Speaker
Description
Obtaining and interpreting solutions to the quantum Hamilton constraint of LQG is a long-standing and difficult problem. In this talk, we review recent developments which approach this problem with novel numerical methods which harness the power of neural networks. We begin by introducing the basic idea of parameterising quantum states with a neural network. To illustrate its applicability, we consider 3d Euclidean gravity in Smolin’s weak coupling limit whereby the quantum theory is truncated by introducing a fixed graph and a cutoff on representations on account for computational feasibility. We then find and compare approximate solutions of the Thiemann regularised Hamilton constraint with a more naive regularisation and show that they quantitatively have much more in common than expected. Lastly, we present some preliminary results and work in progress in building towards finding solutions to the Thiemann regularised quantum Hamilton constraint of 4d gravity in Smolin’s weak coupling limit.
