Neural network approach to quasiparticle dispersions in doped antiferromagnets

Understand

Numerically simulating spinful, fermionic systems is of great interest from the perspective of condensed matter physics.

• However, the exponential growth of the Hilbert space dimension with system size renders an exact parameterization of large quantum systems prohibitively demanding.
• This is a perfect playground for neural networks, owing to their immense representative power that often allows to use only a fraction of the parameters that are needed in exact methods.
• Here, we investigate the ability of neural quantum states (NQS) to represent the bosonic and fermionic $t-J$ model - the high interaction limit of the Fermi-Hubbard model - on different 1D and 2D lattices.