jVMC: Versatile and performant variational Monte Carlo leveraging automated differentiation and GPU acceleration
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 st...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article (Journal) Chapter/Article |
| Language: | English |
| Published: |
14 Dec 2021
|
| In: |
Arxiv
Year: 2021, Pages: 1-33 |
| DOI: | 10.48550/arXiv.2108.03409 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.48550/arXiv.2108.03409 Verlag, lizenzpflichtig, Volltext: http://arxiv.org/abs/2108.03409 
|
| Author Notes: | Markus Schmitt and Moritz Reh |
| Summary: | 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. |
|---|---|
| Item Description: | Gesehen am 23.09.2022 |
| Physical Description: | Online Resource |
| DOI: | 10.48550/arXiv.2108.03409 |