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quant-ph2025
AutoQML: A Framework for Automated Quantum Machine Learning
Marco Roth, David A. Kreplin, Daniel Basilewitsch +7
Automated Machine Learning (AutoML) has significantly advanced the efficiency of ML-focused software development by automating hyperparameter optimization and pipeline construction…
quant-ph2024
Quantum Neural Networks in Practice: A Comparative Study with Classical Models from Standard Data Sets to Industrial Images
Daniel Basilewitsch, João F. Bravo, Christian Tutschku +1
We compare the performance of randomized classical and quantum neural networks (NNs) as well as classical and quantum-classical hybrid convolutional neural networks (CNNs) for the…
quant-ph2024
Ion-Based Quantum Computing Hardware: Performance and End-User Perspective
Thomas Strohm, Karen Wintersperger, Florian Dommert +6
This is the second paper in a series of papers providing an overview of different quantum computing hardware platforms from an industrial end-user perspective. It follows our first…