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.

Open Research Topics

  • None

Assigned Research Topics

  • Mark Seebode, Jonas Stein and Claudia Linnhoff-Popien, „Unsupervised Learning with QBMs using Quantum Annealing“, Bachelor’s Thesis
  • Joel Furtak, Jonas Stein and Claudia Linnhoff-Popien, „Towards purely Quantum Transformers“, Master’s Thesis
  • Simon Hehnen, Michael Kölle, Jonas Stein and Claudia Linnhoff-Popien, „Determining Semantic Links in Product Data Using Quantum Restricted Boltzmann Machines“, Master’s Thesis

Supervised Research Topics

  • Alexander Feist, Michael Kölle, Jonas Stein and Claudia Linnhoff-Popien, „Evaluating Parameter-Based Training Performance of Neural Networks and Variational Quantum Circuits“, Bachelor’s Thesis
  • Carlotta von L’Estocq, Jonas Stein and Claudia Linnhoff-Popien, „Leveraging Preconditioning to speed up quantum simulation-based optimization“, Bachelor’s Thesis
  • Viktoria Patapovich, Jonas Stein and Claudia Linnhoff-Popien, „Space-Efficient Quantum Optimization for the Traveling Salesman Problem via Binary Encoding of Feasible Solutions“, Master’s Thesis
  • Federico Harjes Ruiloba, Tobias Rohe, Jonas Stein and Claudia Linnhoff-Popien, „Neural Encoded VQEs for Combinatorial Optimization Problems“, Bachelor’s Thesis
  • Jonathan Wulf, Jonas Stein, Maximilian Zorn and Claudia Linnhoff-Popien, „State Preparation on Quantum Hardware Using an Island Genetic Algorithm“, Master’s Thesis
  • Sabrina Egger, Jonas Stein and Claudia Linnhoff-Popien, „Applying parameterized Quantum Walks in Reinforcement Learning“, Master’s Thesis
  • Het Dave, Arnold Unterauer, Jonas Stein and Claudia Linnhoff-Popien, „Explainable Time Series Forecasting using exogenous variables – How weather affects the stock market“, Bachelor’s Thesis
  • David Fischer, Jonas Stein, Philipp Altmann, Dirk André Deckert  and Claudia Linnhoff-Popien, „A Path Towards Quantum Advantage for the Unit Commitment Problem“, Bachelor’s Thesis
  • Jonas Blenninger, Jonas Stein, Maximilian Zorn, Peter Eder and Claudia Linnhoff-Popien, „CUAOA: A Novel CUDA-Accelerated Simulation Framework for the Quantum Approximate Optimization Algorithm“, Master’s Thesis
  • Jonathan Wulf, Jonas Stein, David Bucher and Claudia Linnhoff-Popien, „Quantum Singular Value Transformation in Practice“, Research Internship
  • Christopher Rieß, Jonas Stein, Michael Kölle and Claudia Linnhoff-Popien, „Benchmarking Quantum Gaussian Processes“, Research Internship
  • Jakob Mayer, Jonas Nüßlein, Jonas Stein and Claudia Linnhoff-Popienm „Towards Efficient Arbitrage Detection: A Study on Quantum Algorithms“, Bachelor’s Thesis
  • Nico Kraus, Jonas Stein, Jonas Nüßlein and Claudia Linnhoff-Popien, „The Influence of Data Characteristics on the Efficacy of Forecasting Models“, Research Internship
  • Constantin Economides, Jonas Stein, Lode Pollet, „A Novel Quantum Circuit for Efficient Computation of Exponentially Large Weighted Sums in Variational Quantum Circuits“, Master’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
  • Gerhard Stenzel, Michael Kölle, Jonas Stein, Andreas Sedlmeier, Claudia Linnhoff-Popien, „Quantum-Enhanced Denoising Diffusion Model“, Master’s Thesis
  • Vasily Bokov, Jonas Stein, Claudia Linnhoff-Popien, Michael Wolf, „Exploring the suitability of efficient Quantum Kernels for exponential speedups“, 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
  • Dominik Ott, Jonas Stein, Jonas Nüßlein, Claudia Linnhoff-Popien, „NISQ-ready community detection on weighted graphs using separation-node identification“, Bachelor’s Thesis
  • Lorena Wemmer, Jonas Stein, Michael Kölle, Elena Van der Vorst, Claudia Linnhoff-Popien, „Analyzing Reinforcement Learning strategies from a prameterized quantum walker“, Bachelor’s Thesis
  • Anant Agnihotri, Jonas Stein, Jeanette Lorenz, „Introducing Linear Entropy Based Community Detection“, Master’s Thesis
  • Viktoryia Patapovich, Jonas Stein, Michael Kölle, Maximilian Balthasar Mansky, Claudia Linnhoff-Popien, „Efficient quantum circuit architecture for coined quantum walks on many bipartite graphs“, Bachelor’s Thesis
  • Ahmad Issa, Jonas Stein, Daniëlle Schuman, Claudia Linnhoff-Popien, „Anomaly Detection using Quantum Circuit Born Machines“, Bachelor’s Thesis
  • Ivo Christ, Jonas Stein, Robert Müller, Claudia Linnhoff-Popien, „Investor sentiment analysis using classical and quantum algorithms“, Master’s Thesis
  • 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

Projects

Publications

  • J. Nüßlein, L. Sünkel, J. Stein, T. Rohe, D. Schuman, S.Feld, C. O’Meara, G. Cortiana, and C. Linnhoff-Popien, “Reducing QUBO Density by Factoring out Semi-Symmetries”. arXiv preprint [qp], Dec. 2024. arXiv: 2412.17841.
  • J. Nüßlein, M. Zorn, F. Ritz, J. Stein, G. Stenzel, J. Schönberger, T. Gabor, and C. Linnhoff-Popien, “Optimizing Sensor Redundancy in Sequential Decision-Making Problems”. To appear in the Proceedings of ICAART 2025.
  • T. Rohe, B. Böhm, M. Kölle, J. Stein, R. Müller, C. Linnhoff-Popien, “Coconut Palm Tree Counting on Drone Images with Deep Object Detection and Synthetic Training Data”. To appear in the Proceedings of ICAART 2025. arXiv preprint [CS], Dec. 2024. arXiv: 2412.11949
  • J. Stein, L. Müller, L. Hölscher, G. Chnitidis, J. Jojo, A. Farea, M. S. Çelebi, D. Bucher, J. Wulf, D. Fischer, P. Altmann, C. Linnhoff-Popien, and S. Feld, “Exponential Quantum Speedup for Simulation-Based Optimization Applications”. arXiv preprint [qp], Sep. 2024. arXiv: 2305.08482.
  • M. Kölle, D. Seidl, M. Zorn, P. Altmann, J. Stein and T. Gabor, “Optimizing Variational Quantum Circuits Using Metaheuristic Strategies in Reinforcement Learning”. To appear in the Proceedings of the IEEE International Conference on Quantum Computing and Engineering in Sep. 2024. arXiv:2408.01187.
  • J. Stein, J. Blenninger, D. Bucher, J. P. Eder, E. Çetiner, M. Zorn and C. Linnhoff-Popien, “CUAOA: A Novel CUDA-Accelerated Simulation Framework for the QAOA”. To appear in the Proceedings of the IEEE International Conference on Quantum Computing and Engineering in Sep. 2024. arXiv:2407.13012.
  • N. Roshani, J. Stein, M. Zorn, M. Kölle, P. Altmann and C. Linnhoff-Popien, “Sequential Hamiltonian Assembly: Enhancing the training of combinatorial optimization problems on quantum computers”. arXiv preprint [qp], Aug. 2024. arXiv:2408.04751.
  • M. Zorn, J. Stein, P. Altmann, M. Kölle, C. Linnhoff-Popien and T. Gabor, “Cohesive Quantum Circuit Layer Construction with Reinforcement Learning”. To appear in the Proceedings of the IEEE International Conference on Quantum Computing and Engineering in Sep. 2024.
  • T. Rohe, D. Schuman, J. Nüßlein, L. Sünkel, J. Stein and C. Linnhoff-Popien, “The Questionable Influence of Entanglement in Quantum Optimisation Algorithms”. To appear in the Proceedings of the IEEE International Conference on Quantum Computing and Engineering in Sep. 2024. arXiv:2407.17204.
  • D. Bucher, N. Kraus, J. Blenninger, M. Lachner, J. Stein and C. Linnhoff-Popien, “Towards Robust Benchmarking of Quantum Optimization Algorithms”. To appear in the Proceedings of the IEEE International Conference on Quantum Computing and Engineering in Sep. 2024. arXiv: 2405.07624.
  • P. Altmann, A. Bärligea, J. Stein, M. Kölle, T. Gabor, T. Phan and C. Linnhoff-Popien, “Challenges for Reinforcement Learning in Quantum Computing”. To appear in the Proceedings of the IEEE International Conference on Quantum Computing and Engineering in Sep. 2024.  arXiv: 2312.11337.
  • T. Rohe, S. Grätz, M. Kölle, S. Zielinski, J. Stein and C. Linnhoff-Popien, “From Problem to Solution: A general Pipeline to Solve Optimisation Problems on Quantum Hardware”. To appear in the Proceedings of the Future of Information and Communication Conference in Apr. 2025. arXiv:2406.19876.
  • A. Unterauer, D. Bucher, M. Knoll, C. Economides, M. Lachner, T. Germain, M. Kessel, S. Hajdinovic and J. Stein, “Solving the Turbine Balancing Problem using Quantum Annealing”. In Proceedings of the Companion Conference on Genetic and Evolutionary Computation, pages 1972–1978, Jul. 2024. DOI: 10.1145/3638530.3664107. arXiv: 2405.06412.
  • P. Altmann, A. Bärligea, J. Stein, M. Kölle, T. Gabor, T. Phan and C. Linnhoff-Popien, “Quantum Circuit Design: A Reinforcement Learning Challenge”. In the Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems, pages 2123–2125, May 2024. DOI: 10.5555/3635637.3663081.
  • M. Kölle, A. Giovagnoli, J. Stein, M. B. Mansky, J. Hager, T. Rohe, R. Müller and C. Linnhoff-Popien, “Weight Re-Mapping for Variational Quantum Algorithms”. Agents and Artificial Intelligence (LNCS volume 14546, LNAI), pages 286-309, Mar. 2024. DOI: 10.1007/978-3-031-55326-4_14. arXiv: 2306.05776.
  • J. Stein, T. Rohe, F. Nappi, J. Hager, D. Bucher, M. Zorn, M. Kölle and C. Linnhoff-Popien, “Introducing Reduced-Width QNNs, an AI-inspired Ansatz Design Pattern”. In Proceedings of the International Conference on Agents and Artificial Intelligence – Volume 3, pages 1127–1134, Feb. 2024. DOI: 10.5220/0012449800003636. arXiv: 2306.05047.
  • J. Stein, N. Roshani, M. Zorn, P. Altmann, M. Kölle and C. Linnhoff-Popien, “Improving Parameter Training for VQEs by Sequential Hamiltonian Assembly”. In Proceedings of the International Conference on Agents and Artificial Intelligence – Volume 2, pages 99–109, Feb. 2024. DOI: 10.5220/0012312500003636. arXiv: 2312.05552.
  • J. Stein, M. Poppel, P. Adamczyk, R. Fabry, Z. Wu, M. Kölle, J. Nüßlein, D. Schuman, P. Altmann, T. Ehmer, V. Narasimhan and C. Linnhoff-Popien, “Benchmarking Quantum Surrogate Models on Scarce and Noisy Data”. In Proceedings of the International Conference on Agents and Artificial Intelligence – Volume 3, pages 352–359, Feb. 2024. DOI: 10.5220/0012348900003636. arXiv: 2306.05042.
  • J. Stein, D. Schuman, M. Benkard, T. Holger, W. Sajko, M. Kölle, J. Nüßlein, L. Sünkel, O. Salomon and C. Linnhoff-Popien, “Exploring Unsupervised Anomaly Detection with Quantum Boltzmann Machines in Fraud Detection”.  In Proceedings of the International Conference on Agents and Artificial Intelligence – Volume 2, pages 177-185, Feb. 2024. DOI: 10.5220/0012326100003636. arXiv: 2306.04998.
  • M. Kölle, T. Schubert, P. Altmann, J. Stein, M. Zorn and C. Linnhoff-Popien, “A Reinforcement Learning Environment for Directed Quantum Circuit Synthesis”. In Proceedings of the International Conference on Agents and Artificial Intelligence – Volume 1, pages 83-94, Feb. 2024. DOI: 10.5220/0012383200003636. arXiv: 2401.07054.
  • M. Kölle, G. Stenzel, J. Stein, S. Zielinski, B. Ommer and C. Linnhoff-Popien, “Quantum Denoising Diffusion Models”. In the Proceedings of the IEEE International Conference on Quantum Software (QSW), Shenzhen, China, 2024 pp. 88-98. DOI: 10.1109/QSW62656.2024.00023. arXiv: 2401.07049.
  • M. Kölle, J. Mauerer, P. Altmann, L. Sünkel, J. Stein and C. Linnhoff-Popien, “Disentangling Quantum and Classical Contributions in Hybrid Quantum Machine Learning Architectures”. In Proceedings of the International Conference on Agents and Artificial Intelligence – Volume 3, pages 649-656, Feb. 2024. DOI: 10.5220/0012381600003636. arXiv: 2311.05559.
  • M. Kölle, M. Hgog, F. Ritz, M. Zorn, J. Stein and C. Linnhoff-Popien, “Quantum Advantage Actor-Critic for Reinforcement Learning”. In Proceedings of the International Conference on Agents and Artificial Intelligence – Volume 1, pages 297-304, Feb. 2024. DOI: 10.5220/0012383900003636. arXiv: 2401.07043.
  • S. Zielinski, J. Nüßlein, J. Stein, T. Gabor, C. Linnhoff-Popien and S. Feld, “Pattern QUBOs: Algorithmic Construction of 3SAT-to-QUBO Transformations”. Electronics – Volume 12, no. 16: 3492. Aug. 2023. DOI: 10.3390/electronics12163492. arXiv: 2305.02659.
  • J. Stein, D. Ott, J. Nüßlein, D. Bucher, M. Schönfeld and S. Feld, “NISQ-ready community detection based on separation-node identification”. Mathematics – Volume 11, no. 15; 3323, Jul. 2023. DOI: 10.3390/math11153323. arXiv: 2212.14717.
  • J. Stein, I. Christ, N. Kraus, M. B. Mansky, R. Müller and C. Linnhoff-Popien, “Applying QNLP to sentiment analysis in finance”. In Proceedings of the IEEE International Conference on Quantum Computing and Engineering – Volume 2, pages 20-25, Sep. 2023. DOI: 10.1109/QCE57702.2023.10178. arXiv: 2307.11788.
  • D. J. Schuman, L. Sünkel, P. Altmann, J. Stein, C. Roch, T. Gabor and C. Linnhoff-Popien, “Towards Transfer Learning for Large-Scale Image Classification Using Annealing-based Quantum Boltzmann Machines”. In Proceedings of the IEEE International Conference on Quantum Computing and Engineering – Volume 2, pages 42-47, Sep. 2023. DOI: 10.1109/QCE57702.2023.10182. arXiv: 2311.15966.
  • P. Stougiannidis, J. Stein, D. Bucher, S. Zielinski, C. Linnhoff-Popien and S. Feld, “Approximative lookup-tables and arbitrary function rotations for facilitating NISQ-implementations of the HHL and beyond”. In Proceedings of the IEEE International Conference on Quantum Computing and Engineering – Volume 1, pages 151-160, Sep. 2023. DOI: 10.1109/QCE57702.2023.00025. arXiv: 2306.05024.
  • M. B. Mansky, F. Wörle, J. Stein, R. Müller and C. Linnhoff-Popien, “Adapting the DisCoCat framework for Question Answering to the Chinese Language”. In Proceedings of the IEEE International Conference on Quantum Computing and Engineering – Volume 1, pages 591-600, Sep. 2023. DOI: 10.1109/QCE57702.2023.00073.
  • J. Stein, F. Chamanian, M. Zorn, J. Nüßlein, S. Zielinski, M. Kölle and C. Linnhoff-Popien, “Evidence that PUBO outperforms QUBO when solving continuous optimization problems with the QAOA”. In Proceedings of the Companion Conference on Genetic and Evolutionary Computation, pages 2254–2262, Jul. 2023. DOI: 10.1145/3583133.3596358. arXiv: 2305.03390.
  • S. Zielinski, J. Nüßlein, J. Stein, T. Gabor, C. Linnhoff-Popien and S. Feld, “Influence of Different 3SAT-to-QUBO Transformations on the Solution Quality of Quantum Annealing: A Benchmark Study”. In Proceedings of the Companion Conference on Genetic and Evolutionary Computation, pages 2263–2271, Jul. 2023. DOI: 10.1145/3583133.3596330. arXiv: 2305.00720.
  • J. Nuesslein, C. Roch, T. Gabor, J. Stein, C. Linnhoff-Popien and S. Feld, “Black Box Optimization Using QUBO and the Cross Entropy Method”. In Proceedings of the International Conference on Computational Science – Volume 5, pages 48–55, Jun. 23. DOI: 10.1007/978-3-031-36030-5_4. arXiv: 2206.12510.
  • 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 International Conference on Agents and Artificial Intelligence – Volume 3, pages 744-751, Feb. 2023. 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, pages 251-258, Feb. 2023. DOI: 10.5220/0011696300003393. arXiv: 2212.14807.
  • T. Gabor, M. Lachner, N. Kraus, J. Stein, C. Roch, D. Ratke and C. Linnhoff-Popien, “Modifying the Quantum-Assisted Genetic Algorithm”. In Proceedings of the Companion Conference on Genetic and Evolutionary Computation, pages 2205–2213, Jul. 2022. DOI: 10.1145/3520304.3534034.
  • 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 preprint [qp], Jun. 2022. arXiv: 2206.14109.

Talks

Community

  • Springer Quantum Machine Intelligence: Reviewer
  • 2nd International Workshop on Quantum Machine Learning: From Research to Practice (QML@QCE’24): Member of the Program Commttee
  • IEEE International Conference on Quantum Computing and Engineering (QCE) 2024: Reviewer and Session Chair
  • ACM Transactions on Quantum Computing: Reviewer