Doctoral Theses
Current Doctoral Theses
- Lennart Binkowski, Quantum algorithms for combinatorial optimisation (supervised by Tobias J. Osborne)
- Andreea-Iulia Lefterovici, Hybrid Benchmarking of Quantum Algorithms (supervised by Tobias J. Osborne, Antonio Rotundo)
- Ugne Liaubaite, Quantum Error Mitigation and Correction (supervised by Tobias J. Osborne)
- Debora Ramacciotti, Sparse State Encoding and Stabilizer-Based Metrology for Quantum Computing (supervised by Tobias J. Osborne)
- Shawn Skelton, Quantum algorithms through quantum signal processing (supervised by Tobias J. Osborne)
- Martin Steinbach, Quantum Multigrid Methods applied to Maxwell’s Equations (supervised by Tobias J. Osborne, Thomas Wick)
- Sören Wilkening, Quantum Amplitude Engineering (supervised by Tobias J. Osborne)
- Timo Ziegler, Quantum Conic Programming (supervised by Tobias J. Osborne, René Schwonnek)
Completed Doctoral Theses
- Viktoria-Sophie Schmiesing, Machine Learning in Quantum Mechanical and Optical Systems, 2025 (supervised by Tobias J. Osborne)
- Laura Charlotte Niermann, Holographic toy models for de Sitter spacetime, 2024 (supervised by Tobias J. Osborne)
- Kerstin Beer, Quantum Neural Networks, 2022 (supervised by Tobias J. Osborne)
Master's Theses
Current Topics
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Comparing Functional Linear Solvers with VTAA (contact: Shawn Skelton)
There are many proposals for solving a system of linear equations with a quantum computer. These quantum linear solvers (QLS) generally fall into two categories: functional and adiabatic algorithms. Adiabatic algorithms have the best asymptotic lower bounds, but functional algorithms can be made competitive by using a complex routine called variable time amplitude amplification (VTAA).
There is increasing interest in performing high-level resource analyses to compare quantum algorithms with similar assumptions and determine if one offers an advantage over another. This type of work requires a blend of complexity analysis and some applied computer science. While existing comparisons of functional QLS use this method, none have incorporated VTAA. . So, existing comparisons use results which are known to underperform in asymptotic limit.
This project will consist of learning about VTAA and functional QLS algorithms, and then adapting existing code to perform a high-level resource analysis of several variants of VTAA applied to functional linear solvers. A student would need to be comfortable working with and adapting existing Python code, as well as working through proofs.
- individual projects, contact: everyone
Current Master's Theses
- Robin Syring, Reinforcement Learning for Cavity Locking (supervised by Tobias J. Osborne, Viktoria-Sophie Schmiesing)
- Nils Zolitschka, Gaussian Dissipative Neural Networks (supervised by Tobias J. Osborne, Viktoria-Sophie Schmiesing)
Completed Master's Theses
- Nico Buß, Near-Term Quantum-Algorithmic Approaches to the Facility Location Problem, 2024 (supervised by Lennart Binkowski, Tobias J. Osborne, Thomas Wick)
- Martin Steinbach, Quantum Linear Systems Algorithms applied to Partial Differential Equations with Poisson’s Problem as an Example, 2024 (supervised by Tobias Osborne, Thomas Wick)
- Zi Chua, Learning Unitaries: Comparing Classical vs. Quantum Dissipative Neural Networks, 2024 (supervised by Tobias J. Osborne, Viktoria-Sophie Schmiesing)
- Marvin Schwiering, Quantum Optimization Algorithms for the Traveling Salesman Problem, 2024 (supervised by Lennart Binkowski, Tobias J. Osborne)
- Paul J. Christiansen, Enhancing Branch and Bound Techniques by Quantum Approximate Optimization, 2023 (supervised by Tobias J. Osborne, Sören Wilkening)
- Jannik Eggert, Solving the Traveling Salesman Problem with the Quantum Approximate Optimization Algorithm, 2023 (supervised by Tobias J. Osborne)
- Tim Heine, Free Probabilistic Subordination for Asymptotic Spectral Distributions of Functions in Random-Matrix-Variables, 2023 (supervised by Tobias J. Osborne, Reinhard F. Werner)
- Gereon Koßmann, A Quantum Algorithm with Group Theory, 2023 (supervised by Tobias J. Osborne, Benjamin Sambale, René Schwonnek)
- Lennart Binkowski, Constraint Model Analysis of the Quantum Alternating Operator Ansatz, 2022 (supervised by Michael Cuntz, Tobias J. Osborne, René Schwonnek, Timo Ziegler)
- Debora Ramacciotti, A Reinforcement Learning Framework for Continuous Quantum Measurements, 2022 (supervised by Chiara Macchiavello, Tobias J. Osborne, Viktoria-Sophie Schmiesing)
- Gabriel Müller, Generative Adversarial Learning with Quantum Neural Networks, 2021 (supervised by Kerstin Beer, Tobias J. Osborne)
- Christian Struckmann, Training Quantum Neural Networks with Graph-Structured Quantum Data, 2021 (supervised by Kerstin Beer, Tobias J. Osborne)
- Julius Köhler, Quantum Machine Learning of Graph-structured Quantum Data, 2020 (supervised by Kerstin Beer, Tobias J. Osborne)
- Kerstin Beer, Contextuality and Cohomology, 2017 (supervised by Tobias J. Osborne)
Bachelor's Theses
Current Topics
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Comparing Methods for Hamiltonian Simulation (contact: Shawn Skelton)
Hamiltonian simulation is one of the most fundamental tasks in quantum computing. Many methods have been proposed to perform it, including Trotter simulation and quantum eigenvalue transformation (QET). These two methods can't be compared analytically because they are based on very different assumptions about the information a quantum computer can access. However, small numerical simulations can be used to study the circumstances under which QET might begin to outperform Trotter.
Within this project, a student will learn the basics of quantum algorithms, noise models, and quantum error mitigation strategies. After completing the background reading, the student will adapt pre-existing code for a more realistic simulation and then study the performance of both methods using numeric simulations of the calculation. If excellent progress is made, an experiment might be run on a real quantum computer.
- individual projects, contact: everyone
Current Bachelor's Theses
- Vincent von Wolff, Utilising tensorised Pauli decomposition for error analysis of quantum channels (supervised by Lennart Binkowski, Lukas Hantzko, Tobias J. Osborne)
Completed Bachelor's Theses
- Moritz Marwede, Efficiently preparing supports of boolean functions in uniform superposition, 2025 (supervised by Lennart Binkowski, Tobias J. Osborne)
- Robert Karimov, Enhancements for the Quantum Tree Generator: Application to Multidimensional Knapsack Problems, 2024 (supervised by Lennart Binkowski, Andreea-Iulia Lefterovici, Tobias J. Osborne, Sören Wilkening)
- Neele Hinrichs, Algorithmic Design and Documentation with Qiskit: The Quantum Tree Generator Handbook, 2024 (supervised by Lennart Binkowski, Andreea-Iulia Lefterovici, Tobias J. Osborne, Sören Wilkening)
- Ole Grimsel, Grundlagen der Quantenmechanik und Qiskit für Quantencomputing, 2023 (supervised by Andreea-Iulia Lefterovici, Tobias J. Osborne)
- Maren Lankhorst, Statistische Analyse der konzeptionellen Durchführung einer Unterrichtsanalyse zur Vermittlung quantenmechanicher Grundlagen, 2023 (supervised by Andreea-Iulia Lefterovici, Tobias J. Osborne)
- Lara Niemann, Didaktische Analyse einer Unterrichtsstunde zur Vermittlung quantenmechanischer Grundlagen, 2023 (supervised by Andreea-Iulia Lefterovici, Tobias J. Osborne)
- Leonhard Richter, The Quantum Approximate Optimization Algorithm and the Time-Dependent Variational Principle, 2023 (supervised by Tobias J. Osborne)
- Vivian Sattler, Quantum Singular Value Transformation Methods for Quantum Phase Estimation, 2023 (supervised by Tobias J. Osborne, Shawn Skelton)
- Robin Syring, PAC Bounds for Quantum Neural Networks, 2023 (supervised by Tobias J. Osborne, Viktoria-Sophie Schmiesing)
- Marlene Funck, Quantum algorithms for optimization problems, 2022 (supervised by Tobias J. Osborne, Antonio Rotundo)
- Lara Lelakowski, Quantenalgorithmen für das Travelling Salesman Problem, 2022 (supervised by Tobias J. Osborne, Sören Wilkening)
- Jan-Henrik Niestroj, A Study of Quantum Optimization Algorithms for a Job Shop Scheduling Problem, 2022 (supervised by Tobias J. Osborne)
- Jan Rasmus Holst, QAOA for the Knapsack Problem, 2022 (supervised by Tobias J. Osborne)
- Nils Gerhard Renziehausen, Quantum Neural Networks with General Output, 2022 (supervised by Tobias J. Osborne, Viktoria-Sophie Schmiesing)
- Jannik Eggert, Using Quantum Neural Networks to Cool Down Thermal States, 2020 (supervised by Dmytro Bondarenko, Tobias J. Osborne)
- Jan Hendrik Pfau, Quantum Machine Learning and Classication, 2020 (supervised by Kerstin Beer, Tobias J. Osborne)
- Marvin Schwiering, Classical Simulation of Quantum Feed-forward Neural Networks, 2020 (supervised by Kerstin Beer, Tobias J. Osborne)
- Martin Steinbach, Source-device-independent Quantum Random Number Generation, 2020 (supervised by Tobias Osborne, René Schwonnek, Ramona Wolf)
- Kerstin Beer, Quantum Compiling - Zerlegung unitärer Quantenoperationen, 2015 (supervised by Friederike Dziemba, Tobias J. Osborne)
