Simulating both parity sectors of the Hubbard model with tensor networks

Tensor networks are a powerful tool to simulate a variety of different physical models, including those that suffer from the sign problem in Monte Carlo simulations. The Hubbard model on the honeycomb lattice with nonzero chemical potential is one such problem. Our method is based on projected entangled pair states using imaginary-time evolution. We demonstrate that it provides accurate estimators for the ground state of the model, including cases where Monte Carlo simulations fail miserably. In particular, it shows near to optimal, that is linear, scaling in lattice size. We also present an approach to directly simulate the subspace with an odd number of fermions. It allows to independently determine the ground state in both sectors. Without a chemical potential, this corresponds to half-filling and the lowest-energy state with one additional electron or hole. We identify several stability issues, such as degenerate ground states and large single-particle gaps, and provide possible fixes.

Automated summary

Tensor networks are a powerful tool for simulating various physical models, overcoming sign problems in Monte Carlo simulations. Using imaginary-time evolution, accurate estimators for ground states of models like the Hubbard model have been provided. A method to directly simulate the subspace with an odd number of fermions has also been presented.