jVMC: Versatile and performant variational Monte Carlo
Project description
jVMC
This is an impementation of Variational Monte Carlo (VMC) for quantum many-body dynamics using the JAX library (and Flax on top) to exploit the blessings of automatic differentiation for easy model composition and just-in-time compilation for execution on accelerators.
Documentation
Documentation is available here.
Installation
Option 1: pip
-install
- We recommend you create a new conda environment to work with jVMC:
conda create -n jvmc python=3.8
conda activate jvmc
pip
-install the package
pip install jVMC
Option 2: Clone and pip
-install
- Clone the jVMC repository and check out the development branch:
git clone https://github.com/markusschmitt/vmc_jax.git
cd vmc_jax
- We recommend you create a new conda environment to work with jVMC:
conda create -n jvmc python=3.8
conda activate jvmc
- Create a wheel and
pip
-install the package
python setup.py bdist_wheel
python -m pip install dist/*.whl
Test that everything worked, e.g. run 'python -c "import jVMC"' from a place different than vmc_jax
.
Option 3: Manually install dependencies
If you want to work on the jVMC code you might prefer to install dependencies and set up jVMC without pip
-install.
Compiling JAX
How to compile JAX on a supercomputing cluster
Online example
Click on the badge above to open a notebook that implements an exemplary ground state search in Google Colab.
Citing jVMC
If you use the jVMC package for your research, please cite our reference paper arXiv:2108.03409.
Project details
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