English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Recurrent neural networks (RNNs) are a class of neural networks that haveemerged from the paradigm of artificial intelligence and has enabled lots ofinteresting advances in the field of natural language processing.Interestingly, these architectures were shown to be powerful ansatze toapproximate the ground state of quantum systems. Here, we build over theresults of [Phys. Rev. Research 2, 023358 (2020)] and construct a more powerfulRNN wave function ansatz in two dimensions. We use symmetry and annealing toobtain accurate estimates of ground state energies of the two-dimensional (2D)Heisenberg model, on the square lattice and on the triangular lattice. We showthat our method is superior to Density Matrix Renormalisation Group (DMRG) forsystem sizes larger than or equal to $14 \times 14$ on the triangular lattice.

Supplementing Recurrent Neural Network Wave Functions with Symmetry and Annealing to Improve Accuracy