JCMS: Simulating strongly correlated systems with neural quantum states

by Dr Hannah Lange (Ludwig Maximilians University Munich)

Monday 27 Apr 2026, 10:30 → 12:30 Europe/Stockholm

Albano 3: 6228 - Mega (22 seats) (Albano Building 3)

Simulating strongly correlated many-body quantum systems remains a major challenge for existing numerical and experimental platforms, yet it is essential for understanding a wide range of materials and phenomena, including high-temperature superconductivity. In this talk, I will present a machine-learning framework for large-scale simulations of many-body quantum systems. This approach uses neural networks to parametrize quantum many-body wave function coefficients, an idea known as neural quantum states (NQS). I will outline recent advances in NQS and demonstrate how they provide access to regimes beyond the reach of conventional methods such as matrix product states. As a concrete example, I will discuss our studies of single-layer and bilayer models relevant to cuprate and bilayer nickelate superconductors using fermionic versions of NQS. Finally, I will present how NQS can be used to simulate multiband models relevant to a range of unconventional superconductors.