32 citations · 76 across the 7 of their papers we have counts for
7 papers
A Sovereign, Open-Source Foundation Model for German and English
Soofi-Team, :, Benedikt Droste +30
We present Soofi S 30B-A3B, a sovereign, open-source Mixture-of-Experts (MoE) hybrid Mamba Transformer foundation model for German and English. Its hybrid design activates only 3B…
Modalities, a PyTorch-native Framework For Large-scale LLM Training and Research
Max Lübbering, Timm Ruland, Richard Rutmann +8
Today's LLM (pre-) training and research workflows typically allocate a significant amount of compute to large-scale ablation studies. Despite the substantial compute costs of thes…
RydbergGPT
David Fitzek, Yi Hong Teoh, Hin Pok Fung +5
We introduce a generative pretained transformer (GPT) designed to learn the measurement outcomes of a neutral atom array quantum computer. Based on a vanilla transformer, our encod…
Inter-case Predictive Process Monitoring: A candidate for Quantum Machine Learning?
Stefan Hill, David Fitzek, Patrick Delfmann +1
Regardless of the domain, forecasting the future behaviour of a running process instance is a question of interest for decision makers, especially when multiple instances interact.…
Optimizing Variational Quantum Algorithms with qBang: Efficiently Interweaving Metric and Momentum to Navigate Flat Energy Landscapes
David Fitzek, Robert S. Jonsson, Werner Dobrautz +1
Variational quantum algorithms (VQAs) represent a promising approach to utilizing current quantum computing infrastructures. VQAs are based on a parameterized quantum circuit optim…
Applying quantum approximate optimization to the heterogeneous vehicle routing problem
David Fitzek, Toheed Ghandriz, Leo Laine +2
Quantum computing offers new heuristics for combinatorial problems. With small- and intermediate-scale quantum devices becoming available, it is possible to implement and test thes…
Deep Q-learning decoder for depolarizing noise on the toric code
David Fitzek, Mattias Eliasson, Anton Frisk Kockum +1
We present an AI-based decoding agent for quantum error correction of depolarizing noise on the toric code. The agent is trained using deep reinforcement learning (DRL), where an a…