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: firstname.lastname@example.org.
Get to know the rest of our team here and learn more about research here.
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 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 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