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)

Single-component gradient rules for variational quantum algorithms. - Thomas Hubregtsen; Frederik Wilde; Shozab Qasim; Jens Eisert (CoRR, 2021)

Scalably learning quantum many-body Hamiltonians from dynamical data. - Frederik Wilde; Augustine Kshetrimayum; Ingo Roth; Dominik Hangleiter; Ryan Sweke; Jens Eisert (CoRR, 2022)

Noise can be helpful for variational quantum algorithms. - Junyu Liu; Frederik Wilde; Antonio Anna Mele; Liang Jiang; Jens Eisert (CoRR, 2022)

A super-polynomial quantum advantage for combinatorial optimization problems. - Niklas Pirnay; Vincent Ulitzsch; Frederik Wilde; Jens Eisert; Jean-Pierre Seifert (CoRR, 2022)