23 citations · 45 across the 9 of their papers we have counts for
9 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.…
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…
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 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…
Reinforcement learning-based architecture search for quantum machine learning
Frederic Rapp, David A. Kreplin, Marco F. Huber +1
Quantum machine learning models use encoding circuits to map data into a quantum Hilbert space. While it is well known that the architecture of these circuits significantly influen…
sQUlearn -- A Python Library for Quantum Machine Learning
David A. Kreplin, Moritz Willmann, Jan Schnabel +3
sQUlearn introduces a user-friendly, NISQ-ready Python library for quantum machine learning (QML), designed for seamless integration with classical machine learning tools like scik…
Reduction of finite sampling noise in quantum neural networks
David A. Kreplin, Marco Roth
Quantum neural networks (QNNs) use parameterized quantum circuits with data-dependent inputs and generate outputs through the evaluation of expectation values. Calculating these ex…