202 citations · 2.5k across the 68 of their papers we have counts for
10 papers · 1 filter
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…
Using deep learning to construct stochastic local search SAT solvers with performance bounds
Maximilian J. Kramer, Paul Boes, Jens Eisert
The Boolean Satisfiability problem (SAT), as the prototypical -complete problem, is crucial in both theoretical computer science and practical applications. To address…
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…
An introduction to infinite projected entangled-pair state methods for variational ground state simulations using automatic differentiation
Jan Naumann, Erik Lennart Weerda, Matteo Rizzi +2
Tensor networks capture large classes of ground states of phases of quantum matter faithfully and efficiently. Their manipulation and contraction has remained a challenge over the…