I am interested in modelling, simulating, learning and controlling quantum systems with applications in quantum technologies, often related to spin dynamics. A particular interesting aspect of this is to learn physical laws of a system to enable to control it. Applications are focused on quantum spin networks, spintronics, magnetic resonance imaging and spectroscopy, and chemical synthesis.
Zien Ma, S. M. Shermer, Oktay Karakuş, Frank C. Langbein. The Sim-to-Real Gap in MRS Quantification: A Systematic Deep Learning Validation for GABA. Preprint, February 2026. [PDF] [arXiv:2602.20289]
Z Ma, O Karakus, SM Shermer, FC Langbein. The Impact of Training Data on MRS Metabolite Quantification with Deep Learning. Poster. ISMRM & ISMRT Annual Meeting & Exhibition, Honolulu, Hawaiʻi, USA, 10-15 May 2025. [PDF:Abstract] [PDF:Poster]
CA Weidner, SP O’Neil, EA Jonckheere, FC Langbein, SG Schirmer. Energy Landscape Shaping for Robust Control of Atoms in Optical Lattices. New J. Phys. 27 064503, 2025. [arXiv:2501.12564] [DOI:10.1088/1367-2630/addc0d] [PDF]
Asmail Muftah, SM Shermer, Frank C Langbein. Texture Feature Analysis for Classification of Early-Stage Prostate Cancer in mpMRI. Proc AI in Healthcare (AIiH), Swansea, UK, September 2024. [PDF] [arXiv:2406.15571]
E Alwadee, X Sun, Y Qin, FC Langbein. Assessing and Enhancing the Robustness of Brain Tumor Segmentation using a Probabilistic Deep Learning Architecture. Proc ISMRM and ISMRT Annual Meeting and Exhibition, Singapore, May 2024. [PDF] [Slides:PDF] [Video]
E Alwadee, X Sun, Y Qin, FC Langbein. LATUP-Net: A Lightweight 3D Attention U-Net with Parallel Convolutions for Brain Tumor Segmentation. Computers in Biology and Medicine, 184:109353, 2025. [DOI:10.1016/j.compbiomed.2024.109353] [arXiv:2404.05911] [PDF]
Frank C Langbein. Metabolite Quantification with AI from MR Spectra. Cardiff/University of Chinese Academy of Sciences (UCAS) Workshop on Visual Computing, 29th January 2024. [PDF]
S. P. O’Neil, C. A. Weidner, E. A. Jonckheere, F. C. Langbein, S. G. Schirmer. Robustness of Dynamic Quantum Control: Differential Sensitivity Bounds. AVS Quantum Sci. 6, 032001, 2024. [DOI:10.1116/5.0196110] [arXiv:2401.00301] [PDF]
C. A. Weidner, E. A. Reed, J. Monroe, S. O’Neil, E. Maas, E. A. Jonckheere, F. C. Langbein, S. G. Schirmer. Robust Quantum Control in Closed and Open Systems: Theory and Practice. Automatica, 172:111987, 2025. [DIO:10.1016/j.automatica.2024.111987] [arXiv:2401.00294] [PDF]
Finding and Characterising Robust Quantum Controls. Faculti interview with Frank Langbein on robust quantum control, 25th May 2023. [URL: https://faculti.net/finding-and-characterising-robust-quantum-controls/]
Zien Ma, Oktay Karakus, Sophie Shermer, Frank Langbein. Quantification of Metabolites in Magnetic Resonance Spectra with Deep Learning: Insights on Simulated and Real Data. Presentation at the One Day Meeting: Synthetic Data for Machine Learning, The British Machine Vision Association and Society for Pattern Recognition, Wednesday 8 November 2023. [PDF:abstract]
E Jonckheere, SG Schirmer, FC Langbein, CA Weidner, S O’Neil. Disturbance-agnostic robust performance with structured uncertainties and initial state error in classical versus quantum oscillatory systems. Preprint, 2023. [arXiv:2305.03918] [PDF]