Mathematical reasoning in small language models: Comparing challenges and potentials of different prompting strategies
Author: Patrik FelbingerSupervision: Philipp Altmann, Thomas GaborCompleted on: January 14, 2026
Empirical Evaluation of Quantum Circuit Ansatz Design Effects for Supervised Learning
Author: Alexander KovacsSupervision: Michael Poppel, Jonas SteinCompleted on: January 14, 2026
Ingredient Recognition and Relative Quantity Estimation in Food Images on Hardware-Constrained Systems
Author: Michael AnderleSupervision: Gerhard Stenzel, Michael KölleCompleted on: December 3, 2025
A Comparison of the Ant Colony Algorithm and Dijkstra’s Algorithm for Routing in Quantum Networks
Author: Felix MichlSupervision: Leo Sünkel, Tobias RoheCompleted on: December 3, 2025
Generalization in a Natural-Language-Based Genetic Algorithm
Author: Sarah GernerSupervision: Thomas Gabor, Gerhard StenzelCompleted on: November 12, 2025
A Comparison of Echo State Networks and Spiking Neural Networks for Reservoir Computing
Author: Flo HelmbergerSupervision: Philipp Altmann, Thomas GaborCompleted on: November 12, 2025
Improving Cooperative MARL through Curriculum Learning for Dynamic Role Assignment
Author: David EngelSupervision: Maximilian Zorn, Philipp AltmannCompleted on: November 12, 2025
Exploring the Practical Application of Quantum-Native Self-Attention for Quantum Vision Transformers
Author: Joel FurtakSupervision: Jonas Stein, Michael KölleCompleted on: November 12, 2025
Vision Transformers (ViTs) have emerged as a powerful alternative to convolutional neural networks in computer vision, demonstrating superior performance across numerous tasks due to their ability to capture global relationships through the self-attention mechanism. Unlike previously established methods that focus primarily on local features, ViTs process images by analysing relationships between patches, allowing for flexible architectures that excel at modelling global dependencies. Despite their success, the self-attention mechanism in ViTs faces computational challenges when deployed in resource-constrained environments or real-time applications where efficiency is paramount. This limitation has motivated the exploration of different approaches using quantum computing, thatpreserve the global modelling capabilities of ViTs while reducing computational overhead within the self-attention mechanism. While some approaches aim to replicate the self-attention mechanism using trainable compound matrices, this thesis proposes a Quantum Vision Transformer (QViT) architecture native to the quantum paradigm, that leverages the Quantum Singular Value Transformation (QSVT) to approximate the self-attention mechanism. The proposed model integrates parameterized quantum circuits (PQCs) to encode patch-wise image embeddings, employs a Linear Combination of Unitaries (LCU) to mix patch representations in an attention-like manner, and applies the QSVT to introduce non-linear expressivity. In addition, a quantum classification circuit extends the data register with trainable class qubits, which serve as a quantum analogue to the classical class token and are measured to obtain the final outputs. To assess the models’ capability in image classification tasks, it is evaluated on Bars-and-Stripes and binary MNIST datasets, where it achieves up to∼99% accuracy. An analysis of the models’ computational complexity shows improved theoretical scaling with input size, as well as lower parameter counts. The results obtained in this thesis serve as proof-of-concept for the proposed QViT model and its application to computer vision tasks.
Offline Quantum Reinforcement Learning using Metaheuristic Optimization Strategies
Author: Frederik BickelSupervision: Michael Kölle, Julian HagerCompleted on: October 8, 2025
This thesis investigates offline quantum reinforcement learning (QRL) with variational quantum circuits (VQCs) and metaheuristic optimization. O!ine reinforcement learning (RL) provides a realistic training paradigm in which agents learn entirely from fixed datasets instead of online interaction, making it particularly suited for reproducible studies and controlled comparisons. For the offline training, we created a dataset for the CartPole-v1 environment by combining random, medium, and expert policies, resulting in 525,000 transitions with diverse state–action coverage. On this dataset, we evaluated the effectiveness of four gradient-free metaheuristic optimizers: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Tabu Search (TS). We trained a DQN agent with a 4-qubit, 2-layer VQC. Their performance is compared to a gradient-based gradient descent (GD) baseline with Adam optimizer. Each optimizer undergoes per-factor hyperparameter tuning, followed by an optimizer comparison on a single dataset pass.
Geometric Quantum GANs: Topology-Driven Architectures for Graph Generation
Supervision: Tobias RoheCompleted on: October 1, 2025
The generative modeling of complexly structured data, such as graphs with embedded geometric constraints, continues to represent a fundamental challenge for classical machine learning approaches. In this study, we investigate the potential of Quantum Generative Adversarial Networks (QuGANs) to overcome these limitations by focusing on a representative benchmark task: the generation of complete graphs with four nodes (K4) that reflect plausible flight routes/topologies. These graphs must satisfy important geometric properties to be considered physically valid within Euclidean space, particularly the triangle inequality for all sub-triangles and the Ptolemaic inequality for each four-node pair. We present a rigorous comparative analysis between a classical Generative Adversarial Network (GAN) and several hybrid QuGAN variants, each using a different quantum generator architecture. These include a generic and a problem-oriented entangled approach that directly incorporates the structural priors of the target graphs into the design of the quantum circuit.
Evolutionary Optimization of Variational Quantum Circuits for High Accuracy with Minimal Parameterized Gates
Author: Tobias DaakeSupervision: Leo Sünkel, Maximilian ZornCompleted on: September 17, 2025
Variational Quantum Circuits (VQCs) represent one of the most promising approaches for leveraging the capabilities of near-term quantum computers. While they offer a high degree of flexibility and adaptability, VQCs face significant challenges, particularly with respect to trainability and scalability. To address these limitations, this work explores the use of evolutionary algorithms for the automatic generation of VQCs with a minimal number of parameterized gates. This approach enables a systematic investigation of the role such gates play in the optimization of VQCs for common benchmark problems.The proposed method is evaluated in the context of a classification task, with particular attention to classification accuracy and the number of parameterized gates required. Experiments were conducted on random circuits with 4 and 6 qubits with variable circuit depth. The results demonstrate that reducing the number of parameters improves the optimization process, leading to classifiers that exhibit greater robustness and enhanced accuracy.
Fine-Tuning LLMs in Teacher-Student-Settings: Improving Code-Performance using RL
Author: Wei YuSupervision: Gerhard Stenzel, Maximilian ZornCompleted on: September 17, 2025
Geometry-Aware Quantum GANs: Topology-Guided Architectures for Graph Generation
Author: Markus BaumannSupervision: Tobias RoheCompleted on: September 17, 2025
Masked Encoder with Projection Layer for Anomaly Detection
Author: Anna SimonSupervision: Robert Müller, Michael KölleCompleted on: September 17, 2025
Query-Efficient Reinforcement Learning from Preferences
Author: Johannes KindermannSupervision: Philipp Altmann, Jonas NüßleinCompleted on: September 17, 2025
Applicability of QAOA to Minimizing Teleportation Costs in Distributed Quantum Computing
Author: Tim HenningnerSupervision: Leo Sünkel, Jonas SteinCompleted on: August 6, 2025
Efficient Reinforcement-Learning Curriculum Generation via Quality-Diversity Methods
Author: Nicole KilianSupervision: Max Zorn, Philipp AltmannCompleted on: August 6, 2025
Enhancing Expressivity in Hybrid QuGANs for Structured Graph Generation
Author: Emily BurtonSupervision: Tobias Rohe, Leo SünkelCompleted on: August 6, 2025
Creation and Empirical Evaluation of Synthetic Data for NP-Hard Optimization Problems
Author: Gordian HuneckeSupervision: Jonas Nüßlein, Julian SchönbergerCompleted on: August 6, 2025
Gradient Descent Optimizability of Final Productive Fitness Landscapes in Evolutionary Algorithms
Author: Christoph PöltSupervision: Thomas Gabor, Max ZornCompleted on: August 6, 2025
Assessing Noise Robustness of Variational Quantum Circuits in Reinforcement Learning Environments
Author: Justin Dominik Marinus KleinSupervision: Julian Hager, Michael KölleCompleted on: July 2, 2025
This bachelor thesis investigates the robustness of variational quantum circuits (VQCs) in reinforcement learning (RL) compared to classical neural networks under the influence of observation noise. Observation noise describes the uncertainty that arises when the states perceived by an RL agent deviate from the actual states of the environment, for example due to sensor noise, environmental influences or targeted adversarial attacks. A deterministic REINFORCE algorithm is used, which always selects the action with the highest probability prediction instead of the usual stochastic sampling. This methodological decision enables a targeted analysis of the direct influence of observation noise on the agent’s policy, independent of random exploration effects. Robustness is investigated using the deterministic variant of the well-known reinforcement learning environment Frozen-Lake, which is extended by an observation noise model with a self designed hot zone logic. Within these hot zones, the agent receives deliberately incorrect observations orthogonal to its original direction of movement. A classical neural network in form of a multi-layer perceptron (MLP) is compared with a VQC. Although the MLP often converges faster, it exhibits volatile and non-monotonic performance under increasing noise influence. In contrast, the VQC demonstrates superior stability with a predictable performance degradation, especially at higher noise levels. The results suggest that the structural properties of VQCs may enable better generalisation and robustness against structured observation noise.
An Empirical Evaluation of Quantum Annealing-Based Image Classification Using Discriminative Quantum Boltzmann Machines
Author: Mark SeebodeSupervision: Jonas Stein, Daniëlle SchumanCompleted on: June 4, 2025
Analysis and Improvement of Retrieval Quality in the Context of RAG using LLMs on the Code Domain
Author: Melvin TjiokSupervision: Gerhard Stenzel, Michael KölleCompleted on: June 4, 2025
An Empirical Evaluation of Quantum Annealing-Based Image Classification Using Discriminative Quantum Boltzmann Machines
Author: Mark Vorapong SeebodeSupervision: Jonas Stein, Daniëlle SchumanCompleted on: June 4, 2025
The Boltzmann machine has been highly influential in the development of artificial intelligence, serving as a foundational framework for energy-based models and neural network research. However, its direct applications in modern deep learning have been severely limited due to computational constraints. Classical sampling methods have consistently proven to be inefficient, rendering the processing of high-dimensional inputs practically infeasible. Therefore, alternatives like the Restricted Boltzmann Machine (RBM) have been introduced, sacrificing expressiveness for faster computations. In contrast, Quantum Boltzmann Machines can efficiently sample from approximate Boltzmann distributions when implemented using quantum algorithms such as quantum annealing. Empirical results suggest that this approach can yield a more efficient sampling process than classical methods, enabling more effective exploration of energy landscapes while reducing computational overhead. Additionally, this also makes full connectivity possible, preserving the expressiveness of the original BM. Nonetheless, to the best knowledge of the author, only a sparse amount of other studies have explored the capability of QBMs for supervised learning. This is particularly true for an application-driven context using real quantum hardware. Thus, the primary goal of this work is the evaluation of the practical effectiveness of QBMs utilizing discriminative learning for the classification of real-world image data using a novel embedding approach to save expensive Quantum Processing Unit time. This is done by employing discriminative QBMs, which always clamp the input units to a data point regardless of the current phase. The model can therefore learn the conditional distribution of a label given a data point. The results demonstrate competitive performance compared to discriminative BMs trained with simulated annealing and discriminative RBMs, while also indicating a slight reduction in the number of training epochs required. Additionally, the embedding approach proposed in this work significantly accelerated sampling, with an average speedup of 69.65% over the conventional embedding.
Quantum Architecture Search for Solving Quantum Machine Learning Tasks
Author: Simon SalferSupervision: Michael Kölle, Philipp AltmannCompleted on: June 4, 2025
Quantum computing is a computing paradigm based on the principles of quantum mechanics. This makes it fundamentally different from classical computing. For selected problem domains, quantum computers are expected to offer a performance advantage, the so-called quantum advantage, which manifests itself in exponentially faster computation times or lower resource requirements. In the current Noisy Intermediate Scale Quantum era, quantum hardware is still limited in performance and highly error-prone. Variational Quantum Circuits represent an approach that is comparatively robust to these limitations. The performance of these quantum circuits is highly dependent on the underlying architecture of the parameterized quantum circuit. The development of powerful, hardware-compatible circuit architectures is therefore an important task, also known as Quantum Architecture Search. Developing good architectures manually is an inefficient and error-prone process. First attempts have been made to automate this process. In addition to Evolutionary Algorithms, Differentiable Architecture Search, and Monte Carlo Tree Search, Reinforcement Learning is another potentially suitable approach for finding good architectures, but it has been relatively little studied. In particular, little is known about its suitability as a search strategy for Machine Learning problems. The goal of this work is to investigate Reinforcement Learning as a suitable search strategy for quantum circuits in the context of Machine Learning problems. For this purpose, the RL-QAS framework is presented, which enables the automated search for circuit architectures using a Reinforcement Learning Agent. The RL-QAS framework is evaluated on the Iris and binary MNIST classification problems. RL-QAS enabled the discovery of architectures that achieve high test accuracy in the classification of the aforementioned datasets while exhibiting low complexity. RL-QAS demonstrated that Reinforcement Learning is indeed suitable for architecture discovery. However, in order for RL QAS to be applied to more complex problems, further development of the approach is necessary.