Quantum Machine Learning for Photometric Redshift Estimation

- Bachelorarbeit -

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Description:

The purpose of this thesis is to use quantum machine learning algorithms to estimate the redshift values (which determines the distance) of stellar objects. For this application, photometric data, which is relatively easy to obtain, from the SDSS dataset is used. The aim is to make expensive sprectral measurements obsolete in the future by getting more accurate estimations with the help of quantum machine learning methods.

During the process of this thesis, various quantum machine learning approaches will be implemented for redshift estimation as well as existing classical ones. Then these approaches will be compared to each other to validate quantum improvement and to find the best fitting method. As the final product, an extensive comparison between all the implemented methods will be produced to guide future research in the field.

Anforderungen/Kenntnisse:

Machine Learning, Quantum Computing

Bearbeitung:

F. L. Truetzschler

Betreuung:

Prof. Dr. rer. nat. habil. Sven Groppe
Institut für Informationssysteme
Ratzeburger Allee 160 ( Gebäude 64 - 2. OG)
23562 Lübeck
Telefon: 0451 / 3101 5706