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Practical quantum computing (QC) is still in its infancy and problemsconsidered are usually fairly small, especially in quantum machine learningwhen compared to its classical counterpart. Image processing applications inparticular require models that are able to handle a large amount of features,and while classical approaches can easily tackle this, it is a major challengeand a cause for harsh restrictions in contemporary QC. In this paper, we applya hybrid quantum machine learning approach to a practically relevant problemwith real world-data. That is, we apply hybrid quantum transfer learning to animage processing task in the field of medical image processing. Morespecifically, we classify large CT-scans of the lung into COVID-19, CAP, orNormal. We discuss quantum image embedding as well as hybrid quantum machinelearning and evaluate several approaches to quantum transfer learning withvarious quantum circuits and embedding techniques.

Hybrid Quantum Machine Learning Assisted Classification of COVID-19 from Computed Tomography Scans