Výsledky vyhledávání - "Wolf, Maximilian A."

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Systematic Evaluation of Synthetic Data Augmentation for Multi-class NetFlow Traffic

Autor: Wolf, Maximilian, Landes, Dieter, Hotho, Andreas, Schlör, Daniel

The detection of cyber-attacks in computer networks is a crucial and ongoing research challenge. Machine learning-based attack classification offers a promising solution, as these models can be continuously updated with new data, enhancing the effect

Externí odkaz: http://arxiv.org/abs/2408.16034

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Towards Application-Aware Quantum Circuit Compilation

Autor: Quetschlich, Nils, Kiwit, Florian J., Wolf, Maximilian A., Riofrio, Carlos A., Burgholzer, Lukas, Luckow, Andre, Wille, Robert

Quantum computing has made tremendous improvements in both software and hardware that have sparked interest in academia and industry to realize quantum computing applications. To this end, several steps are necessary: The underlying problem must be e

Externí odkaz: http://arxiv.org/abs/2404.12433

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Benchmarking Quantum Generative Learning: A Study on Scalability and Noise Resilience using QUARK

Autor: Kiwit, Florian J., Wolf, Maximilian A., Marso, Marwa, Ross, Philipp, Lorenz, Jeanette M., Riofrío, Carlos A., Luckow, Andre

Quantum computing promises a disruptive impact on machine learning algorithms, taking advantage of the exponentially large Hilbert space available. However, it is not clear how to scale quantum machine learning (QML) to industrial-level applications.

Externí odkaz: http://arxiv.org/abs/2403.18662

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