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Talk32 min

Quantum Machine Learning with Physics-Informed and Symmetry-Aware Models

Oleksandr Kyriienko · CERN

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About

In the talk I will describe recent advances in building quantum models that can learn on tasks with symmetries and laws of physics in the form of differential equations. First, I will introduce approaches based on differential constraints and discuss their applications in generative modelling. Second, I will discuss the concept of quantum data and present an analysis for embeddings motivated by physical processes. Finally, I will show that embedding symmetries into quantum machine learning models can help discovering protocols able to solve Simon's and forrelation problems with excellent generalization..

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The talk

  1. Quantum Machine Learning with Physics-Informed and Symmetry-Aware Models 2025-01-21 · 32 min · CERN's player

Credits and licence

Institution
CERN
Department
QTI other events or meetings
Speaker
Oleksandr Kyriienko
Year
2025
Language
English
Subjects
Physics, Earth and environment
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.
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