202 citations · 2.5k across the 68 of their papers we have counts for
8 papers · 2 filters
Learning fermionic correlations by evolving with random translationally invariant Hamiltonians
Janek Denzler, Antonio Anna Mele, Ellen Derbyshire +2
Schemes of classical shadows have been developed to facilitate the read-out of digital quantum devices, but similar tools for analog quantum simulators are scarce and experimentall…
On the expressivity of embedding quantum kernels
Elies Gil-Fuster, Jens Eisert, Vedran Dunjko
One of the most natural connections between quantum and classical machine learning has been established in the context of kernel methods. Kernel methods rely on kernels, which are…
Potential and limitations of random Fourier features for dequantizing quantum machine learning
Ryan Sweke, Erik Recio-Armengol, Sofiene Jerbi +4
Quantum machine learning is arguably one of the most explored applications of near-term quantum devices. Much focus has been put on notions of variational quantum machine learning…
Pseudomagic Quantum States
Andi Gu, Lorenzo Leone, Soumik Ghosh +3
Notions of nonstabilizerness, or "magic", quantify how non-classical quantum states are in a precise sense: states exhibiting low nonstabilizerness preclude quantum advantage. We i…
Generalised linear response theory for the full quantum work statistics
Giacomo Guarnieri, Jens Eisert, Harry J. D. Miller
We consider a quantum system driven out of equilibrium via a small Hamiltonian perturbation. Building on the paradigmatic framework of linear response theory (LRT), we derive an ex…
Understanding quantum machine learning also requires rethinking generalization
Elies Gil-Fuster, Jens Eisert, Carlos Bravo-Prieto
Quantum machine learning models have shown successful generalization performance even when trained with few data. In this work, through systematic randomization experiments, we sho…