# C03

Circumventing the Sign Problem in Lattice Gauge Theories with Topological Terms

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. In particular, we aim to map out the phase diagram and study implications for condensed-matter physics, such as topological mass generation and the quantum Hall effect. Moreover, in collaboration with project C02, we will use machine learning enhanced Monte Carlo methods to study the Chern Simons-term in QED. Long-term plans include extending these methods to (3+1)-dimensional LGTs, including Lattice Quantum Chromodynamics with the θ-term.