Eliška Greplová

Research

Papers

Talks & Teaching

About

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.

News

  • 05/2022 Jin's second PhD paper out on arXiv! We show how to engineer superconducting circuits using symmetries that protect enntanglement in a topological way,

  • 05/2022 Arash's first PhD paper out on arXiv! We show exciting connection between specific correlators in quantum circuits and quantum resources of the resulting state.

  • 04/2022 Book of lecture notes from Warsaw Machine Learning School is now on arXiv

  • 03/2022 Our new optimizer for recontructing Hamiltonians of bilayer graphene quantum dots is now on arXiv!

  • 02/2022 Our Hamiltonian learning for large scale quantum systems (and Jin's first PhD paper) now published in Physical Review A

  • 02/2022 Our interpretable neural network quantum states now published in Physical Review Research

  • 12/2021 Eliska receives NWO grant VENI

  • 12/2021 QMAI is joining Dutch Quantum Cloud computer Quantum Inspire

  • 10/2021 new PhD student Vinicius Hernandes!

  • 09/2021 Eliska's Quantum Research Seminar at the University of Toronto is online

  • 09/2021 Eliska's lectures from Summer School: Machine Learning in Quantum Physics and Chemistry are now online

  • 08/2021 Eliska is guest-editing MDPI Electronics on Machine Learning and AI in Quantum Computing Systems. Submit your papers now

  • 07/2021 We received a Kavli Synergy Project funding on Quantum Physics Exploration of Neuronal Activity in collaboration with BioNanoscience department at the Kavli Institute in Delft.

  • 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!

  • 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 "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