Image classification is a crucial task in machine learning with widespread practical applications. The existing classical framework for image classification typically utilizes a global pooling operation at the end of the network to reduce computational complexity and mitigate overfitting. However, this operation often results in a significant loss of information, which can affect the performance of classification models. To overcome this limitation, we introduce a novel image classification framework that leverages variational quantum algorithms (VQAs)-hybrid approaches combining quantum and classical computing paradigms within quantum machine learning. The major advantage of our framework is the elimination of the need for the global pooling operation at the end of the network. In this way, our approach preserves more discriminative features and fine-grained details in the images, which enhances classification performance. Additionally, employing VQAs enables our framework to have fewer parameters than the classical framework, even in the absence of global pooling, which makes it more advantageous in preventing overfitting. We apply our method to different state-of-the-art image classification models and demonstrate the superiority of the proposed quantum architecture over its classical counterpart through a series of experiments on public datasets. Our experiments show that the proposed quantum framework achieves up to a 9.21% increase in accuracy and up to a 15.79% improvement in F1 score, compared to the classical framework.

翻译：图像分类是机器学习中的关键任务，具有广泛的实际应用。现有经典图像分类框架通常在网络末端采用全局池化操作以降低计算复杂度并减少过拟合。然而，该操作往往导致显著的信息损失，从而影响分类模型的性能。为克服这一局限性，我们提出了一种基于变分量子算法（VQAs）的新型图像分类框架——该框架属于量子机器学习中融合量子与经典计算范式的混合方法。本框架的主要优势在于无需在网络末端使用全局池化操作。通过这种方式，我们的方法能保留图像中更具区分性的特征与细节信息，从而提升分类性能。此外，采用VQAs使本框架在未使用全局池化的条件下，仍比经典框架具有更少的参数数量，这使其在防止过拟合方面更具优势。我们将该方法应用于多种前沿图像分类模型，并通过在公开数据集上的系列实验证明了所提出的量子架构相对于经典架构的优越性。实验结果表明，与经典框架相比，本量子框架在准确率上最高提升9.21%，F1分数上最高提升15.79%。