  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.
