11 citations · 11 across the 2 of their papers we have counts for
6 papers
Tensor network approaches for learning non-linear dynamical laws
A. Goeßmann, M. Götte, I. Roth +3
Given observations of a physical system, identifying the underlying non-linear governing equation is a fundamental task, necessary both for gaining understanding and generating det…
Quantum certification and benchmarking
J. Eisert, D. Hangleiter, N. Walk +5
Concomitant with the rapid development of quantum technologies, challenging demands arise concerning the certification and characterization of devices. The promises of the field ca…
Expressive power of tensor-network factorizations for probabilistic modeling, with applications from hidden Markov models to quantum machine learning
Ivan Glasser, Ryan Sweke, Nicola Pancotti +2
Tensor-network techniques have enjoyed outstanding success in physics, and have recently attracted attention in machine learning, both as a tool for the formulation of new learning…
Lieb-Robinson bounds for open quantum systems with long-ranged interactions
Ryan Sweke, Jens Eisert, Michael Kastner
We state and prove four types of Lieb-Robinson bounds valid for many-body open quantum systems with power law decaying interactions undergoing out of equilibrium dynamics. We also…
Randomized benchmarking for individual quantum gates
E. Onorati, A. H. Werner, J. Eisert
Any technology requires precise benchmarking of its components, and the quantum technologies are no exception. Randomized benchmarking allows for the relatively resource economical…
Hierarchical restricted isometry property for Kronecker product measurements
I. Roth, A. Flinth, R. Kueng +2
Hierarchically sparse signals and Kronecker product structured measurements arise naturally in a variety of applications. The simplest example of a hierarchical sparsity structure…