6 papers
Constraint-Data-Value-Maximization: Utilizing Data Attribution for Effective Data Pruning in Low-Data Environments
Danilo Brajovic, David A. Kreplin, Marco F. Huber
Attributing model behavior to training data is an evolving research field. A common benchmark is data removal, which involves eliminating data instances with either low or high val…
Exponential Scaling Barriers for Variational Quantum Eigensolvers
Manuel Hagelueken, David A. Kreplin, Florian Wieland +2
The Variational Quantum Eigensolver (VQE) is widely regarded as a promising algorithm for calculating ground states of quantum systems that are intractable for classical computers.…
Data-Efficient Quantum Noise Modeling via Machine Learning
Yanjun Ji, Marco Roth, David A. Kreplin +2
Maximizing the computational utility of near-term quantum processors requires predictive noise models that inform robust, noise-aware compilation and error mitigation. Conventional…
Quantum Deep Learning: A Comprehensive Review
Yanjun Ji, Zhao-Yun Chen, Marco Roth +10
Quantum deep learning (QDL) explores the use of both quantum and quantum-inspired resources to determine when deep learning's core capabilities, such as expressivity, generalizatio…
Quantum vs. classical: A comprehensive benchmark study for predicting time series with variational quantum machine learning
Tobias Fellner, David Kreplin, Samuel Tovey +1
Variational quantum machine learning algorithms have been proposed as promising tools for time series prediction, with the potential to handle complex sequential data more effectiv…
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…