jVMC 0.1.3

jVMC: Versatile and performant variational Monte Carlo

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

1. We recommend you create a new conda environment to work with jVMC:

    conda create -n jvmc python=3.8
    conda activate jvmc

2. pip-install the package

    pip install jVMC

Option 2: Clone and pip-install

1. Clone the jVMC repository and check out the development branch:

    git clone https://github.com/markusschmitt/vmc_jax.git
    cd vmc_jax

2. We recommend you create a new conda environment to work with jVMC:

    conda create -n jvmc python=3.8
    conda activate jvmc

3. 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.