// summary
JaQMC is a modular framework built on JAX designed for performing neural network quantum Monte Carlo simulations. It utilizes deep neural networks as variational wavefunctions to solve the electronic Schrödinger equation with high accuracy. The platform supports diverse quantum systems including molecules, solids, and fractional quantum Hall states through swappable, independent components.
// technical analysis
JaQMC is a JAX-based framework designed for neural network quantum Monte Carlo simulations, enabling the solution of the electronic Schrödinger equation without reliance on traditional basis sets or density functionals. Its architecture is strictly modular, decoupling wavefunctions, samplers, estimators, and optimizers to allow for flexible experimentation with different physical models and loss functions. By leveraging JAX, the project provides high-performance features like automatic differentiation, JIT compilation, and multi-device parallelism, making it a robust tool for modeling complex quantum systems like molecules, solids, and fractional quantum Hall states.
// key highlights
// use cases
// getting started
To begin, clone the repository and install the dependencies using uv or pip within a Python 3.12 environment. Once installed, you can execute simulations directly from the command line, such as running a hydrogen atom training task with 'jaqmc hydrogen-atom train'. Users can further customize these runs by modifying parameters via the CLI or by exploring the provided documentation for molecular and solid-state workflows.