Neural network quantum states (NQS) are a promising method to simulate large fermionic systems. This work reports on accurate simulations of the t-J model in 1D and 2D lattices by means of NQS based on a recurrent neural network (RNN) architecture focusing on the calculation of dispersion relations, for which a general method is introduced, and on the performance of the RNN ansatz upon doping.
- Hannah Lange
- Fabian Döschl
- Annabelle Bohrdt
