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Email:
sascha.muecke
tu-dortmund.de
Phone: 0231/755-7536 Room-No.: JvF25 R124 |
My main research interest is Quantum Computing and its applications to Machine Learning. In particular I focus on boolean optimization problems (QUBO, Ising model) that are solvable through Quantum Annealing on adiabatic quantum computers, but also on classical hardware. I developed an FPGA-based hardware solver for QUBO problems that employs a genetic algorithm. My ORCID is 0000-0001-8332-6169.
| Muecke/2023a |
Mücke, Sascha.
Coding Nuggets: Faster QUBO Brute-Force Solving.
TU Dortmund University,
2023.
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| Fischer/etal/2022a | Fischer, Raphael and Jakobs, Matthias and Mücke, Sascha and Morik, Katharina. A Unified Framework for Assessing Energy Efficiency of Machine Learning. In Proceedings of the ECML Workshop on Data Science for Social Good, 2022. |
| Franken/etal/2020a |
Franken, Lukas and Georgiev, Bogdan and Mücke, Sascha and Wolter, Moritz and Piatkowski, Nico and Bauckhage, Christian.
Quantum Circuit Evolution on NISQ Devices.
In
2022 IEEE Congress on Evolutionary Computation (CEC),
pages 1-8,
2022.
|
| Morik/etal/2021a |
Morik, Katharina and Kotthaus, Helena and Fischer, Raphael and Mücke, Sascha and Jakobs, Matthias and Piatkowski, Nico and Pauly, Andreas and Heppe, Lukas and Heinrich, Danny.
Yes We Care! - Certification for Machine Learning Methods through the Care Label Framework.
In
Elisa Fromont (editors),
Frontiers in Artificial Intelligence,
Frontiers,
2022.
|
| Muecke/etal/2022a |
Mücke, Sascha and Heese, Raoul and Müller, Sabine and Wolter, Moritz and Piatkowski, Nico.
Quantum Feature Selection.
arXiv,
2022.
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| Muecke/Piatkowski/2022a | Mücke, Sascha and Piatkowski, Nico. Quantum-Inspired Structure-Preserving Probabilistic Inference. In 2022 IEEE Congress on Evolutionary Computation (CEC), pages 1-9, 2022. |
| Gonsior/etal/2021a |
Felix Gonsior and Sascha Mücke and Katharina Morik.
Structure Search for Normalizing Flows.
In
Thomas Seidl and Michael Fromm and Sandra Obermeier (editors),
Proceedings of the LWDA 2021 Workshops: FGWM, KDML, FGWI-BIA, and FGIR, Online, September 1-3, 2021,
Vol. 2993,
pages 98--105,
CEUR-WS.org,
2021.
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| Heese/etal/2021a | Heese, Raoul and Wolter, Moritz and Mücke, Sascha and Franken, Lukas and Piatkowski, Nico. On the effects of biased quantum random numbers on the initialization of artificial neural networks. 2021. |
| Fischer/etal/2020a |
Fischer, Raphael and Jakobs, Matthias and Mücke, Sascha and Morik, Katharina.
Solving Abstract Reasoning Tasks with Grammatical Evolution.
In
Trabold, Daniel and Welke, Pascal and Piatkowski, Nico (editors),
Proceedings of the LWDA 2020 Workshops: KDML, FGWM, FGWI-BIA, and FGDB,
pages 6--10,
2020.
|
| Muecke/2019a | Mücke, Sascha. Evolutionäre Optimierung pseudoboolescher Funktionen auf FPGAs. TU Dortmund University, 2019. |
| Muecke/etal/2019a |
Mücke, Sascha and Piatkowski, Nico and Morik, Katharina.
Learning Bit by Bit: Extracting the Essence of Machine Learning.
In
Jäschke, Robert and Weidlich, Matthias (editors),
Proceedings of the Conference on "Lernen, Wissen, Daten, Analysen" (LWDA),
Vol. 2454,
pages 144--155,
2019.
|
| Muecke/Piatkowski/2019a |
Muecke, Sascha and Piatkowski, Nico and Morik, Katharina.
Hardware Accelerated Learning at the Edge.
In
Kamp, Michael and Paurat, Daniel and Krishnamurthy, Yamuna (editors),
Decentralized Machine Learning at the Edge,
Springer,
2019.
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