119 citations · 152 across the 29 of their papers we have counts for
29 papers
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…
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…
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…
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…
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…
Quantum Architecture Search for Solving Quantum Machine Learning Tasks
Michael Kölle, Simon Salfer, Tobias Rohe +2
Quantum computing leverages quantum mechanics to address computational problems in ways that differ fundamentally from classical approaches. While current quantum hardware remains…
Investigating the Lottery Ticket Hypothesis for Variational Quantum Circuits
Michael Kölle, Leonhard Klingert, Julian Schönberger +3
Quantum computing is an emerging field in computer science that has seen considerable progress in recent years, especially in machine learning. By harnessing the principles of quan…
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…
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…
PIMAEX: Multi-Agent Exploration through Peer Incentivization
Michael Kölle, Johannes Tochtermann, Julian Schönberger +3
While exploration in single-agent reinforcement learning has been studied extensively in recent years, considerably less work has focused on its counterpart in multi-agent reinforc…
Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs
Tobias Rohe, Florian Burger, Michael Kölle +3
The demand for artificially generated data for the development, training and testing of new algorithms is omnipresent. Quantum computing (QC), does offer the hope that its inherent…
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…
Efficient Quantum One-Class Support Vector Machines for Anomaly Detection Using Randomized Measurements and Variable Subsampling
Michael Kölle, Afrae Ahouzi, Pascal Debus +4
Quantum one-class support vector machines leverage the advantage of quantum kernel methods for semi-supervised anomaly detection. However, their quadratic time complexity with resp…
A Study on Optimization Techniques for Variational Quantum Circuits in Reinforcement Learning
Michael Kölle, Timo Witter, Tobias Rohe +3
Quantum Computing aims to streamline machine learning, making it more effective with fewer trainable parameters. This reduction of parameters can speed up the learning process and…
Qandle: Accelerating State Vector Simulation Using Gate-Matrix Caching and Circuit Splitting
Gerhard Stenzel, Sebastian Zielinski, Michael Kölle +3
To address the computational complexity associated with state-vector simulation for quantum circuits, we propose a combination of advanced techniques to accelerate circuit executio…
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…
Towards Efficient Quantum Anomaly Detection: One-Class SVMs using Variable Subsampling and Randomized Measurements
Michael Kölle, Afrae Ahouzi, Pascal Debus +3
Quantum computing, with its potential to enhance various machine learning tasks, allows significant advancements in kernel calculation and model precision. Utilizing the one-class…
Efficient and Accurate Tree Detection from 3D Point Clouds through Paid Crowdsourcing
Michael Kölle, Volker Walter, Ivan Shiller +1
Accurate tree detection is of growing importance in applications such as urban planning, forest inventory, and environmental monitoring. In this article, we present an approach to…
Weight Re-Mapping for Variational Quantum Algorithms
Michael Kölle, Alessandro Giovagnoli, Jonas Stein +5
Inspired by the remarkable success of artificial neural networks across a broad spectrum of AI tasks, variational quantum circuits (VQCs) have recently seen an upsurge in quantum m…
Introducing Reduced-Width QNNs, an AI-inspired Ansatz Design Pattern
Jonas Stein, Tobias Rohe, Francesco Nappi +5
Variational Quantum Algorithms are one of the most promising candidates to yield the first industrially relevant quantum advantage. Being capable of arbitrary function approximatio…
Benchmarking Quantum Surrogate Models on Scarce and Noisy Data
Jonas Stein, Michael Poppel, Philip Adamczyk +9
Surrogate models are ubiquitously used in industry and academia to efficiently approximate given black box functions. As state-of-the-art methods from classical machine learning fr…
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…
Evidence that PUBO outperforms QUBO when solving continuous optimization problems with the QAOA
Jonas Stein, Farbod Chamanian, Maximilian Zorn +4
Quantum computing provides powerful algorithmic tools that have been shown to outperform established classical solvers in specific optimization tasks. A core step in solving optimi…
Compression of GPS Trajectories using Autoencoders
Michael Kölle, Steffen Illium, Carsten Hahn +3
The ubiquitous availability of mobile devices capable of location tracking led to a significant rise in the collection of GPS data. Several compression methods have been developed…