Bridging Classical, Quantum and Machine Learning Approaches: New Avenues to Studying Complex Quantum Materials

Datum

19.06.2025

Zeit

13:00 - 15:00

Sprecher

Werner Dobrautz

Zugehörigkeit

DRESDEN-concept Research Group Leader, TU Dresden

Serie

TUD nanoSeminar

Sprache

en

Hauptthema

Physik

Andere Themen

Physik

Host

Arezoo Dianat

Beschreibung

Understanding and predicting the behavior of complex quantum systems is a fundamental challenge in physics, chemistry, and materials science. Accurately modeling strongly correlated materials, catalysts, and superconductors requires solving computationally intractable quantum many-body problems, where classical methods face exponential scaling. In this talk, I will present how High-Performance Computing (HPC), Artificial Intelligence (AI)/Machine Learning (ML), and Quantum Algorithms might revolutionize computational approaches to quantum matter. HPC enables large-scale quantum simulations, AI/ML can accelarate optimization and yield data-driven insights, while quantum computing offers novel paradigms to potentially tackle problems beyond classical feasibility. I will discuss applications ranging from transition metal clusters releven to molecular catalysts and model systems for superconductors and quantum materials, highlighting how these computational techniques complement experimental and theoretical research.

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Letztmalig verändert: 26.04.2025, 07:37:31