Erlangen 2018 – wissenschaftliches Programm
Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe
Q: Fachverband Quantenoptik und Photonik
Q 62: Poster: Quantum Optics and Photonics V
Q 62.72: Poster
Donnerstag, 8. März 2018, 16:15–18:15, Redoutensaal
Machine Learning to tackle the Entanglement Separability Problem in the Bloch Space — •Klaus Kades1, Benjamin Claßen1, Matthias Weidemüller1, 2, and Zhen-Sheng Yuan3 — 1Physikalisches Institut, Universität Heidelberg, Im Neuenheimer Feld 226, 69120 Heidelberg, Germany — 2Shanghai Branch, University of Science and Technology of China, 201315 Shanghai, China — 3Department of Modern Physics, University of Science and Technology of China, 230026 Hefei, China
Determining entanglement and separability is still an open problem for multipartite quantum systems. By transforming the density matrix into the Bloch space, we have a new different access to physical quantities. Lu et al. have shown that entangled and separable states occupy certain regions in this new space (arXiv: 1705.01523). Through analyzing the state space in great depth and by developing a new way to generate random density matrices we can now apply Machine Learning algorithms to identify entangled and separable states.