Theses

Current Doctoral Theses

• Lennart Binkowski, Quantum Algorithmic Design for Classical Problems (supervised by Tobias J. Osborne, René Schwonnek)
• Andreea-Iulia Lefterovici, Hybrid Benchmarking of Quantum Algorithms (supervised by Tobias J. Osborne, Antonio Rotundo)
• Viktoria-Sophie Schmiesing, Machine Learning in quantum mechanical and optical environments (supervised by von Tobias J. Osborne)
• Shawn Skelton, Quantum algorithms through quantum signal processing (supervised by Tobias J. Osborne)
• Timo Ziegler, Quantum Conic Programming (supervised by Tobias J. Osborne, René Schwonnek)

Completed Doctoral Theses

• Kerstin Beer, Quantum Neural Networks, 2022 (supervised by Tobias J. Osborne)

Current Topics

• Speed of convergence of the Quantum Alternating Operator Ansatz (see Lennart Binkowski et al 2024 New J. Phys. 26 073001), contact: Lennart Binkowski

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)
• 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)
• 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)
• Julias 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)

Current Topics

• Generalization and implementation of  the Tensorized Pauli Decomposition algorithm (see Lukas Hantzko et al 2024 Phys. Scr. 99 085128), contact: Lennart Binkowski
• Quantum Floats (see Seidel et al 2021 Efficient Floating Point Arithmetic for Quantum Computers), contact: Sören Wilkening

Current Bachelor's Theses

Completed Bachelor's Theses

• 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 Donfarenko, 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)
• Kerstin Beer, Quantum Compiling - Zerlegung unitärer Quantenoperationen, 2015 (supervised by Friederike Dziemba, Tobias J. Osborne)