Jonas Stein, M.Sc.

Jonas Stein, M.Sc.

Lehrstuhl für Mobile und Verteilte Systeme

Ludwig-Maximilians-Universität München, Institut für Informatik

Oettingenstraße 67
80538 München

Raum G 008

Telefon: +49 89 / 2180-9163

Fax: +49 89 / 2180-9148

Mail: jonas.stein@ifi.lmu.de

Research Interests

Remarks concerning Bachelor’s / Master’s Theses

If you are interested in writing a thesis (BA/MA) on a topic within a subset of my research interests, feel free to contact me. While a list of currently announced theses can be found below, any further ideas are welcome as well. When contacting me, please use your @campus.lmu.de e-mail address and enclose a current transcript of records.

Currently Announced Bachelor’s Theses

  • None

Currently Announced Master’s Theses

  • None

Assigned Theses

  • Gerhard Stenzel, Michael Kölle, Jonas Stein, Andreas Sedlmeier, Claudia Linnhoff-Popien, „Quantum-Enhanced Denoising Diffusion Model“, Master’s Thesis
  • Viktoryia Patapovich, Jonas Stein, Michael Kölle, Maximilian Balthasar Mansky, Claudia Linnhoff-Popien, „Efficient quantum circuit architecture for parameterized coined quantum walks on many bipartite graphs“, Bachelor’s Thesis
  • Dominik Ott, Jonas Stein, Jonas Nüßlein, Claudia Linnhoff-Popien, „NISQ-ready community detection on weighted graphs using separation-node identification“, Bachelor’s Thesis
  • Max Adler, Jonas Stein, Jonas Nüßlein, Nico Kraus, Claudia Linnhoff-Popien, „Using Quantum Machine Learning to predict asset prices in financial markets“, Master’s Thesis
  • Ahmad Issa, Jonas Stein, Daniëlle Schuman, Claudia Linnhoff-Popien, „Anomaly Detection using Quantum Circuit Born Machines“, Bachelor’s Thesis
  • Lorena Wemmer, Jonas Stein, Michael Kölle, Elena van der Forst, Claudia Linnhoff-Popien, „Analyzing Reinforcement Learning strategies from a prameterized quantum walker“, Bachelor’s Thesis
  • Vasily Bokov, Jonas Stein, Michael Wolf, „Exploring the suitability of efficient Quantum Kernels for exponential speedups“, Master’s Thesis
  • Constantin Economides, Jonas Stein, Lode Pollet, „Quantum Average Gradient Descent“, Master’s Thesis
  • Ivo Christ, Jonas Stein, Robert Müller, Claudia Linnhoff-Popien, „Investor sentiment analysis using classical and quantum algorithms“, Master’s Thesis
  • Anant Agnihotri, Jonas Stein, Philipp Altmann, Jeanette Lorenz, „Quantum Entropy based Community Detection“, Master’s Thesis
  • Navid Roshani, Jonas Stein, Lode Pollet, „Introducing sequential Hamiltonian assembly for training VQEs and its application in graph coloring“, Masters’s Thesis

Supervised Theses

  • Soren Little, Christoph Roch, Jonas Stein, Claudia Linnhoff-Popien, „Structural Analysis of Graph Based Optimization Problems and their QUBO Formulations“, Bachelor’s Thesis
  • David Münzer, Michael Kölle, Jonas Stein, Claudia Linnhoff-Popien, „Data Embedding for efficient Quantum Machine Learning“, Bachelor’s Thesis
  • Petros Stougiannidis, Jonas Stein, Sebastian Feld, Claudia Linnhoff-Popien, „Introducing a space-efficient polynomial rotation circuit for bypassing traditional Quantum Arithmetic in NISQ-applications of HHL and beyond“, Bachelor’s Thesis
  • Leopold Bodendörfer, Jonas Stein, Michael Kölle, Claudia Linnhoff-Popien, „Efficient Data Embedding for offline Handwriting Recognition using Quantum Support Vector Machines“, Bachelor’s Thesis
  • Johannes Kolb, Jonas Stein, Christoph Roch, Claudia Linnhoff-Popien, „Comparing the performance of PUBO and QUBO based QAOA for continuous optimization problems“, Bachelor’s Thesis
  • Tobias Rohe, Jonas Stein, Julian Hager, Claudia Linnhoff-Popien, „Introducing a Hardware-efficient Layer-VQE based Ansatz“, Bachelor’s Thesis
  • Franziska Wörle, Jonas Stein, Maximilian Mansky, Robert Müller, Claudia Linnhoff-Popien, „Possibilities and limitations of Quantum Machine Translation: Adaption of the DisCoCat-Model for Question Answering in the Chinese Language“, Bachelor’s Thesis
  • Jonathan Wulf, Jonas Stein, Michael Kölle, Claudia Linnhoff-Popien, „Efficient embedding in Quantum Support Vector Machines using a specialized NISQ approach“, Bachelor’s Thesis
  • Philip Hierhager, Sebastian Zielinski, Jonas Stein, Claudia Linnhoff-Popien, „Evaluation of quantum-classical hybrid algorithms for solving 3-SAT problems“, Bachelor’s Thesis
  • Ivelina Bozhinova, Jonas Stein, Sebastian Feld, Claudia Linnhoff-Popien, „Pruning the search space in QUBO-based Community Detection“, Master’s Thesis
  • Kai-Chun Lin, Jonas Stein, Christoph Roch, Claudia Linnhoff-Popien, „Community Detection in Multilayer Networks via Quantum Annealing“, Master’s Thesis
  • Maximilian Beer, Jonas Stein, Sebastian Feld, Claudia Linnhoff-Popien, „Local Community Detection via Quantum Walks in the Quantum Gate Model“, Bachelor’s Thesis
  • Moritz Finsterwalder, Christoph Roch, Jonas Stein, Claudia Linnhoff-Popien, „Optimizing Shift Plans Using Classical Methods“, Bachelor’s Thesis
  • Peter Lang, Jonas Stein, Sebastian Feld, Claudia Linnhoff-Popien, „Optimized Community Detection through edge deletion on quantum annealers“, Bachelor’s Thesis
  • Bob Godar, Christoph Roch, Jonas Stein, Claudia Linnhoff-Popien, „Optimizing a Quantum Key Distribution Network using Quantum Annealing“, Masters’s Thesis

Teaching

  • Quantum Computing Programming (Practical Course): S21, W21/22, S22, W22/23, S23
  • Trends in Mobile and Distributed Systems (Seminar): S21
  • Seminar on advanced topics in Mobile and Distributed Systems (Seminar): W21/22, S22, W22/23, W22/23, S23

Projects

Publications

  • J. Stein, D. Ott, M. Schönfeld and S. Feld, „NISQ-ready community detection based on separation-node identification“, arXiv.2212.14717 [qp], Jan. 2023. DOI: 10.48550/arXiv.2212.14717. arXiv: 2212.14717
  • P. Altmann, L. Sünkel, J. Stein, T. Müller, C. Roch and C. Linnhoff-Popien, “SEQUENT: Towards Traceable Quantum Machine Learning using Sequential Quantum Enhanced Training“. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence – Volume 3: ICAART, ISBN 978-989-758-623-1; ISSN 2184-433X, SciTePress, pages 744-751. DOI: 10.5220/0011772400003393. arXiv: 2301.02601.
  • M. Kölle, A. Giovagnoli, J. Stein, M. B. Mansky, J. Hager and C. Linnhoff-Popien, “Improving Convergence for Quantum Variational Classifiers using Weight Re-Mapping“. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence – Volume 2: ICAART, ISBN 978-989-758-623-1; ISSN 2184-433X, SciTePress, pages 251-258. DOI: 10.5220/0011696300003393. arXiv: 2212.14807.
  • J. Stein, J. Nüßlein, M. Kölle, C. Linnhoff-Popien, „Practical course: Quantum Computing Programming“, 4th Edition, LMU Munich, October 2022. pdf
  • B. Godar, C. Roch, J. Stein, M. Geitz, B. Lehmann, M. Gunkel, V. Fürst and F. Hofmann, “Optimization of QKD Networks with Classical and Quantum Annealing”, arXiv.2206.14109 [qp], Jun. 2022, DOI: 10.48550/ARXIV.2206.14109. arXiv: 2206.14109.
  • T. Gabor, M. Lachner, N. Kraus, J. Stein, C. Roch, D. Ratke and C. Linnhoff-Popien, “Modifying the Quantum-Assisted Genetic Algorithm”. The 2022 Genetic and Evolutionary Computation Conference. Boston, July 9-13, 2022. DOI: 10.1145/3520304.3534034.
  • J. Stein, „Quantencomputing-Der neueste Stand der Super-Technologie“, Digitale Welt 6, 3 (2022). DOI: 10.1007/s42354-022-0464-7
  • J. Stein, L. Borrmann, S. Feld, T. Gabor, C. Roch, L. Sünkel, S. Zielinski and C. Linnhoff-Popien, „Wie gelingt meinem Unternehmen der Einstieg ins Quantencomputing? Über das Ökosystem für die Quantencomputing-Anwenderkompetenz des QAR-Lab“, Digitale Welt 5, 18–23 (2021). DOI: 10.1007/s42354-021-0331-y