MoFusion, a novel hybrid quantum-classical framework that fuses deep features from pre-trained Audio Spectrogram Transformer (AST) and Whisper models using a Variational Quantum Circuit (VQC). Our approach significantly outperforms individual backbones and prior state-of-the-art benchmarks, demonstrating superior accuracy and robustness, particularly on imbalanced classes. Q-MoFusion demonstrates the potential of hybrid quantum computing to enhance bioacoustic surveillance for addressing critical public health challenges.
BibTeX
@inproceedings{aaai2026_qmofusionaquantu,
title = {Q-MoFusion: A Quantum Classifier for Masquito Species Classification (Student Abstract)},
author = {Vishesh Kumar and Ahana Chanda and Poulomi Bhattacharya and Akshay Agarwal},
booktitle = {AAAI 2026},
year = {2026}
}