  The research explores the potential of quantum deep learning models to
address challenging machine learning problems that classical deep learning
models find difficult to tackle. We introduce a novel model architecture that
combines classical convolutional layers with a quantum neural network, aiming
to surpass state-of-the-art accuracy while maintaining a compact model size.
The experiment is to classify high-dimensional audio data from the Bird-CLEF
2021 dataset. Our evaluation focuses on key metrics, including training
duration, model accuracy, and total model size. This research demonstrates the
promising potential of quantum machine learning in enhancing machine learning
tasks and solving practical machine learning challenges available today.
