Quantum Transfer Learning (QTL) recently gained popularity as a hybrid quantum-classical approach for image classification tasks by efficiently combining the feature extraction capabilities of large Convolutional Neural Networks with the potential benefits of Quantum Machine Learning (QML). Existing approaches, however, only utilize gate-based Variational Quantum Circuits for the quantum part of these procedures. In this work we present an approach to employ Quantum Annealing (QA) in QTL-based image classification. Specifically, we propose using annealing-based Quantum Boltzmann Machines as part of a hybrid quantum-classical pipeline to learn the classification of real-world, large-scale data such as medical images through supervised training. We demonstrate our approach by applying it to the three-class COVID-CT-MD dataset, a collection of lung Computed Tomography (CT) scan slices. Using Simulated Annealing as a stand-in for actual QA, we compare our method to classical transfer learning, using a neural network of the same order of magnitude, to display its improved classification performance. We find that our approach consistently outperforms its classical baseline in terms of test accuracy and AUC-ROC-Score and needs less training epochs to do this.

翻译：量子迁移学习（QTL）近期作为一种混合量子-经典方法在图像分类任务中广受欢迎，它通过高效结合大型卷积神经网络的特征提取能力与量子机器学习（QML）的潜在优势发挥作用。然而，现有方法仅在该流程的量子部分使用基于门的变分量子电路。本文提出一种在基于QTL的图像分类中运用量子退火（QA）的方法。具体而言，我们采用基于退火的量子玻尔兹曼机作为混合量子-经典流水线的一部分，通过监督训练学习对真实世界大规模数据（如医学图像）进行分类。我们通过将其应用于三分类COVID-CT-MD数据集（一组肺部计算机断层扫描（CT）切片）来展示该方法。使用模拟退火作为实际QA的替代，我们将该方法与经典迁移学习进行对比，采用相同数量级的神经网络，以展示其改进的分类性能。我们发现，该方法在测试准确率和AUC-ROC评分上始终优于经典基线，且所需训练周期更少。