119 citations · 152 across the 29 of their papers we have counts for
17 papers · 1 filter
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 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…
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…
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…
Improving Convergence for Quantum Variational Classifiers using Weight Re-Mapping
Michael Kölle, Alessandro Giovagnoli, Jonas Stein +3
In recent years, quantum machine learning has seen a substantial increase in the use of variational quantum circuits (VQCs). VQCs are inspired by artificial neural networks, which…