Our group – by its vision, expertise and access to its clinical environment – is in a niche position to deliver practical quantum computing solutions in clinically-relevant problem domains. We have been active in this field since 2019 and built foundational expertise to achieve a high-level of scientific merit within our research domain. Throughout our activities, we provide complementary knowledge to those conducted at the Center as well as at the Medical University of Vienna (MUV) within the field of imaging, analysis and AI.
Research Area
Team members
Principal Investigator
Members
- Christoph Brandner
- QIA – Quantum Image Analysis, Focus-M 2022, CMPBME, MUV (08/2022 – 08/2023)
- QKSpace – QUANTUM K-space Image Reconstruction, Focus-S 2022, CMPBME, MUV (08/2022 – 02/2023)
- Tumor Characterization with Quantum Image Analysis. Microsoft Azure Quantum Credits Programme 2022 (02/2022 – 08/2022)
- Foundations of a Quantum Computational Lab at the CMPBME, Focus-XL 2019, CMPBME, MUV (01/2020 – 06/2022)
Selected peer-reviewed publications
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L Papp, D Haberl, B Ecsedi, C P Spielvogel, D Krajnc, M Grahovac, S Moradi, W Drexler, "DEBI-NN: Distance-encoding biomorphic-informational neural networks for minimizing the number of trainable parameters" , Elsevier Neural Networks, Volume 167, 2023, Pages 517-532, ISSN 0893-6080.
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Moradi, S., Brandner, C., Spielvogel, C., D. Krajnc, S. Hillmich, R. Wille, W. Drexler, L. Papp. "Clinical data classification with noisy intermediate scale quantum computers". Nature Sci Rep 12, 1851 (2022).
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Moradi, S., Spielvogel, C., Krajnc, D., et al. L. Papp. Error mitigation enables PET radiomic cancer characterization on quantum computers. Eur J Nucl Med Mol Imaging 50, 3826–3837 (2023).
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Grahovac M, Spielvogel CP, Krajnc D, Ecsedi B, Traub-Weidinger T, Rasul S, Kluge K, Zhao M, Li X, Hacker M, Haug A, Papp L. Machine learning predictive performance evaluation of conventional and fuzzy radiomics in clinical cancer imaging cohorts. Eur J Nucl Med Mol Imaging. 2023 May;50(6):1607-1620.