52 citations · 54 across the 5 of their papers we have counts for
5 papers
A PAC-Bayesian approach to generalization for quantum models
Pablo Rodriguez-Grasa, Matthias C. Caro, Jens Eisert +3
Generalization is a central concept in machine learning theory, yet for quantum models, it is predominantly analyzed through uniform bounds that depend on a model's overall capacit…
A measurement-driven quantum algorithm for SAT: Performance guarantees via spectral gaps and measurement parallelization
Franz J. Schreiber, Maximilian J. Kramer, Alexander Nietner +1
The Boolean satisfiability problem (SAT) is of central importance in both theory and practice. Yet, most provable guarantees for quantum algorithms rely exclusively on Grover-type…
Learning complexity gradually in quantum machine learning models
Erik Recio-Armengol, Franz J. Schreiber, Jens Eisert +1
Quantum machine learning is an emergent field that continues to draw significant interest for its potential to offer improvements over classical algorithms in certain areas. Howeve…
Tomography of parametrized quantum states
Franz J. Schreiber, Jens Eisert, Johannes Jakob Meyer
Characterizing quantum systems is a fundamental task that enables the development of quantum technologies. Various approaches, ranging from full tomography to instances of classica…
Classical surrogates for quantum learning models
Franz J. Schreiber, Jens Eisert, Johannes Jakob Meyer
The advent of noisy intermediate-scale quantum computers has put the search for possible applications to the forefront of quantum information science. One area where hopes for an a…