Atmospheric rivers (ARs) represent narrow corridors that facilitate the predominant poleward transportation of water vapor in the midlatitudes. These corridors exhibit notable features such as elevated water vapor levels and robust lower-level winds, playing a role in the expansive warm conveyor belt associated with extratropical cyclones. The meridional movement of water vapor within ARs holds significant importance for water reserves, yet their interaction with mountainous regions can lead to severe flooding events. Quantum neural networks are an emerging field combining quantum computing with artificial neural networks. The idea is that the computational advantage of quantum computing could potentially improve the performance of neural networks including those used for the complex task of atmospheric river detection.