activity
19982016
most citedArea laws for the entanglement entropy - a review

2.9k citations · 18.4k across the 101 of their papers we have counts for

collaborators

99 papers

quant-ph2016★ 8 cited

Multi-partite entanglement

Michael Walter, David Gross, Jens Eisert

We give an introduction to the theory of multi-partite entanglement. We begin by describing the "coordinate system" of the field: Are we dealing with pure or mixed states, with sin…

cs.IT2015★ 15 cited

Improving compressed sensing with the diamond norm

Martin Kliesch, Richard Kueng, Jens Eisert +1

In low-rank matrix recovery, one aims to reconstruct a low-rank matrix from a minimal number of linear measurements. Within the paradigm of compressed sensing, this is made computa…

math.ST2015★ 21 cited

Uncertainty Quantification for Matrix Compressed Sensing and Quantum Tomography Problems

Alexandra Carpentier, Jens Eisert, David Gross +1

We construct minimax optimal non-asymptotic confidence sets for low rank matrix recovery algorithms such as the Matrix Lasso or Dantzig selector. These are employed to devise adapt…

quant-ph2012★ 453 cited

Positive Wigner functions render classical simulation of quantum computation efficient

A. Mari, J. Eisert

We show that quantum circuits where the initial state and all the following quantum operations can be represented by positive Wigner functions can be classically efficiently simula…

quant-ph2012★ 46 cited

Recursive quantum detector tomography

L. Zhang, A. Datta, H. B. Coldenstrodt-Ronge +4

Conventional tomographic techniques are becoming increasingly infeasible for reconstructing the operators of quantum devices of growing sophistication. We describe a novel tomograp…

quant-ph2012★ 374 cited

Quantum Tomography via Compressed Sensing: Error Bounds, Sample Complexity, and Efficient Estimators

Steven T. Flammia, David Gross, Yi-Kai Liu +1

Intuitively, if a density operator has small rank, then it should be easier to estimate from experimental data, since in this case only a few eigenvectors need to be learned. We pr…