About
Abstract
This talk introduces the fundamental concepts of quantum machine learning (QML). In the realm of parametrised quantum circuits, embedding of classical data and parameter optimization methods as part of the general data processing pipeline for quantum networks are being discussed. Furthermore, possible advantages and challenges in the QML domain are considered and the presentation concludes with examples of CERN specific use-cases.
Bio
Carla is a theoretical physicist specializing in quantum computing and quantum algorithms. With a master's degree from ETH Zurich, Carla is currently pursuing a Ph.D. at CERN with TUM, focusing on quantum algorithms for combinatorial problems and efficient classical simulability of quantum circuits.
0:00:00 Slide 1
0:01:38 Slide 2
0:03:40 Slide 3
0:05:16 Slide 4
0:10:53 Slide 5
0:12:23 Slide 6
0:13:54 Slide 7
0:16:54 Slide 8
The talk
Credits and licence
- Institution
- CERN
- Department
- CERN openlab summer student lecture programme
- Speaker
- Carla Sophie Rieger
- Year
- 2024
- Language
- English
- Subjects
- Physics, Computer science
- Source
- CERN
- Licence
- CERN, as CERN states it. CERN's terms
- Attribution
- © CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
- Who can watch
- CERN gives no rating. Invisible Network shows it to Tweens profiles and older, judged from what the source says about it. How courses are placed