TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhysCodeb.2-r0.1
TI  - Codebase release 0.1 for jVMC
PY  - 2022/08/23
UR  - https://www.scipost.org/SciPostPhysCodeb.2-r0.1
JF  - SciPost Physics Codebases
JA  - SciPost Phys. Codebases
SP  - 2-r0.1
A1  - Schmitt, Markus
AU  - Reh, Moritz
AB  - 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.
ER  -

@Article{10.21468/SciPostPhysCodeb.2,
	title={{jVMC: Versatile and performant variational Monte Carlo leveraging automated differentiation and GPU acceleration}},
	author={Markus Schmitt and Moritz Reh},
	journal={SciPost Phys. Codebases},
	pages={2},
	year={2022},
	publisher={SciPost},
	doi={10.21468/SciPostPhysCodeb.2},
	url={https://scipost.org/10.21468/SciPostPhysCodeb.2},
}

@Article{10.21468/SciPostPhysCodeb.2-r0.1,
	title={{Codebase release 0.1 for jVMC}},
	author={Markus Schmitt and Moritz Reh},
	journal={SciPost Phys. Codebases},
	pages={2-r0.1},
	year={2022},
	publisher={SciPost},
	doi={10.21468/SciPostPhysCodeb.2-r0.1},
	url={https://scipost.org/10.21468/SciPostPhysCodeb.2-r0.1},
}