Eliška Greplová



Talks & Teaching


Quantum Matter and AI

Our group at Kavli Institute of Nanoscience (TU Delft) works at the boundary of quantum technologies, artificial intelligence and condensed matter physics. We are always looking for motivated scientists to join our team - if you are interested get in touch via email: e.greplova@tudelft.nl.
Get to know the rest of our team
here and learn more about research here.

Right now we are specifically looking for new team members for Kavli Synergy Project at the boundary of quantum physics and bionanoscience.

Email your application to: e.greplova@tudelft.nl


  • 03/2021 Our work on interpretable neural networks as variational wave-functions is now on arXiv!

  • 03/2021 Our work on learning large scale Hamiltonians from experimental measurements is now on arXiv!

  • 02/2021 Machine Learning for Scientists Jupyterbook is now released!!! Find accompanying lecture note on arXiv.

  • 02/2021 new PhD student Arash Ahmadi!

  • 01/2021 Our book "Machine Learning Kompakt" written together with the group of Titus Neupert (University of Zurich) is now published! If your institution has a SpringerLink you can download it for free.

  • 01/2021 new PhD student Guliuxin Jin!

  • 11/2020 Imelda Romero joined QMAI as an intern. She is working on reinforcement learning for ultra-precise parameter estimation.

09/2020 News&Views article in Nature Machine Intelligence on reinforcement learning as a tool for solving hard optimization problems in condensed matter

  • 09/2020 Luca Rüegg joined QMAI as a visiting student! He will work on topological phases and quantum computing.

  • 09/2020 "Quantum Matter and AI" (QMAI) group is joining TU Delft, get in touch if you are interested in PhD or master project!

  • 07/2020 New article in Europhysics News about our experience with Virtual Science Forum and why you should join the effort!

  • 06/2020 Our machine learning method for search and identification of 2D-material samples is now published in Physical Review Applied

  • 05/2020 Our experimental work on tuning quantum dots using machine learning is now published in Physical Review Applied as Editors' Suggestion