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

- Institution: CERN
- Department: QTI other events or meetings
- Speaker: Oleksandr Kyriienko
- Year: 2025
- Language: English
- Subjects: Physics, Earth and environment
- Source: [CERN](https://videos.cern.ch/record/3022278)
- Licence: [CERN](http://copyright.web.cern.ch)
- Who can watch: Tweens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

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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## Lectures

1. [Quantum Machine Learning with Physics-Informed and Symmetry-Aware Models](https://invisiblenetwork.ai/learn/cern/3022278/1ddb8b4b3f6c), 32 min (CERN's player)