Research
Our group develops theoretical and computational methods to address open problems in strongly coupled quantum field theories. In particular, we design new algorithms using lattice field theory, tensor networks, deep learning, and quantum computing to study models ranging from (1+1) to (3+1) dimensions, with applications in particle and condensed matter physics. Our work is carried out in collaboration with theoretical and experimental physicists, mathematicians, and computer scientists.
Variational Quantum Eigensolver (VQE)
Development of tailored ansatz circuits for computing ground states of lattice gauge theories within the Variational Quantum Eigensolver framework, with particular emphasis on systems in (2+1) dimensions.
Adaptive Variational Quantum Eigensolver (ADAPT-VQE)
Investigation of adaptive circuit construction strategies in variational quantum algorithms. Key directions include symmetry-informed operator pools and the analysis of general properties and convergence behaviour of ADAPT-VQE.
Sampling-based Krylov Quantum Diagonalization (SKQD)
Study of quantum algorithms based on real-time evolution for sampling Krylov subspaces, enabling efficient ground-state approximation within reduced Hilbert spaces.
Qudits
Exploratory research on qudit-based quantum computing architectures for lattice gauge theory simulations. These emerging platforms offer a promising route toward more resource-efficient and accurate simulations of higher-dimensional systems.
Tree Tensor Networks (TTN)
Application of Tree Tensor Network methods to lattice gauge theories in (3+1) dimensions, with the aim of simulating topological terms and exploring emergent physical properties.
Matrix Product States
Computation of the step-scaling function in the Hamiltonian formulation of (2+1)-dimensional U(1) gauge theory using matrix product states. This work targets parameter regimes that are difficult to access with conventional Monte Carlo methods.
Step Scaling
Computation of the step-scaling function in the Hamiltonian formulation of (2+1)-dimensional U(1) gauge theory, aimed at charting parameter regions that are inaccessible to conventional Monte Carlo approaches.
Entanglement Entropy in Quantum Field Theory
Investigation of entanglement measures in strongly interacting quantum many-body systems and quantum field theories, using a combination of Monte Carlo simulations and tensor network techniques.
Normalizing Flows for the Hubbard Model
Development of machine-learning-based methods for the ergodic simulation of strongly coupled lattice systems, including the Hubbard model. Approaches include normalizing flows, symmetry-aware architectures, and annealing-based techniques.
Density of States with Normalizing Flows
Investigation of the density of states method for lattice field theories with a sign problem. The approach is applied to models such as the Hubbard model and U(1) gauge theory, using normalizing flows to reconstruct the generalized density of states.
Stochastic Normalizing Flows and Non-equilibrium Monte Carlo
Development of hybrid Monte Carlo and machine-learning algorithms for ergodic and scalable sampling of Hubbard and gauge field theories.
Enhancing the Adaptive Moment Estimation (ADAM) Optimizer
Development of an Extreme Value Extension (EVE) to the ADAM optimizer, designed to improve robustness in highly ill-conditioned optimization problems.
Lattice Field Theory at Finite Density
Study of lattice QCD at finite chemical potential, with a focus on different methods to address the sign problem. Approaches include imaginary chemical potential, Lefschetz thimbles, and density-of-states techniques, applied to the exploration of the QCD phase diagram.
Method Development for Lattice Field Theory
Development of methods for reconstructing spectral functions and transport coefficients from Euclidean correlators, as well as generative machine-learning models—such as masked autoregressive networks and normalizing flows—for improved sampling.
Master-Theses
Investigation of adaptive circuit construction strategies in variational quantum algorithms. Key directions include symmetry-informed operator pools and the analysis of general properties and convergence behaviour of ADAPT-VQE.
Extending an existing VQE framework for (2+1)D QED to incorporate multiple flavors of fermions and thus enabling the description of the theory at finite density. Existence of phase transitions was shown and these were subsequently studied.
Using generative neural networks, called normalizing flows, to sample from the Boltzmann distribution of the phi4-theory and the Hubbard model. By exploiting specific symmetries of these systems, training is accelerated and the sample-quality is improved.
This thesis applies machine-learning-based optimization to VQE on NISQ devices, combining gaussian process regression with bayesian optimization via EMICoRe. Simulations show improved performance and robustness over state-of-the-art methods, even under limited measurements and hardware noise.
Bachelor-Theses
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In this thesis, the dark energy equation-of-state parameter w_0 is investigated within quantum gravity theories based on the S-matrix formulation, with a particular focus on string theory. Analytical and numerical predictions for the value of w_0 are derived, and the capability of the Euclid and SKA Phase 1 experiments to test these predictions is assessed.
Affiliations
The University of Bonn has established several Transdisciplinary Research Areas (TRAs) units to foster interdisciplinary collaboration and address complex challenges. These TRAs serve as platforms where researchers from diverse fields converge to explore innovative solutions and collaborate.
The TRA Matter is one of the Transdisciplinary Research Areas (TRAs) at the University of Bonn, focusing on the fundamental properties of matter and their implications for science and society. This TRA brings together researchers from diverse fields such as physics, chemistry, biology, mathematics, and material sciences to explore and understand the complex behaviors, structures, and transformations of matter.
Link to the webpage: https://www.uni-bonn.de/de/forschung-lehre/forschungsprofil/transdisziplinaere-forschungsbereiche
The Helmholtz Institute for Radiation and Nuclear Physics (HISKP) is a prominent research institute within the University of Bonn. HISKP is dedicated to advancing the fields of physics through cutting-edge research and education. The research focus lies mainly on experimental and theoretical hadron and nuclear physics as well as laser spectroscopy, solid state and applied physics. Our group at HISKP is at the forefront of research in quantum computing and machine learning, developing innovative methods to advance computational physics and simulations.
Link to the webpage: https://www.hiskp.uni-bonn.de/
The Bethe Center for Theoretical Physics (BCTP) is a collaborative research institute at the University of Bonn, Germany, uniting theoretical physicists and mathematicians from various university institutes. Established in 2008 and named after Nobel laureate Hans Bethe, the center serves as a hub for interdisciplinary research across a wide spectrum of theoretical and mathematical physics.
Link to the webpage: https://www.bctp.uni-bonn.de/
The Lamarr Institute for Machine Learning and Artificial Intelligence is a leading research center dedicated to advancing high-performance, trustworthy, and resource-efficient AI solutions. Established with permanent institutional funding from the Federal Ministry of Education and Research (BMBF) and the state of North Rhine-Westphalia, the institute also emphasizes interdisciplinary research, applying AI to fields like planning and logistics, physics, industry and production, life sciences, and natural language processing.
Link to the webpage: https://lamarr-institute.org/
Coordinated Research Programs
The CRC1639 NuMeriQS is an interdisciplinary collaborative research center funded by the DFG, bringing together scientists from theoretical chemistry and theoretical physics with numerical mathematicians and computer scientists.
Link to the webpage: https://numeriqs.hiskp.uni-bonn.de/
The "Color meets Flavor" (CmF) Cluster of Excellence is a premier research network in Germany dedicated to uncovering the fundamental laws of particle physics. Funded under the German Excellence Strategy from 2026–2032, the cluster brings together experts from the University of Bonn, TU Dortmund University, the University of Siegen, and Forschungszentrum Jülich.
Our research focuses on the intricate interplay between "Color" (the strong interaction) and "Flavor" (the weak interaction) to explore the building blocks of the universe. By combining theoretical precision with high-intensity experimental data from facilities like the Large Hadron Collider (LHC) and the ELSA accelerator, we aim to identify phenomena beyond the Standard Model and gain new insights into the Higgs boson and the search for dark matter.
Link to the webpage: Color meets Flavor website.
ML4Q is a consortium of scientists with backgrounds in the key disciplines of quantum computation: condensed matter physics, quantum optics, quantum devices, and quantum information. We aim to push the frontiers of the field by developing novel forms of quantum hard- and software: from fundamental research on quantum matter over quantum information devices to operation protocols and software. Our research focus is on pioneering technologies that are at an early development stage today, but may become game changers tomorrow. ML4Q is the project synergizing the Rhineland into a hub in quantum computing research. A key part of our mission is the training of the next generation of researchers that will pull quantum computation past the application threshold.
Link to the webpage: https://ml4q.de/