3 papers
cs.LG2026
Long Range Frequency Tuning for QML
Michael Poppel, Markus Baumann, Sebastian Wölckert +2
Angle-encoded variational quantum circuits admit a truncated Fourier series representation of their output, but approximating functions with maximum frequency using fix…
quant-ph2026
Architecture Shape Governs QNN Trainability: Jacobian Null Space Growth and Parameter Efficiency
Michael Poppel, David Bucher, Maximilian Zorn +5
Variational quantum circuits with angle encoding implement truncated Fourier series, and architectures arranging qubits with encoding layers each -- sharing encoding budget…
quant-ph2025
Evaluating Parameter-Based Training Performance of Neural Networks and Variational Quantum Circuits
Michael Kölle, Alexander Feist, Jonas Stein +2
In recent years, neural networks (NNs) have driven significant advances in machine learning. However, as tasks grow more complex, NNs often require large numbers of trainable param…