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
- doi: 10.21468/SciPostPhysCodeb.2
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| DOI | Type | |
|---|---|---|
| 10.21468/SciPostPhysCodeb.2 | Article | |
| 10.21468/SciPostPhysCodeb.2-r0.1 | Codebase release |
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.
Cited by 9
Authors / Affiliations: mappings to Contributors and Organizations
See all Organizations.- 1 Markus Schmitt,
- 2 Moritz Reh
- 1 Universität zu Köln / University of Cologne [UoC]
- 2 Ruprecht-Karls-Universität Heidelberg / Heidelberg University