jaqmc

JaQMC is a modular, JAX-based framework designed for performing neural network quantum Monte Carlo simulations. It utilizes deep neural networks as variational wavefunctions to solve the electronic Schrödinger equation without relying on traditional basis sets. The project supports various quantum systems, including molecules, solids, and fractional quantum Hall states, through a highly configurable and extensible architecture.

JaQMC is a JAX-based framework designed for neural network quantum Monte Carlo simulations, enabling the solution of the electronic Schrödinger equation using deep neural networks as variational wavefunctions. By leveraging JAX, the project provides automatic differentiation, JIT compilation, and multi-device parallelism to achieve high accuracy without the limitations of traditional basis sets. Its modular architecture allows researchers to independently swap wavefunctions, samplers, and optimizers, making it a flexible tool for modeling diverse quantum systems including molecules, solids, and fractional quantum Hall states.