Detail

Keywords

Artificial Intelligence; Data Mining; Image Interpretation, Computer-Assisted; Image Processing, Computer-Assisted

Research group(s)

• Applied Quantum Computing Group
  Head: Laszlo Papp
  Research Area: Quantum imaging, radiomics and AI; Classic-Quantum adoption;
  Members:
  Sasan Moradi
  Laszlo Papp

Research interests

Biomorphic computing, quantum computing, machine learning, image processing, personalized medicine, tumour characterization

Techniques, methods & infrastructure

Techniques, Methods:

• Quantum machine learning and deep learning
• Quantum image analysis
• Quantum simulators and NISQs
• Biomorphic computing
• In vivo feature engineering
• Radiomics, holomics
• Ensemble learning




Infrastructure:

• Quantum simulation HPC (256 CPU cores, 7 TByre RAM)

Grants

• MORPHEDRON (2026)
  Source of Funding: aws (austria wirtschaftsservice), Proof of Concept (Large)
  Principal Investigator
• Quantum Image Analysis (2022)
  Source of Funding: Medical University of Vienna, Focus M Grant Scheme
  Principal Investigator
• Quantum Image Analysis (QIA) (2022)
  Source of Funding: Medical University of Vienna, Focus-M
  Principal Investigator
• Foundations of a Quantum Computational Lab at the CMPBME (2019)
  Source of Funding: Medical University of Vienna, Focus XL Grant Scheme
  Principal Investigator

Selected publications

1. Papp, L. et al. (2026) “The dawn of quantum AI in nuclear medicine: an EANM perspective,” The EANM Journal. Edited by , 3, p. 100227. Available at: https://doi.org/10.1016/j.eanmj.2026.100227.
2. Papp, L. et al. (2023) ‘DEBI-NN: Distance-encoding biomorphic-informational neural networks for minimizing the number of trainable parameters’, Neural Networks, 167, pp. 517–532. Available at: https://doi.org/10.1016/j.neunet.2023.08.026.
3. Moradi, S. et al. (2023) “Error mitigation enables PET radiomic cancer characterization on quantum computers,” European Journal of Nuclear Medicine and Molecular Imaging. Edited by , 50(13), pp. 3826–3837. Available at: https://doi.org/10.1007/s00259-023-06362-6.
4. Moradi, S. et al. (2022) ‘Clinical data classification with noisy intermediate scale quantum computers’, Scientific Reports, 12(1). Available at: https://doi.org/10.1038/s41598-022-05971-9.
5. Papp, L. et al. (2020) ‘Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [68Ga]Ga-PSMA-11 PET/MRI’, European Journal of Nuclear Medicine and Molecular Imaging, 48(6), pp. 1795–1805. Available at: https://doi.org/10.1007/s00259-020-05140-y.