activity
20202026
most citedExploring Unsupervised Anomaly Detection with Quantum Boltzmann Machines in Fraud Detection

13 citations · 31 across the 13 of their papers we have counts for

collaborators

13 papers

cs.LG2026

Reinforcement Learning for Parameterized Quantum State Preparation: A Comparative Study

Gerhard Stenzel, Isabella Debelic, Michael Kölle +4

We extend directed quantum circuit synthesis (DQCS) with reinforcement learning from purely discrete gate selection to parameterized quantum state preparation with continuous singl…

quant-ph2026

An Evaluation of the Remote CX Protocol under Noise in Distributed Quantum Computing

Leo Sünkel, Michael Kölle, Tobias Rohe +1

Quantum computers connected through classical and quantum communication channels can be combined to function as a single unit to run large quantum circuits that each device is unab…

quant-ph2026

An Adaptive Purification Controller for Quantum Networks: Dynamic Protocol Selection and Multipartite Distillation

Pranav Kulkarni, Leo Sünkel, Michael Kölle

Efficient entanglement distribution is a cornerstone of the Quantum Internet. However, physical link parameters such as photon loss, memory coherence time, and gate error rates flu…

quant-ph2026

Emergent Cooperation in Quantum Multi-Agent Reinforcement Learning Using Communication

Michael Kölle, Christian Reff, Leo Sünkel +3

Emergent cooperation in classical Multi-Agent Reinforcement Learning has gained significant attention, particularly in the context of Sequential Social Dilemmas (SSDs). While class…

cs.LG2026

Quantum King-Ring Domination in Chess: A QAOA Approach

Gerhard Stenzel, Michael Kölle, Tobias Rohe +4

The Quantum Approximate Optimization Algorithm (QAOA) is extensively benchmarked on synthetic random instances such as MaxCut, TSP, and SAT problems, but these lack semantic struct…

quant-ph2025

Quantum Circuit Construction and Optimization through Hybrid Evolutionary Algorithms

Leo Sünkel, Philipp Altmann, Michael Kölle +3

We apply a hybrid evolutionary algorithm to minimize the depth of circuits in quantum computing. More specifically, we evaluate two different variants of the algorithm. In the firs…

quant-ph2025

Evaluating Mutation Techniques in Genetic Algorithm-Based Quantum Circuit Synthesis

Michael Kölle, Tom Bintener, Maximilian Zorn +4

Quantum computing leverages the unique properties of qubits and quantum parallelism to solve problems intractable for classical systems, offering unparalleled computational potenti…

cs.AI2025

Optimization of Link Configuration for Satellite Communication Using Reinforcement Learning

Tobias Rohe, Michael Kölle, Jan Matheis +3

Satellite communication is a key technology in our modern connected world. With increasingly complex hardware, one challenge is to efficiently configure links (connections) on a sa…

quant-ph2024

Towards Federated Learning on the Quantum Internet

Leo Sünkel, Michael Kölle, Tobias Rohe +1

While the majority of focus in quantum computing has so far been on monolithic quantum systems, quantum communication networks and the quantum internet in particular are increasing…

quant-ph2023★ 8 cited

Hybrid Quantum Machine Learning Assisted Classification of COVID-19 from Computed Tomography Scans

Leo Sünkel, Darya Martyniuk, Julia J. Reichwald +5

Practical quantum computing (QC) is still in its infancy and problems considered are usually fairly small, especially in quantum machine learning when compared to its classical cou…

quant-ph2023★ 13 cited

Exploring Unsupervised Anomaly Detection with Quantum Boltzmann Machines in Fraud Detection

Jonas Stein, Daniëlle Schuman, Magdalena Benkard +7

Anomaly detection in Endpoint Detection and Response (EDR) is a critical task in cybersecurity programs of large companies. With rapidly growing amounts of data and the omnipresenc…

quant-ph2023★ 9 cited

SEQUENT: Towards Traceable Quantum Machine Learning using Sequential Quantum Enhanced Training

Philipp Altmann, Leo Sünkel, Jonas Stein +3

Applying new computing paradigms like quantum computing to the field of machine learning has recently gained attention. However, as high-dimensional real-world applications are not…

quant-ph2020★ 1 cited

The Holy Grail of Quantum Artificial Intelligence: Major Challenges in Accelerating the Machine Learning Pipeline

Thomas Gabor, Leo Sünkel, Fabian Ritz +5

We discuss the synergetic connection between quantum computing and artificial intelligence. After surveying current approaches to quantum artificial intelligence and relating them…