Development of Machine Learning Algorithms for studies of quantum mechanical many-body systems

The project deals with studies and development of advanced algorithms for interacting many-particle systems (either classical or quantum mechanical ones) in condensed matter physics and/or subatomic physics with an emphasis on Machine Learning, both supervised and unsupervised  approaches.  

Plots of one-body densities for two-dimensional quantum dots for 2, 6 and 12 electrons computed with Reduced Boltzmann Machines (RBM) and standard Variational Monte Carlo approaches for fermions.

Artificial neural networks have been shown to be powerful approximations for quantum many-body problems. In this project, the aim is to study and develop various deep learning methods based on restricted Boltzmann machines and  neural networks to study quantum mechanical many-particle systems.

Deep learning approaches have proven to be a flexible tool to approximate quantum many-body states in condensed matter and chemistry problems, even when non-perturbative interactions are prominent. The project has its main focus on  many-particle systems in condensed matter physics and nuclear physics, using suitable approaches for modelling the ground-state wave function of quantum mechanical many-body systems. The project relies on efficient stochastic sampling and optimization schemes, including various Monte Carlo sampling methods.

The project can also be extended to include quantum machine learning approaches based on quantum Boltzmann machines and other deep learning methods.


  • The applicant is expected to hold a Master’s degree or equivalent in Computational or Theoretical Physics with specialization in quantum mechanical many-body problems with a  strong background in Computational Physics/Science.
  • Candidates with a background in Machine Learning will be preferred.


Professor Morten Hjorth-Jensen

Researcher Simen Kvaal

Call 2: Project start autumn 2022

This project is in call 2, starting autumn 2022. 


Published Aug. 20, 2020 12:32 PM - Last modified Nov. 1, 2021 11:06 AM