activity
20182024
most citedAlgorithmic QUBO Formulations for k-SAT and Hamiltonian Cycles

18 citations · 46 across the 15 of their papers we have counts for

collaborators

25 papers

cs.DS202218 cited

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…

quant-ph2022

Simple Quantum State Encodings for Hybrid Programming of Quantum Simulators

Thomas Gabor, Marian Lingsch Rosenfeld, Claudia Linnhoff-Popien

Especially sparse quantum states can be efficiently encoded with simple classical data structures. We show the admissibility of using a classical database to encode quantum states…

quant-ph20221 cited

How to Approximate any Objective Function via Quadratic Unconstrained Binary Optimization

Thomas Gabor, Marian Lingsch Rosenfeld, Sebastian Feld +1

Quadratic unconstrained binary optimization (QUBO) has become the standard format for optimization using quantum computers, i.e., for both the quantum approximate optimization algo…

cs.LG202118 cited

Acoustic Leak Detection in Water Networks

Robert Müller, Steffen Illium, Fabian Ritz +4

In this work, we present a general procedure for acoustic leak detection in water networks that satisfies multiple real-world constraints such as energy efficiency and ease of depl…

cs.LG20204 cited

SAT-MARL: Specification Aware Training in Multi-Agent Reinforcement Learning

Fabian Ritz, Thomy Phan, Robert Müller +8

A characteristic of reinforcement learning is the ability to develop unforeseen strategies when solving problems. While such strategies sometimes yield superior performance, they m…

eess.AS2020

Surgical Mask Detection with Convolutional Neural Networks and Data Augmentations on Spectrograms

Steffen Illium, Robert Müller, Andreas Sedlmeier +1

In many fields of research, labeled datasets are hard to acquire. This is where data augmentation promises to overcome the lack of training data in the context of neural network en…