Supplementing Recurrent Neural Network Wave Functions with Symmetry and Annealing to Improve Accuracy
This work addresses the challenge of accurately modeling quantum systems for researchers in computational physics, though it is incremental as it builds on prior results.
The authors tackled the problem of approximating ground state energies of quantum systems by enhancing a recurrent neural network (RNN) wave function ansatz with symmetry and annealing, achieving superior accuracy to Density Matrix Renormalisation Group (DMRG) for system sizes of 14x14 or larger on the triangular lattice.
Recurrent neural networks (RNNs) are a class of neural networks that have emerged from the paradigm of artificial intelligence and has enabled lots of interesting advances in the field of natural language processing. Interestingly, these architectures were shown to be powerful ansatze to approximate the ground state of quantum systems. Here, we build over the results of [Phys. Rev. Research 2, 023358 (2020)] and construct a more powerful RNN wave function ansatz in two dimensions. We use symmetry and annealing to obtain accurate estimates of ground state energies of the two-dimensional (2D) Heisenberg model, on the square lattice and on the triangular lattice. We show that our method is superior to Density Matrix Renormalisation Group (DMRG) for system sizes larger than or equal to $14 \times 14$ on the triangular lattice.