34 citations · 94 across the 21 of their papers we have counts for
9 papers · 1 filter
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…