Erlangen 2026 – wissenschaftliches Programm

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GR: Fachverband Gravitation, Relativistische Astrophysik und Kosmologie

GR 13: Quantum Gravity and Quantum Cosmology I

GR 13.6: Vortrag

Donnerstag, 19. März 2026, 15:00–15:15, KH 02.012

Exploring physical states of loop quantum gravity using neural networks — •Waleed Sherif and Hanno Sahlmann — Institute for Quantum Gravity, Department of Physics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Staudtstraße 7, 91058, Erlangen, Germany

Loop quantum gravity (LQG) is a canonical approach to the quantisation of general relativity that aims to preserve background independence. The dynamics of the theory is encoded in a set of constraints, among which the Hamiltonian constraint plays a central role. Constructing and analysing general physical states that satisfy this constraint has long remained one of the central open challenges in LQG.

In this talk, we show that this problem can be approached using modern deep learning techniques, specifically neural network quantum states (NQS). We demonstrate that these methods make it possible to construct approximate physical states in simplified models of LQG for the first time in a way that is both flexible and scalable. Using a range of models in Euclidean LQG, we illustrate how this approach enables large-scale numerical investigations of the theory's quantum dynamics, allowing for the systematic characterisation of physical states and exploring the impact of different operator orderings, regularisation choices and more.

Keywords: Loop quantum gravity; Neural network quantum states; Variational Monte Carlo; Quantum gravity