NuMeriQS Projects

The goal of this project is to determine the spectrum of baryon resonances which encodes the key to understand the strong force at medium energies. The extraction of the spectrum from data is challenging and sophisticated, but numerically very costly, dynamical coupled-channel models are the method of choice.

The main challenge in Lattice Quantum Chromodynamics (LQCD) computations is efficiently solving large sparse linear systems of the form Dz = b, where D depends on a gauge field U and a mass constant m.

This project aims at combining complementary path integral Lagrangian and Hamiltonian methods in order to investigate lattice gauge theories from first principles. For this, efficient digitisations of non-Abelian gauge theories will be investigated and tested, a non-perturbative matching protocol between the Lagrangian and Hamiltonian lattice formulations will be developed and, as a first application, the running coupling constant in an Abelian gauge theory in 2+1 dimensions will be determined.

The main goal of this project is to combine different methodologies effectively, leveraging their strengths against weaknesses. By integrating hybrid stochastic techniques with machine learning, multidimensional Tensor Networks, and the Iterative Configuration Expansion from quantum chemistry, our long term goal is to create a powerful approach to challenging quantum many-body systems.

This project focuses on using tensor networks, quantum computing, and machine learning to study topological effects in lattice gauge theories (LGTs) of increasing complexity. The initial goal will be the simulation of (2+1)-dimensional LGTs with tensor networks and quantum computing, focusing on compact quantum electrodynamics (QED) with a topological Chern-Simons term.

NuMeriQS administrative project.

This project addresses the need for high performance computing expertise in many of the scientific projects in the NuMeriQS CRC. It offers central support, focusing on performance optimisation and addressing challenges related to the heterogeneous supercomputing resources.