The journal Quantum Machine Intelligence has published an article entitled Boosting the Performance of Quantum Annealers using Machine Learning, authored by researchers from the Department of Knowledge Technologies, the Department of Complex Matter, the Jožef Stefan International Postgraduate School, the Nanocentre and the Faculty of Mathematics and Physics of the University of Ljubljana. The paper is co-authored by researchers Jure Brence, prof. dr. Dragan Mihailović, prof. dr. Viktor V. Kabanov, prof. dr. Ljupčo Todorovski in prof. dr. Sašo Džeroski, under the supervision of Dr. Jaka Vodeb, have managed to minimise the noise in quantum annealers using machine learning methods and thus improve their performance. Quantum annealers are a completely new type of computers that exploit the quantum dynamics of microscopic quantum bits to efficiently solve optimisation problems. They have succeeded in removing the influence of noise to such an extent that they have improved the performance of quantum annealers by three orders of magnitude, opening the way to solving more complex problems.