Abstract
Quantum computing and quantum annealing present promising avenues for addressing complex problems in various fields, including tomographic image reconstruction. This study investigates the application of hybrid quantum annealing in the context of tomographic image reconstruction, focusing on the formulation of compatible conventional image regularization strategies: L2 and total variation. Using a Shepp-Logan phantom of image size 32 × 32 with 4-bit grayscale encoding, we study the effect of the regularization techniques under the influence of their parameters and the runtime of the hybrid quantum annealer. The study reveals, that L2 regularization effectively enhances the obtained image reconstructions and total variation can further improve them. Despite efforts to employ regularized hybrid quantum annealing reconstructions, they still fall short in comparison to traditional reconstruction techniques.
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© 2024 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Nau, M.A., Vija, A.H., Reymann, M.P., Gohn, W., Maier, A.K. (2024). Improving Hybrid Quantum Annealing Tomographic Image Reconstruction with Regularization Strategies. In: Maier, A., Deserno, T.M., Handels, H., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2024. BVM 2024. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-44037-4_3
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DOI: https://doi.org/10.1007/978-3-658-44037-4_3
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