no code implementations • 18 Sep 2023 • Dominik Leib, Tobias Seidel, Sven Jäger, Raoul Heese, Caitlin Isobel Jones, Abhishek Awasthi, Astrid Niederle, Michael Bortz
We present a comprehensive case study comparing the performance of D-Waves' quantum-classical hybrid framework, Fujitsu's quantum-inspired digital annealer, and Gurobi's state-of-the-art classical solver in solving a transport robot scheduling problem.
1 code implementation • 22 Jan 2023 • Raoul Heese, Thore Gerlach, Sascha Mücke, Sabine Müller, Matthias Jakobs, Nico Piatkowski
The resulting attributions can be interpreted as explanations for why a specific circuit works well for a given task, improving the understanding of how to construct parameterized (or variational) quantum circuits, and fostering their human interpretability in general.
Explainable Artificial Intelligence (XAI) Quantum Machine Learning
no code implementations • 19 Jan 2023 • Raoul Heese, Sascha Mücke, Matthias Jakobs, Thore Gerlach, Nico Piatkowski
We propose a novel definition of Shapley values with uncertain value functions based on first principles using probability theory.
1 code implementation • 24 Mar 2022 • Sascha Mücke, Raoul Heese, Sabine Müller, Moritz Wolter, Nico Piatkowski
In machine learning, fewer features reduce model complexity.
1 code implementation • 30 Aug 2021 • Raoul Heese, Moritz Wolter, Sascha Mücke, Lukas Franken, Nico Piatkowski
Recent advances in practical quantum computing have led to a variety of cloud-based quantum computing platforms that allow researchers to evaluate their algorithms on noisy intermediate-scale quantum (NISQ) devices.
1 code implementation • 30 Aug 2021 • Raoul Heese, Patricia Bickert, Astrid Elisa Niederle
We propose a quantum representation of binary classification trees with binary features based on a probabilistic approach.
2 code implementations • 17 Jun 2021 • Moritz Wolter, Felix Blanke, Raoul Heese, Jochen Garcke
Additionally, this paper proposes to learn a model for the detection of synthetic images based on the wavelet-packet representation of natural and GAN-generated images.
1 code implementation • 4 Mar 2021 • Raoul Heese, Jochen Schmid, Michał Walczak, Michael Bortz
In a second step, the latent space representation of the training data is extended to the whole feature space by fitting a regression model to the transformed data.
no code implementations • 23 Dec 2020 • Lukas Franken, Bogdan Georgiev, Sascha Mücke, Moritz Wolter, Raoul Heese, Christian Bauckhage, Nico Piatkowski
The results provide intuition on how randomized search heuristics behave on actual quantum hardware and lay out a path for further refinement of evolutionary quantum gate circuits.
1 code implementation • 27 Aug 2020 • Raoul Heese, Michael Bortz
We present a novel adaptive optimization algorithm for black-box multi-objective optimization problems with binary constraints on the foundation of Bayes optimization.
no code implementations • 11 May 2020 • Raoul Heese, Lukas Morand, Dirk Helm, Michael Bortz
Using data from a simulated cup drawing process, we demonstrate how the inherent geometrical structure of cup meshes can be used to effectively prune an artificial neural network in a straightforward way.
no code implementations • 24 Jul 2019 • Raoul Heese, Michał Walczak, Lukas Morand, Dirk Helm, Michael Bortz
We address a non-unique parameter fitting problem in the context of material science.
1 code implementation • 29 Mar 2019 • Laura von Rueden, Sebastian Mayer, Katharina Beckh, Bogdan Georgiev, Sven Giesselbach, Raoul Heese, Birgit Kirsch, Julius Pfrommer, Annika Pick, Rajkumar Ramamurthy, Michal Walczak, Jochen Garcke, Christian Bauckhage, Jannis Schuecker
It considers the source of knowledge, its representation, and its integration into the machine learning pipeline.
no code implementations • 18 Feb 2019 • Raoul Heese, Michal Walczak, Tobias Seidel, Norbert Asprion, Michael Bortz
We propose a novel algorithm to explore such an unknown parameter space and improve its feasibility classification in an iterative way.