• jVMC: Versatile and performant...

Treffer: jVMC: Versatile and performant variational Monte Carlo leveraging automated differentiation and GPU acceleration

Title:
jVMC: Versatile and performant variational Monte Carlo leveraging automated differentiation and GPU acceleration
Authors:
Schmitt, Markus, Reh, Moritz
Source:
SciPost Phys. Codebases 2 (2022)
Publication Year:
2021
Collection:
Condensed Matter
Physics (Other)
Subject Terms:
Physics - Computational Physics, Condensed Matter - Disordered Systems and Neural Networks, Condensed Matter - Strongly Correlated Electrons
Document Type:
Report Working Paper
DOI:
10.21468/SciPostPhysCodeb.2
Access URL:
http://arxiv.org/abs/2108.03409
Accession Number:
edsarx.2108.03409
Database:
arXiv

Weitere Informationen

The introduction of Neural Quantum States (NQS) has recently given a new twist to variational Monte Carlo (VMC). The ability to systematically reduce the bias of the wave function ansatz renders the approach widely applicable. However, performant implementations are crucial to reach the numerical state of the art. Here, we present a Python codebase that supports arbitrary NQS architectures and model Hamiltonians. Additionally leveraging automatic differentiation, just-in-time compilation to accelerators, and distributed computing, it is designed to facilitate the composition of efficient NQS algorithms.
Comment: 33 pages, 7 figures. Revised version. Code repository: https://github.com/markusschmitt/vmc_jax