Recurrent neural network wave functions for Rydberg atom arrays on kagome lattice

Rydberg atom array experiments have demonstrated the ability to act as powerful quantum simulators, preparing strongly-correlated phases of matter which are challenging to study for conventional computer simulations. A key direction has been the implementation of interactions on frustrated geometries, to prepare exotic many-body states such as spin liquids and glasses. In this talk, I will demonstrate how two-dimensional recurrent neural network (RNN) wave functions can be applied to study the ground states of Rydberg atom arrays on the kagome lattice. I will also showcase the value of annealing in boosting the performances of RNNs in regions of the phase diagram where exotic phases may occur, corresponding to rough optimization landscapes. For Rydberg atom array Hamiltonians studied previously on the kagome lattice, the RNN ground states show no evidence of exotic spin liquid or emergent glassy behavior. In the latter case, I will argue that the presence of a non-zero Edwards-Anderson order parameter is an artifact of the long autocorrelations times experienced with quantum Monte Carlo simulations. This result emphasizes the utility of autoregressive models, such as RNNs, to explore Rydberg atom array physics on frustrated lattices and beyond.