Several prototypes already exist that can be used to implement the project. One approach lies in hybrid classical “quantum transfer learning” with final localization of the image regions most likely to indicate pathology. This is performed on the quantum gate model. Another solution approach runs on the Quantum Boltzmann Machine which is calculated using quantum annealers.

One of the current challenges is that present quantum computers with the small number of qubits are not yet suitable to process whole CT scans on their own. For this reason, the hybrid approach is currently being forced, in which classical computers work together with quantum computers to do calculations and different architectures are being experimented with. The long-term goal is to maximize the quantum component and so achieve better results.

In the further course of the project, the various architectures will be evaluated and assessed. What are the weaknesses of the used quantum models and how good are the obtained results? On this basis, the models can be improved and extended. In addition, the newest medical findings from the COVID19 pandemic research are continuously integrated into the project.

If the use case “QKI-assisted diagnosis of COVID-19 in radiology images” succeeds in obtaining meaningful diagnoses, QKI methods can also be applied to other disease patterns in the long term and be a significant support to the healthcare sector.