5 citations · 6 across the 6 of their papers we have counts for
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Fourier Fingerprints of Ansatzes in Quantum Machine Learning
Melvin Strobl, M. Emre Sahin, Lucas van der Horst +3
Typical schemes to encode classical data in variational quantum machine learning (QML) lead to quantum Fourier models with Fourier basis functions in the num…
QML Essentials -- A framework for working with Quantum Fourier Models
Melvin Strobl, Maja Franz, Eileen Kuehn +2
In this work, we propose a framework in the form of a Python package, specifically designed for the analysis of Quantum Machine Learning models. This framework is based on the Penn…
Out of Tune: Demystifying Noise-Effects on Quantum Fourier Models
Maja Franz, Melvin Strobl, Leonid Chaichenets +3
Variational quantum algorithms have received substantial theoretical and empirical attention. As the underlying variational quantum circuit (VQC) can be represented by Fourier seri…
From Hope to Heuristic: Realistic Runtime Estimates for Quantum Optimisation in NHEP
Maja Franz, Manuel Schönberger, Melvin Strobl +5
Noisy Intermediate-Scale Quantum (NISQ) computers, despite their limitations, present opportunities for near-term quantum advantages in Nuclear and High-Energy Physics (NHEP) when…