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

@inproceedings{Schmitt2021jVMCVA, title={jVMC: Versatile and performant variational Monte Carlo leveraging automated differentiation and GPU acceleration}, author={Markus Schmitt and Moritz Reh}, year={2021} }

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…

## 2 Citations

NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems

- Computer ScienceArXiv
- 2021

The most significant new feature is the possibility to define arbitrary neural network ansätze in pure Python code using the concise notation of machine-learning frameworks, which allows for just-in-time compilation as well as the implicit generation of gradients thanks to automatic differentiation.

Time-Dependent Variational Principle for Open Quantum Systems with Artificial Neural Networks.

- PhysicsPhysical review letters
- 2021

A variational approach to simulating the dynamics of open quantum many-body systems using deep autoregressive neural networks by employing a time-dependent variational principle.

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