1 code implementation • 14 Apr 2024 • Gerhard Stenzel, Sebastian Zielinski, Michael Kölle, Philipp Altmann, Jonas Nüßlein, Thomas Gabor
To address the computational complexity associated with state-vector simulation for quantum circuits, we propose a combination of advanced techniques to accelerate circuit execution.
no code implementations • 13 Jan 2024 • Michael Kölle, Gerhard Stenzel, Jonas Stein, Sebastian Zielinski, Björn Ommer, Claudia Linnhoff-Popien
In recent years, machine learning models like DALL-E, Craiyon, and Stable Diffusion have gained significant attention for their ability to generate high-resolution images from concise descriptions.
1 code implementation • 13 Jan 2024 • Michael Kölle, Yannick Erpelding, Fabian Ritz, Thomy Phan, Steffen Illium, Claudia Linnhoff-Popien
Recent advances in Multi-Agent Reinforcement Learning have prompted the modeling of intricate interactions between agents in simulated environments.
no code implementations • 13 Jan 2024 • Michael Kölle, Mohamad Hgog, Fabian Ritz, Philipp Altmann, Maximilian Zorn, Jonas Stein, Claudia Linnhoff-Popien
In this work, we propose a novel quantum reinforcement learning approach that combines the Advantage Actor-Critic algorithm with variational quantum circuits by substituting parts of the classical components.
no code implementations • 13 Jan 2024 • Michael Kölle, Tom Schubert, Philipp Altmann, Maximilian Zorn, Jonas Stein, Claudia Linnhoff-Popien
With recent advancements in quantum computing technology, optimizing quantum circuits and ensuring reliable quantum state preparation have become increasingly vital.
no code implementations • 7 Jan 2024 • Robert Müller, Hasan Turalic, Thomy Phan, Michael Kölle, Jonas Nüßlein, Claudia Linnhoff-Popien
In the realm of Multi-Agent Reinforcement Learning (MARL), prevailing approaches exhibit shortcomings in aligning with human learning, robustness, and scalability.
1 code implementation • 18 Dec 2023 • Philipp Altmann, Jonas Stein, Michael Kölle, Adelina Bärligea, Thomas Gabor, Thomy Phan, Sebastian Feld, Claudia Linnhoff-Popien
Quantum computing (QC) in the current NISQ era is still limited in size and precision.
no code implementations • 14 Dec 2023 • Michael Kölle, Afrae Ahouzi, Pascal Debus, Robert Müller, Danielle Schuman, Claudia Linnhoff-Popien
Quantum computing, with its potential to enhance various machine learning tasks, allows significant advancements in kernel calculation and model precision.
no code implementations • 9 Dec 2023 • Jonas Stein, Navid Roshani, Maximilian Zorn, Philipp Altmann, Michael Kölle, Claudia Linnhoff-Popien
A central challenge in quantum machine learning is the design and training of parameterized quantum circuits (PQCs).
no code implementations • 9 Nov 2023 • Michael Kölle, Felix Topp, Thomy Phan, Philipp Altmann, Jonas Nüßlein, Claudia Linnhoff-Popien
We showed that our Variational Quantum Circuit approaches perform significantly better compared to a neural network with a similar amount of trainable parameters.
no code implementations • 9 Nov 2023 • Michael Kölle, Jonas Maurer, Philipp Altmann, Leo Sünkel, Jonas Stein, Claudia Linnhoff-Popien
We propose a novel hybrid architecture: instead of utilizing a pre-trained network for compression, we employ an autoencoder to derive a compressed version of the input data.
no code implementations • 28 Aug 2023 • Michael Kölle, Volker Walter, Ivan Shiller, Uwe Soergel
Accurate tree detection is of growing importance in applications such as urban planning, forest inventory, and environmental monitoring.
no code implementations • 28 Jun 2023 • Michael Kölle, Steffen Illium, Maximilian Zorn, Jonas Nüßlein, Patrick Suchostawski, Claudia Linnhoff-Popien
In the field of wildlife observation and conservation, approaches involving machine learning on audio recordings are becoming increasingly popular.
no code implementations • 9 Jun 2023 • Michael Kölle, Alessandro Giovagnoli, Jonas Stein, Maximilian Balthasar Mansky, Julian Hager, Tobias Rohe, Robert Müller, Claudia Linnhoff-Popien
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 machine learning applications.
no code implementations • 18 Jan 2023 • Michael Kölle, Tim Matheis, Philipp Altmann, Kyrill Schmid
Enabling autonomous agents to act cooperatively is an important step to integrate artificial intelligence in our daily lives.
no code implementations • 18 Jan 2023 • Michael Kölle, Steffen Illium, Carsten Hahn, Lorenz Schauer, Johannes Hutter, Claudia Linnhoff-Popien
The ubiquitous availability of mobile devices capable of location tracking led to a significant rise in the collection of GPS data.
no code implementations • 22 Dec 2022 • Michael Kölle, Alessandro Giovagnoli, Jonas Stein, Maximilian Balthasar Mansky, Julian Hager, Claudia Linnhoff-Popien
In recent years, quantum machine learning has seen a substantial increase in the use of variational quantum circuits (VQCs).
no code implementations • 20 Dec 2022 • Steffen Illium, Maximilian Zorn, Cristian Lenta, Michael Kölle, Claudia Linnhoff-Popien, Thomas Gabor
We introduce organism networks, which function like a single neural network but are composed of several neural particle networks; while each particle network fulfils the role of a single weight application within the organism network, it is also trained to self-replicate its own weights.
no code implementations • 20 Dec 2022 • Steffen Illium, Gretchen Griffin, Michael Kölle, Maximilian Zorn, Jonas Nüßlein, Claudia Linnhoff-Popien
We primarily utilize non-linear recombination of information within an image, fragmenting and occluding small information patches.
no code implementations • 5 Jul 2022 • Michael Kölle, Lennart Rietdorf, Kyrill Schmid
In this environment, reinforcement learning agents learn to trade successfully.
Multi-agent Reinforcement Learning reinforcement-learning +2
no code implementations • 14 Dec 2021 • Andreas Sedlmeier, Michael Kölle, Robert Müller, Leo Baudrexel, Claudia Linnhoff-Popien
In this work, we analyze existing and propose new metrics for the detection and quantification of multimodal uncertainty in RL based World Models.
no code implementations • 10 Feb 2021 • Michael Kölle, Dominik Laupheimer, Stefan Schmohl, Norbert Haala, Franz Rottensteiner, Jan Dirk Wegner, Hugo Ledoux
Automated semantic segmentation and object detection are of great importance in geospatial data analysis.