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.
Werner Dobrautz studied Technical Physics at Graz University of Technology, specializing in computational solid-state physics. In 2019, he obtained his PhD in Computational Quantum Chemistry from the Max Planck Institute for Solid State Research and the University of Stuttgart, where he developed innovative quantum Monte Carlo methods in a high-performance computing (HPC) setting to tackle strongly correlated electron problems.
From 2022 to 2024, he was a Marie Skłodowska-Curie Postdoctoral Fellow at Chalmers University of Technology in Gothenburg, Sweden, conducting research at the Wallenberg Centre for Quantum Technologies (WACQT). His work focused on advancing quantum computing algorithms for realistic electronic structure calculations on current and near-term quantum computing (QC) devices.
Since December 2024, he has been a DRESDEN-concept research group leader, jointly appointed at the Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) in Dresden and the Center for Advanced Systems Understanding (CASUS) in Görlitz. He recently was awarded a BMBF grant through the Quantum Futur Junior Research Group Leader program to establish the AI 4 Quantum research group. His group develops a synergistic HPC+QC approach enhanced by artificial intelligence and deep machine learning methods to study complex quantum systems relevant to the green energy transition.
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.
Werner Dobrautz studied Technical Physics at Graz University of Technology, specializing in computational solid-state physics. In 2019, he obtained his PhD in Computational Quantum Chemistry from the Max Planck Institute for Solid State Research and the University of Stuttgart, where he developed innovative quantum Monte Carlo methods in a high-performance computing (HPC) setting to tackle strongly correlated electron problems.
From 2022 to 2024, he was a Marie Skłodowska-Curie Postdoctoral Fellow at Chalmers University of Technology in Gothenburg, Sweden, conducting research at the Wallenberg Centre for Quantum Technologies (WACQT). His work focused on advancing quantum computing algorithms for realistic electronic structure calculations on current and near-term quantum computing (QC) devices.
Since December 2024, he has been a DRESDEN-concept research group leader, jointly appointed at the Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) in Dresden and the Center for Advanced Systems Understanding (CASUS) in Görlitz. He recently was awarded a BMBF grant through the Quantum Futur Junior Research Group Leader program to establish the AI 4 Quantum research group. His group develops a synergistic HPC+QC approach enhanced by artificial intelligence and deep machine learning methods to study complex quantum systems relevant to the green energy transition.
