Artificial intelligence and machine learning for quantum technologies

M Krenn, J Landgraf, T Foesel, F Marquardt - Physical Review A, 2023 - APS
In recent years the dramatic progress in machine learning has begun to impact many areas
of science and technology significantly. In the present perspective article, we explore how …

Intelligent on-demand design of phononic metamaterials

Y **, L He, Z Wen, B Mortazavi, H Guo, D Torrent… - …, 2022 - degruyter.com
With the growing interest in the field of artificial materials, more advanced and sophisticated
functionalities are required from phononic crystals and acoustic metamaterials. This implies …

Modern applications of machine learning in quantum sciences

A Dawid, J Arnold, B Requena, A Gresch… - ar** of phase diagrams on a physical quantum computer
K Kottmann, F Metz, J Fraxanet, N Baldelli - Physical Review Research, 2021 - APS
One of the most promising applications of quantum computing is simulating quantum many-
body systems. However, there is still a need for methods to efficiently investigate these …

Replacing neural networks by optimal analytical predictors for the detection of phase transitions

J Arnold, F Schäfer - Physical Review X, 2022 - APS
Identifying phase transitions and classifying phases of matter is central to understanding the
properties and behavior of a broad range of material systems. In recent years, machine …

Unsupervised and supervised learning of interacting topological phases from single-particle correlation functions

S Tibaldi, G Magnifico, D Vodola, E Ercolessi - SciPost Physics, 2023 - scipost.org
The recent advances in machine learning algorithms have boosted the application of these
techniques to the field of condensed matter physics, in order eg to classify the phases of …