Bibliographic Details
| Title: |
Neural network evolution strategy for solving quantum sign structures |
| Authors: |
Ao Chen, Kenny Choo, Nikita Astrakhantsev, Titus Neupert |
| Source: |
Physical Review Research, Vol 4, Iss 2, p L022026 (2022) |
| Publisher Information: |
American Physical Society, 2022. |
| Publication Year: |
2022 |
| Collection: |
LCC:Physics |
| Subject Terms: |
Physics, QC1-999 |
| More Details: |
Feed-forward neural networks are a novel class of variational wave functions for correlated many-body quantum systems. Here, we propose a specific neural network ansatz suitable for systems with real-valued wave functions. Its characteristic is to encode the all-important rugged sign structure of a quantum wave function in a convolutional neural network with discrete output. Its training is achieved through an evolutionary algorithm. We test our variational ansatz and training strategy on two spin-1/2 Heisenberg models, one on the two-dimensional square lattice and one on the three-dimensional pyrochlore lattice. In the former, our ansatz converges with high accuracy to the analytically known sign structures of ordered phases. In the latter, where such sign structures are a priori unknown, we obtain better variational energies than with other neural network states. Our results demonstrate the utility of discrete neural networks to solve quantum many-body problems. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2643-1564 |
| Relation: |
https://doaj.org/toc/2643-1564 |
| DOI: |
10.1103/PhysRevResearch.4.L022026 |
| Access URL: |
https://doaj.org/article/c77e7cc9992541ba8e55531eff5588f2 |
| Accession Number: |
edsdoj.77e7cc9992541ba8e55531eff5588f2 |
| Database: |
Directory of Open Access Journals |
