Machine learning algorithms for many-body quantum systems

jQMC code implements two real-space ab initio quantum Monte Carlo (QMC) methods. Variatioinal Monte Carlo (VMC) and lattice regularized diffusion Monte Carlo (LRDMC) methods. jQMC achieves high-performance computations especially on GPUs.

A PyTorch implementation of a Neural Quantum State (NQS) simulator for quantum many-body systems, featuring symmetry-preserving neural networks and advanced sampling techniques.

Benchmarking Neural Quantum States (NQS) as classical samplers for Sample-based Quantum Diagonalization (SQD) in 12-bit molecular systems. Compares FFNN-based NQS with uniform random sampling on LiH, H4, H6 molecules using qiskit-addon-sqd.

This repository provide the codes that support the results in our paper: https://arxiv.org/abs/2506.11222

Code for the paper "Physics-informed Transformers for Electronic Quantum States" (2024).