SciPost logo

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

Markus Schmitt, Moritz Reh

SciPost Phys. Codebases 2 (2022) · published 23 August 2022

This Publication is part of a bundle

When citing, cite all relevant items (e.g. for a Codebase, cite both the article and the release you used).


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.

Cited by 1

Crossref Cited-by

Authors / Affiliations: mappings to Contributors and Organizations

See all Organizations.
Funders for the research work leading to this publication