Learning eigenstates of quantum many-body Hamiltonians within the symmetric subspaces using neural network quantum states

TL;DR

This paper introduces a symmetry-based method for neural network quantum states that efficiently targets specific eigenstates of quantum many-body Hamiltonians, significantly improving accuracy by reducing the effective Hilbert space size.

Contribution

The authors develop a symmetry-aware variational approach for neural network quantum states, enabling precise eigenstate calculations within symmetric subspaces, inspired by exact diagonalization techniques.

Findings

Substantial energy error reduction compared to non-symmetrized methods

Effective isolation of symmetry sectors improves optimization accuracy

Enhanced ability to compute degenerate eigenstates with different quantum numbers

Abstract

The exploration of neural network quantum states has become widespread in the studies of complicated quantum many-body systems. However, achieving high precision remains challenging due to the exponential growth of Hilbert space size and the intricate sign structures. Utilizing symmetries of the physical system, we propose a method to evaluate and sample the variational ansatz within a symmetric subspace. This approach isolates different symmetry sectors, reducing the relevant Hilbert space size by a factor approximately proportional to the size of the symmetry group. It is inspired by exact diagonalization techniques and the work of Choo et al. in Phys. Rev. Lett. 121, 167204 (2018). We validate our method using the frustrated spin-1/2 $J_1$-$J_2$ antiferromagnetic Heisenberg chain and compare its performance to the case without symmetrization. The results indicate that our symmetric subspace approach achieves a substantial improvement over the full Hilbert space on optimizing the ansatz, reducing the energy error by orders of magnitude. We also compare the results on degenerate eigenstates with different quantum numbers, highlighting the advantage of operating within a smaller Hilbert subspace.