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...
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| Hauptverfasser: | , |
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| Dokumenttyp: | Article (Journal) Kapitel/Artikel |
| Sprache: | Englisch |
| Veröffentlicht: |
14 Dec 2021
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| In: |
Arxiv
Year: 2021, Pages: 1-33 |
| DOI: | 10.48550/arXiv.2108.03409 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.48550/arXiv.2108.03409 Verlag, lizenzpflichtig, Volltext: http://arxiv.org/abs/2108.03409 
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| Verfasserangaben: | Markus Schmitt and Moritz Reh |
| Zusammenfassung: | 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. |
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| Beschreibung: | Gesehen am 23.09.2022 |
| Beschreibung: | Online Resource |
| DOI: | 10.48550/arXiv.2108.03409 |