Machine learning algorithms for many-body quantum systems
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Updated
Jun 6, 2024 - Python
Machine learning algorithms for many-body quantum systems
Code for 'From Tensor Network Quantum States to Tensorial Recurrent Neural Networks'.
Neural quantum states in Julia
DeepErwin is a python 3.8+ package that implements and optimizes JAX 2.x wave function models for numerical solutions to the multi-electron Schrödinger equation. DeepErwin supports weight-sharing when optimizing wave functions for multiple nuclear geometries and the usage of pre-trained neural network weights to accelerate optimization.
Code for 'Solving Statistical Mechanics using Variational Autoregressive Networks'.
Dynamical Variational Monte Carlo (dVMC) method implemented and published in arxiv:1912.09960
Variational Quantum Monte Carlo for a molecule, using Fokker-Planck/Langevin approach
Neural network ansatz to approximate a ground state by using variational Monte Carlo (VMC)
📝 Code for the paper "Many-body quantum sign structures as non-glassy Ising models"
Quantum Variational Monte Carlo with Neural Networks - Project repository for my master's thesis in computational physics at the University of Oslo
Example class structure for use in FYS4411: Quantum mechanical systems at UiO.
Infinite order automatic differentiation for Monte Carlo with unnormalized probability distribution
Variational Monte Carlo implemented for the 1D Heisenberg Model and the Haldane-Shastry Model using a Gutzwiller projected wave function as the initial ansatz. (Fortran90)
Performing variational quantum Monte Carlo (VMC) in Julia. For educational purposes.
Supporting code for "Systematic improvement of neural network quantum states using Lanczos (NeurIPS 2022)""
The aim of this project is to compute the Helium nucleus ground state under an harmonic oscilator potential, using variational Montecarlo model and diffusion Montecarlo model.
Neural Network Quantum State
Group work for Solid State physics course at Aalto University
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