Doktorarbeiten
Laufende Doktorarbeiten
- Lennart Binkowski, Quantum algorithms for combinatorial optimisation (betreut von Tobias J. Osborne)
- Andreea-Iulia Lefterovici, Hybrid Benchmarking of Quantum Algorithms (betreut von Tobias J. Osborne, Antonio Rotundo)
- Ugne Liaubaite, Quantum Error Mitigation and Correction (betreut von Tobias J. Osborne)
- Debora Ramacciotti, Sparse State Encoding and Stabilizer-Based Metrology for Quantum Computing (betreut von Tobias J. Osborne)
- Shawn Skelton, Quantum algorithms through quantum signal processing (betreut von Tobias J. Osborne)
- Martin Steinbach, Quantum Multigrid Methods applied to Maxwell’s Equations (betreut von Tobias J. Osborne, Thomas Wick)
- Sören Wilkening, Quantum Amplitude Engineering (betreut von Tobias J. Osborne)
- Timo Ziegler, Quantum Conic Programming (betreut von Tobias J. Osborne, René Schwonnek)
Abgeschlossene Doktorarbeiten
- Viktoria-Sophie Schmiesing, Machine Learning in Quantum Mechanical and Optical Systems, 2025 (betreut von Tobias J. Osborne)
- Laura Charlotte Niermann, Holographic toy models for de Sitter spacetime, 2024 (betreut von Tobias J. Osborne)
- Kerstin Beer, Quantum Neural Networks, 2022 (betreut von Tobias J. Osborne)
Masterarbeiten
Aktuelle Themenvorschläge
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Comparing Functional Linear Solvers with VTAA (Ansprechperson: 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.
- individuelle Projekte, Ansprechperson: alle
Laufende Masterarbeiten
- Robin Syring, Reinforcement Learning for Cavity Locking (betreut von Tobias J. Osborne, Viktoria-Sophie Schmiesing)
- Nils Zolitschka, Gaussian Dissipative Neural Networks (betreut von Tobias J. Osborne, Viktoria-Sophie Schmiesing)
Abgeschlossene Masterarbeiten
- Nico Buß, Near-Term Quantum-Algorithmic Approaches to the Facility Location Problem, 2024 (betreut von 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 (betreut von Tobias Osborne, Thomas Wick)
- Zi Chua, Learning Unitaries: Comparing Classical vs. Quantum Dissipative Neural Networks, 2024 (betreut von Tobias J. Osborne, Viktoria-Sophie Schmiesing)
- Marvin Schwiering, Quantum Optimization Algorithms for the Traveling Salesman Problem, 2024 (betreut von Lennart Binkowski, Tobias J. Osborne)
- Paul J. Christiansen, Enhancing Branch and Bound Techniques by Quantum Approximate Optimization, 2023 (betreut Tobias J. Osborne, Sören Wilkening)
- Jannik Eggert, Solving the Traveling Salesman Problem with the Quantum Approximate Optimization Algorithm, 2023 (betreut von Tobias J. Osborne)
- Tim Heine, Free Probabilistic Subordination for Asymptotic Spectral Distributions of Functions in Random-Matrix-Variables, 2023 (betreut von Tobias J. Osborne, Reinhard F. Werner)
- Gereon Koßmann, A Quantum Algorithm with Group Theory, 2023 (betreut von Tobias J. Osborne, Benjamin Sambale, René Schwonnek)
- Lennart Binkowski, Constraint Model Analysis of the Quantum Alternating Operator Ansatz, 2022 (betreut von Michael Cuntz, Tobias J. Osborne, René Schwonnek, Timo Ziegler)
- Debora Ramacciotti, A Reinforcement Learning Framework for Continuous Quantum Measurements, 2022 (betreut von Chiara Macchiavello, Tobias J. Osborne, Viktoria-Sophie Schmiesing)
- Gabriel Müller, Generative Adversarial Learning with Quantum Neural Networks, 2021 (betreut von Kerstin Beer, Tobias J. Osborne)
- Christian Struckmann, Training Quantum Neural Networks with Graph-Structured Quantum Data, 2021 (betreut von Kerstin Beer, Tobias J. Osborne)
- Julius Köhler, Quantum Machine Learning of Graph-structured Quantum Data, 2020 (betreut von Kerstin Beer, Tobias J. Osborne)
- Kerstin Beer, Contextuality and Cohomology, 2017 (betreut von Tobias J. Osborne)
Bachelorarbeiten
Aktuelle Themenvorschläge
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Comparing Methods for Hamiltonian Simulation (Ansprechperson: 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.
- individuelle Projekte, Ansprechperson: alle
Laufende Bachelorarbeiten
- Vincent von Wolff, Utilising tensorised Pauli decomposition for error analysis of quantum channels (betreut von Lennart Binkowski, Lukas Hantzko, Tobias J. Osborne)
Abgeschlossene Bachelorarbeiten
- Moritz Marwede, Efficiently preparing supports of boolean functions in uniform superposition, 2025 (betreut von Lennart Binkowski, Tobias J. Osborne)
- Robert Karimov, Enhancements for the Quantum Tree Generator: Application to Multidimensional Knapsack Problems, 2024 (betreut von Lennart Binkowski, Andreea-Iulia Lefterovici, Tobias J. Osborne, Sören Wilkening)
- Neele Hinrichs, Algorithmic Design and Documentation with Qiskit: The Quantum Tree Generator Handbook, 2024 (betreut von Lennart Binkowski, Andreea-Iulia Lefterovici, Tobias J. Osborne, Sören Wilkening)
- Ole Grimsel, Grundlagen der Quantenmechanik und Qiskit für Quantencomputing, 2023 (betreut von Andreea-Iulia Lefterovici, Tobias J. Osborne)
- Maren Lankhorst, Statistische Analyse der konzeptionellen Durchführung einer Unterrichtsanalyse zur Vermittlung quantenmechanicher Grundlagen, 2023 (betreut von Andreea-Iulia Lefterovici, Tobias J. Osborne)
- Lara Niemann, Didaktische Analyse einer Unterrichtsstunde zur Vermittlung quantenmechanischer Grundlagen, 2023 (betreut von Andreea-Iulia Lefterovici, Tobias J. Osborne)
- Leonhard Richter, The Quantum Approximate Optimization Algorithm and the Time-Dependent Variational Principle, 2023 (betreut von Tobias J. Osborne)
- Vivian Sattler, Quantum Singular Value Transformation Methods for Quantum Phase Estimation, 2023 (betreut von Tobias J. Osborne, Shawn Skelton)
- Robin Syring, PAC Bounds for Quantum Neural Networks, 2023 (betreut von Tobias J. Osborne, Viktoria-Sophie Schmiesing)
- Marlene Funck, Quantum algorithms for optimization problems, 2022 (betreut von Tobias J. Osborne, Antonio Rotundo)
- Lara Lelakowski, Quantenalgorithmen für das Travelling Salesman Problem, 2022 (betreut von Tobias J. Osborne, Sören Wilkening)
- Jan-Henrik Niestroj, A Study of Quantum Optimization Algorithms for a Job Shop Scheduling Problem, 2022 (betreut von Tobias J. Osborne)
- Jan Rasmus Holst, QAOA for the Knapsack Problem, 2022 (betreut von Tobias J. Osborne)
- Nils Gerhard Renziehausen, Quantum Neural Networks with General Output, 2022 (betreut von Tobias J. Osborne, Viktoria-Sophie Schmiesing)
- Jannik Eggert, Using Quantum Neural Networks to Cool Down Thermal States, 2020 (betreut von Dmytro Bondarenko, Tobias J. Osborne)
- Jan Hendrik Pfau, Quantum Machine Learning and Classication, 2020 (betreut von Kerstin Beer, Tobias J. Osborne)
- Marvin Schwiering, Classical Simulation of Quantum Feed-forward Neural Networks, 2020 (betreut von Kerstin Beer, Tobias J. Osborne)
- Martin Steinbach, Source-device-independent Quantum Random Number Generation, 2020 (betreut von Tobias Osborne, René Schwonnek, Ramona Wolf)
- Kerstin Beer, Quantum Compiling - Zerlegung unitärer Quantenoperationen, 2015 (betreut von Friederike Dziemba, Tobias J. Osborne)