34 citations · 94 across the 21 of their papers we have counts for
21 papers
Evaluating Variational Quantum Circuit Architectures for Distributed Quantum Computing
Leo Sünkel, Jonas Stein, Jonas Nüßlein +2
Scaling quantum computers, i.e., quantum processing units (QPUs) to enable the execution of large quantum circuits is a major challenge, especially for applications that should pro…
Reducing QUBO Density by Factoring Out Semi-Symmetries
Jonas Nüßlein, Leo Sünkel, Jonas Stein +6
Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing are prominent approaches for solving combinatorial optimization problems, such as those formulated as Quadra…
Optimizing Sensor Redundancy in Sequential Decision-Making Problems
Jonas Nüßlein, Maximilian Zorn, Fabian Ritz +5
Reinforcement Learning (RL) policies are designed to predict actions based on current observations to maximize cumulative future rewards. In real-world applications (i.e., non-simu…
Reducing QAOA Circuit Depth by Factoring out Semi-Symmetries
Jonas Nüßlein, Leo Sünkel, Jonas Stein +4
QAOA is a quantum algorithm for solving combinatorial optimization problems. It is capable of searching for the minimizing solution vector of a QUBO problem . The number…
Solving Max-3SAT Using QUBO Approximation
Sebastian Zielinski, Jonas Nüßlein, Michael Kölle +3
As contemporary quantum computers do not possess error correction, any calculation performed by these devices can be considered an involuntary approximation. To solve a problem on…
Architectural Influence on Variational Quantum Circuits in Multi-Agent Reinforcement Learning: Evolutionary Strategies for Optimization
Michael Kölle, Karola Schneider, Sabrina Egger +5
In recent years, Multi-Agent Reinforcement Learning (MARL) has found application in numerous areas of science and industry, such as autonomous driving, telecommunications, and glob…
The Questionable Influence of Entanglement in Quantum Optimisation Algorithms
Tobias Rohe, Daniëlle Schuman, Jonas Nüßlein +3
The performance of the Variational Quantum Eigensolver (VQE) is promising compared to other quantum algorithms, but also depends significantly on the appropriate design of the unde…
Demonstrating Quantum Scaling Advantage in Approximate Optimization for Energy Coalition Formation with 100+ Agents
Naeimeh Mohseni, Thomas Morstyn, Corey O'Meara +3
The formation of energy communities is pivotal for advancing decentralized and sustainable energy management. Within this context, Coalition Structure Generation (CSG) emerges as a…
Quantum Optimization for the Future Energy Grid: Summary and Quantum Utility Prospects
Jonas Blenninger, David Bucher, Giorgio Cortiana +6
In this project summary paper, we summarize the key results and use-cases explored in the German Federal Ministry of Education and Research (BMBF) funded project "Q-GRID" which aim…
ClusterComm: Discrete Communication in Decentralized MARL using Internal Representation Clustering
Robert Müller, Hasan Turalic, Thomy Phan +3
In the realm of Multi-Agent Reinforcement Learning (MARL), prevailing approaches exhibit shortcomings in aligning with human learning, robustness, and scalability. Addressing this,…
Multi-Agent Quantum Reinforcement Learning using Evolutionary Optimization
Michael Kölle, Felix Topp, Thomy Phan +3
Multi-Agent Reinforcement Learning is becoming increasingly more important in times of autonomous driving and other smart industrial applications. Simultaneously a promising new ap…
Incentivising Demand Side Response through Discount Scheduling using Hybrid Quantum Optimization
David Bucher, Jonas Nüßlein, Corey O'Meara +5
Demand Side Response (DSR) is a strategy that enables consumers to actively participate in managing electricity demand. It aims to alleviate strain on the grid during high demand a…
Improving Primate Sounds Classification using Binary Presorting for Deep Learning
Michael Kölle, Steffen Illium, Maximilian Zorn +3
In the field of wildlife observation and conservation, approaches involving machine learning on audio recordings are becoming increasingly popular. Unfortunately, available dataset…
Mapping quantum circuits to modular architectures with QUBO
Medina Bandic, Luise Prielinger, Jonas Nüßlein +8
Modular quantum computing architectures are a promising alternative to monolithic QPU (Quantum Processing Unit) designs for scaling up quantum devices. They refer to a set of inter…
Pattern QUBOs: Algorithmic construction of 3SAT-to-QUBO transformations
Sebastian Zielinski, Jonas Nüßlein, Jonas Stein +3
3SAT instances need to be transformed into instances of Quadratic Unconstrained Binary Optimization (QUBO) to be solved on a quantum annealer. Although it has been shown that the c…
Influence of Different 3SAT-to-QUBO Transformations on the Solution Quality of Quantum Annealing: A Benchmark Study
Sebastian Zielinski, Jonas Nüßlein, Jonas Stein +3
To solve 3SAT instances on quantum annealers they need to be transformed to an instance of Quadratic Unconstrained Binary Optimization (QUBO). When there are multiple transformatio…
CROP: Towards Distributional-Shift Robust Reinforcement Learning using Compact Reshaped Observation Processing
Philipp Altmann, Fabian Ritz, Leonard Feuchtinger +3
The safe application of reinforcement learning (RL) requires generalization from limited training data to unseen scenarios. Yet, fulfilling tasks under changing circumstances is a…
Solving (Max) 3-SAT via Quadratic Unconstrained Binary Optimization
Jonas Nüßlein, Sebastian Zielinski, Thomas Gabor +2
We introduce a novel approach to translate arbitrary 3-SAT instances to Quadratic Unconstrained Binary Optimization (QUBO) as they are used by quantum annealing (QA) or the quantum…
Attention-Based Recurrence for Multi-Agent Reinforcement Learning under Stochastic Partial Observability
Thomy Phan, Fabian Ritz, Philipp Altmann +5
Stochastic partial observability poses a major challenge for decentralized coordination in multi-agent reinforcement learning but is largely neglected in state-of-the-art research…
VoronoiPatches: Evaluating A New Data Augmentation Method
Steffen Illium, Gretchen Griffin, Michael Kölle +3
Overfitting is a problem in Convolutional Neural Networks (CNN) that causes poor generalization of models on unseen data. To remediate this problem, many new and diverse data augme…
Algorithmic QUBO Formulations for k-SAT and Hamiltonian Cycles
Jonas Nüßlein, Thomas Gabor, Claudia Linnhoff-Popien +1
Quadratic unconstrained binary optimization (QUBO) can be seen as a generic language for optimization problems. QUBOs attract particular attention since they can be solved with qua…