The probability distribution effectively sampled by a complex Langevin process for theories with a sign problem is not known a priori and notoriously hard to understand. Diffusion models, a class of generative AI, can learn distributions from data. In this contribution, we explore the ability of diffusion models to learn the distributions created by a complex Langevin process.
Understanding nonperturbative regimes in Strong-Field Quantum Electrodynamics (SFQED) is essential for exploring fundamental processes in high-intensity laser-matter interactions. Despite significant progress in analyzing the Schwinger model, a systematic comparison of the underlying frameworks remains incomplete. In particular, direct contrasts between U(1) and Zₙ models within standard...
Real-time quantum field theories remain challenging due to the severity of the numerical sign problem. In this work, we successfully apply the complex Langevin (CL) method to SU(2) Yang-Mills theory in 3+1 dimensions. By introducing an anisotropic kernel, we stabilize simulations for real-time evolutions beyond the inverse temperature, enabling the first ab initio computations of unequal-time...
