Machine learning algorithms for many-body quantum systems
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Updated
May 31, 2024 - Python
Machine learning algorithms for many-body quantum systems
Example class structure for use in FYS4411: Quantum mechanical systems at UiO.
Code for 'Solving Statistical Mechanics using Variational Autoregressive Networks'.
A framework based on Tensorflow for running variational Monte-Carlo simulations of quantum many-body systems.
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.
Variational Quantum Monte Carlo for a molecule, using Fokker-Planck/Langevin approach
Infinite order automatic differentiation for Monte Carlo with unnormalized probability distribution
Quantum Variational Monte Carlo with Neural Networks - Project repository for my master's thesis in computational physics at the University of Oslo
Third year mathematics dissertation on variational, laplace and mcmc approximations of bayesian logistic regression
Neural network ansatz to approximate a ground state by using variational Monte Carlo (VMC)
Supporting code for "Systematic improvement of neural network quantum states using Lanczos (NeurIPS 2022)""
📝 Code for the paper "Many-body quantum sign structures as non-glassy Ising models"
Dynamical Variational Monte Carlo (dVMC) method implemented and published in arxiv:1912.09960
This repository is intended to be a showcase and will contain code that I've been writing over the years on a bunch of different topics such as Variational Monte Carlo , genetic algorithms, machine learning, quantum chemistry simulations, ...
Advanced Data Assimilation Algorithms and Methods
Neural Network Quantum State
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