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.

该研究探索了量子深度学习模型在解决传统深度学习模型难以处理的具有挑战性的机器学习问题方面的潜力。我们介绍了一种新型模型架构，将经典卷积层与量子神经网络相结合，旨在超越现有最先进的准确性，同时保持紧凑的模型尺寸。该实验对来自 Bird-CLEF 2021 数据集的高维音频数据进行分类，我们的评估重点关注训练持续时间、模型准确性和总模型尺寸等关键指标。这项研究证明了量子机器学习在增强机器学习任务和解决当今实际机器学习挑战方面的潜力。