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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, feel free to send me an e-mail with some information about your research interest, technical background and CV. Open student projects are listed on our group's website.

Publications

  • Isabel Nha Minh Le, Shuo Sun, and Christian B. Mendl: Riemannian quantum circuit optimization based on matrix product operators. arxiv:2501.08872.
  • Isabel Nha Minh Le, Oriel Kiss, Julian Schuhmacher, Ivano Tavernelli, and Francesco Tacchino: Symmetry-invariant quantum machine learning force fields. arXiv:2311.11362.
  • Isabel Nha Minh Le, Julian D. Teske, Tobias Hangleiter, Pascal Cerfontaine, and Hendrik Bluhm: Analytic Filter-Function Derivatives for Quantum Optimal Control. Phys. Rev. Applied 17, 024006 (2022). , arxiv:2103.09126.

See my Google Scholar for a complete list.

Talks and conferences

  • On Riemannian quantum circuit optimization for fermionic Hamiltonian simulation. Poster presentation. 2nd Workshop of Machine Learning for Quantum Technology 2024, Erlangen, Germany.
  • On Riemannian quantum circuit optimization for fermionic Hamiltonian simulation. Poster presentation. Waterloo-Munich Joint Workshop 2024, Waterloo, Canada.
  • Symmetry-invariant quantum machine learning force fields. Invited talk. QAISG QML Seminar Singapore 2024, Online.
  • Symmetry-invariant quantum machine learning force fields. Long talk. Quantum Techniques in Machine Learning 2023, CERN.

Teaching

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