3 papers
quant-ph2026
Grokking and epoch-wise double descent in quantum neural networks
Daniel PranjiÄ, Marco Roth, Christian Tutschku
Grokking, the delayed transition from memorization to generalization, is a fundamental phenomenon in gradient-based learning, yet its dynamics within variational quantum machine le…
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 Annealing based Feature Selection in Machine Learning
Daniel Pranjic, Bharadwaj Chowdary Mummaneni, Christian Tutschku
Feature selection is crucial for enhancing the accuracy and efficiency of machine learning (ML) models. This work investigates the utility of quantum annealing for the feature sele…