23 citations · 45 across the 9 of their papers we have counts for
3 papers · 1 filter
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…