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

Date

Jun 19, 2025

Time

1:00 PM - 3:00 PM

Speaker

Werner Dobrautz

Affiliation

DRESDEN-concept Research Group Leader, TU Dresden

Series

TUD nanoSeminar

Language

en

Main Topic

Physik

Other Topics

Physik

Host

Arezoo Dianat

Description

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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Last modified: Apr 26, 2025, 7:37:31 AM