jVMC: Versatile and performant variational Monte Carlo leveraging automated differentiation and GPU acceleration
Computational Physics
2023-02-08 v2 Disordered Systems and Neural Networks
Strongly Correlated Electrons
Abstract
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.
Cite
@article{arxiv.2108.03409,
title = {jVMC: Versatile and performant variational Monte Carlo leveraging automated differentiation and GPU acceleration},
author = {Markus Schmitt and Moritz Reh},
journal= {arXiv preprint arXiv:2108.03409},
year = {2023}
}
Comments
33 pages, 7 figures. Revised version. Code repository: https://github.com/markusschmitt/vmc_jax