Annabelle Bohrdt

Annabelle Bohrdt is a Professor at the University of Regensburg's Institute of Theoretical Physics and an MCQST START Fellow at LMU Munich. She concurrently holds a postdoctoral fellowship at Harvard University and ITAMP. She leads the Theory of Correlated Matter and Quantum Data Group, focusing on quantum many-body systems, machine learning applications in physics, and quantum simulation.

Education: PhD from Technical University of Munich (with research exchange at Harvard University); Master's/Diploma from Technical University of Kaiserslautern.

Research Interests: Her work integrates numerical methods, machine learning, and quantum simulation to study strongly correlated systems. Key areas include:

• Neural quantum states and machine learning for quantum data analysis
• Fermi-Hubbard and t-J models in mixed dimensions
• Exotic quantum phases (stripes, skyrmions, fractionalization)
• Quantum state tomography and Hamiltonian reconstruction techniques
• Non-equilibrium dynamics in optical lattices

Publications Focus: Her recent articles (2023–2024) predominantly explore neural network applications in quantum physics, doped antiferromagnets, lattice gauge theories, and quantum simulation protocols. A strong emphasis on machine learning–quantum physics intersections is evident, with innovations in interpretable AI for quantum data.

Awards & Honors:

• Friedrich Hirzebruch-Promotionspreis (German National Academic Foundation Thesis Prize)
• Finalist for Deborah Jin Thesis Award (APS DAMOP 2022)
• MCQST START Fellowship

Advising & Team Leadership: Directs 13+ students (PhD/Master's/Bachelor's) at Universität Regensburg. Current research group includes projects on neural quantum states, Fermi-Hubbard models, and quantum data analysis. Actively recruits students for quantum many-body physics projects.

Teaching: Courses include Numerical Methods for Quantum Many-Body Systems (Winter 2023/24) and Machine Learning for Quantum Many-Body Physics (Summer 2023), blending theory with hands-on coding and research applications.