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Technical University of Munich

Isabel Nha Minh Le, M.Sc.

Technische Universität München

TUM School of CIT
Department of Computer Science
Boltzmannstrasse 3
85748 Garching
Germany

Office: SAP Labs Munich (MUE03), B1.09
Mail: isabel.le(at)tum.de
Phone: +49 89 289 18962
ORCID: 0000-0001-6707-044X

 

About myself

Since 2023 I am a research associate and doctoral candidate in the Quantum Computing Group of Prof. Mendl. Before, I have obtained a B.Sc. and M.Sc. in Physics from RWTH Aachen University, where I have set a focus on Quantum Technologies. Have a look at my LinkedIn profile for my previous work experiences.

Currently, I am interested in topics of quantum algorithms for quantum chemistry, tensor network methods, and (quantum) machine learning.

If you are interested in working with me, please apply through our group's website.

Research

Publications

See my Google Scholar or arXiv for a complete list.

Talks and conferences
  • On Riemannian quantum circuit optimization based on tensor networks methods. Talk. Visit of CCQ at the Flatiron Institute in 2025, New York, USA.
  • Riemannian quantum circuit optimization based on matrix product operators. Contributed talk. ICFO-IMPRS Joint Workshop 2025, Garching, Germany; PhD Symposium - Quantum Alliance @ World of Quantum 2025, Munich, Germany; 2nd DPG Fall Meeting 2025, Göttingen, Germany.
  • Riemannian quantum circuit optimization based on matrix product operators. Poster presentation. CQT & IMPRS-MCQST Joint Workshop 2025, Singapore; MCQST conference 2025, Kufstein, Austria; QSim 2025, New York, USA.
  • Employing tensor networks and Riemannian quantum circuit optimization for fermionic Hamiltonian simulation. Contributed talk and poster presentation. APS Global Summit 2025, Anaheim, USA.
  • On Riemannian quantum circuit optimization for fermionic Hamiltonian simulation. Poster presentation. Waterloo-Munich Joint Workshop 2024, Waterloo, Canada; 2nd Workshop of Machine Learning for Quantum Technology 2024, Erlangen, Germany.
  • Symmetry-invariant quantum machine learning force fields. Invited talk. QAISG QML Seminar Singapore 2024, Online.
  • Symmetry-invariant quantum machine learning force fields. Contributed talk. Quantum Techniques in Machine Learning 2023, CERN.

Teaching

  • Tutorial: Introduction to Quantum Computing - WiSe 2023/2024, WiSe 2024/2025, WiSe 2025/2026
  • Tutorial: Advanced Concepts in Quantum Computing - SuSe 2025
  • Seminar: Advanced Topics of Quantum Computing - WiSe 2023/2024, SuSe 2024, WiSe 2024/2025, SuSe 2025, WiSe 2025/2026
  • Seminar: Science & Ethics - WiSe 2024/2025
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