About
This talk starts with an introduction to the fundamental concepts of quantum mechanics and quantum computing. We then gain a basic understanding of quantum algorithms by exploring Deutsch and Grover's algorithms. Building on this, we will explore the key concepts of quantum machine learning (QML). The embedding of classical data and parameter optimisation methods as part of the general data processing pipeline for quantum networks is discussed in the context of parametrised quantum circuits. The presentation concludes with a consideration of the possible advantages and challenges in the QML domain, and with examples of CERN-specific use cases.
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0:00:00 Slide 1
0:08:18 Slide 2
0:10:13 Slide 3
0:11:30 Slide 4
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0:28:54 Slide 10
The talk
Credits and licence
- Institution
- CERN
- Department
- CERN openlab summer student lecture programme
- Speaker
- Carla Sophie Rieger
- Year
- 2025
- 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