The Potential of Quantum Machine Learning in Astrophysics and Cosmology

Giorno 1° marzo 2024, con inizio alle ore 15:00, presso l'Aula T del DFA, la Dr Farida Farsian (OACT-INAF) terrà un seminario dal titolo The Potential of Quantum Machine Learning in Astrophysics and Cosmology.

L'evento è proposto dal Prof. G. Falci nel quadro della ricerca del National Quantum Science and Technology Institute (NQSTI).

Tutte le persone interessate sono invitate a partecipare.

Abstract. Astrophysics grapples with the immense data generated by cosmological surveys, necessitating innovative solutions like Artificial Intelligence (AI) and Machine Learning (ML). These methods manage computational complexity, automate tasks, and unveil novel data features. This presentation outlines my experiences, emphasizing ML's pivotal role in specific fields. It then explores the future application of Quantum ML, discussing its potential and possible contributions to astrophysics.

Bio. Dr Farsian began her academic journey with a Ph.D. in astrophysics and cosmology, focusing on the application of machine learning in these fields. Specializing in deep learning methods, including deep neural networks (NNs), convolutional NNs, and graph NNs, she has applied these techniques to various areas, including Cosmic Microwave Background, Large Scale Structure, and Gamma Ray Burst data. Currently, Dr Farsian is exploring the potential of quantum machine learning in astrophysical and cosmological data.