Stochastic gradient descent for hybrid quantum-classical optimization. - Ryan Sweke; Frederik Wilde; Johannes Jakob Meyer; Maria Schuld; Paul K. Faehrmann; Barthélémy Meynard-Piganeau; Jens Eisert (Quantum, 2020)

Training Quantum Embedding Kernels on Near-Term Quantum Computers. - Thomas Hubregtsen; David Wierichs; Elies Gil-Fuster; Peter-Jan H. S. Derks; Paul K. Fährmann; Johannes Jakob Meyer (CoRR, 2021)

Encoding-dependent generalization bounds for parametrized quantum circuits. - Matthias C. Caro; Elies Gil-Fuster; Johannes Jakob Meyer; Jens Eisert; Ryan Sweke (Quantum, 2021)

Fisher Information in Noisy Intermediate-Scale Quantum Applications. - Johannes Jakob Meyer (Quantum, 2021)

Exploiting symmetry in variational quantum machine learning. - Johannes Jakob Meyer; Marian Mularski; Elies Gil-Fuster; Antonio Anna Mele; Francesco Arzani; Alissa Wilms; Jens Eisert (CoRR, 2022)

Classical surrogates for quantum learning models. - Franz J. Schreiber; Jens Eisert; Johannes Jakob Meyer (CoRR, 2022)

Potential and limitations of random Fourier features for dequantizing quantum machine learning. - Ryan Sweke; Erik Recio; Sofiène Jerbi; Elies Gil-Fuster; Bryce Fuller; Jens Eisert; Johannes Jakob Meyer (CoRR, 2023)

Potential and limitations of random Fourier features for dequantizing quantum machine learning. - Ryan Sweke; Erik Recio-Armengol; Sofiène Jerbi; Elies Gil-Fuster; Bryce Fuller; Jens Eisert; Johannes Jakob Meyer (Quantum, 2025)