- Bachelor-/Masterarbeit -

Beschreibung:

Quantum computing and machine learning are the two of the most popular research areas today. After the imaging of a supermassive black hole with Event Horizon Telescope in 2019, black holes also increased their popularity. This project gives the opportunity to do one of the first study on utilizing quantum machine learning for black hole mass estimation.

The goal of the project is to estimate the masses of supermassive black holes (SMBH). SMBHs are the largest type of black holes. A SMBH’s mass can vary from millions to billions times the mass of the sun. They are located in the center of galaxies. Their masses are estimated using some properties of the active core of the galaxy that they are located in.

The number of features for mass estimation is very few and the number of training data is limited to the observed SMBHs at the AGN dataset. These properties make quantum machine learning a good fit for the task. With variational quantum circuits, smaller number of parameters are required than a classical approach, which makes it easier to train.

The goal of this thesis is to use variational quantum circuits to create a quantum machine learning approach that can learn to estimate supermassive black hole masses. 

Anforderungen/Kenntnisse:
Machine Learning - Basic

Betreuung:

Prof. Dr. rer.nat. habil. Sven Groppe

Institut für Informationssysteme
Ratzeburger Allee 160 ( Gebäude 64 - 2. OG)
23562 Lübeck
Telefon: 0451 / 3101 5706

Umut Çalıkyılmaz

Institut für Informationssysteme
Ratzeburger Allee 160 ( Gebäude 64 - 2. OG)
23562 Lübeck
Telefon: 0451 / 3101 5724