43 citations · 64 across the 10 of their papers we have counts for
10 papers
Classical Limits of Spectral Filtering in Quantum Generative Models
Marco Roth
Spectral filtering has been proposed as a route to regularization in quantum generative models: the quantum Fourier transform exposes the amplitude spectrum of a quantum circuit Bo…
Grokking and epoch-wise double descent in quantum neural networks
Daniel Pranjić, 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…
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…
On the similarity of bandwidth-tuned quantum kernels and classical kernels
Roberto Flórez-Ablan, Marco Roth, Jan Schnabel
Quantum kernels (QK) are widely used in quantum machine learning applications; yet, their potential to surpass classical machine learning methods on classical datasets remains unce…
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…
Application of quantum annealing for scalable robotic assembly line optimization: a case study
Moritz Willmann, Marcel Albus, Jan Schnabel +1
The even distribution and optimization of tasks across resources and workstations is a critical process in manufacturing aimed at maximizing efficiency, productivity, and profitabi…
Quantum Kernel Methods under Scrutiny: A Benchmarking Study
Jan Schnabel, Marco Roth
Since the entry of kernel theory in the field of quantum machine learning, quantum kernel methods (QKMs) have gained increasing attention with regard to both probing promising appl…
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…