9 papers · 1 filter
Optimal algorithmic complexity of inference in quantum kernel methods
Elies Gil-Fuster, Seongwook Shin, Sofiene Jerbi +2
Quantum kernel methods are among the leading candidates for achieving quantum advantage in supervised learning. A key bottleneck is the cost of inference: evaluating a trained mode…
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…
Simulation of noisy quantum circuits using frame representations
Janek Denzler, Jose Carrasco, Jens Eisert +1
One of the core research questions in the theory of quantum computing is to find out to what precise extent the classical simulation of a noisy quantum circuits is possible and whe…
Reinforcement learning of quantum circuit architectures for molecular potential energy curves
Maureen Krumtünger, Alissa Wilms, Paul K. Faehrmann +4
Quantum chemistry and optimization are two of the most prominent applications of quantum computers. Variational quantum algorithms have been proposed for solving problems in these…
Stability of digital and analog quantum simulations under noise
Jayant Rao, Jens Eisert, Tommaso Guaita
Quantum simulation is a central application of near-term quantum devices, pursued in both analog and digital architectures. A key challenge for both paradigms is the effect of impe…
Energy-independent tomography of Gaussian states
Lennart Bittel, Francesco A. Mele, Jens Eisert +1
The exploration of tomography of bosonic Gaussian states is presumably as old as quantum optics, but only recently, their precise and rigorous study have been moving into the focus…