As quantum tomography is becoming a key component of the quantum engineering toolbox,
there is a need for a deeper understanding of the multitude of estimation methods available …

With nowadays steadily growing quantum processors, it is required to develop new quantum
tomography tools that are tailored for high-dimensional systems. In this work, we describe …

Whenever we do not have an informationally complete set of measurements, the estimate of
a quantum state cannot be uniquely determined. In this case, among the density matrices …

We propose a non-convex optimization algorithm, based on the Burer-Monteiro (BM)
factorization, for the quantum process tomography problem, in order to estimate a low-rank …

Quantum state tomography (QST) is the task of statistically constructing the density matrix of
an unknown quantum state by measuring its several copies. The presence of noise in the …

Several Bayesian estimation-based heuristics have been developed to perform quantum
state tomography (QST). Their ability to quantify uncertainties using region estimators and …

We propose a new quantum state reconstruction method that combines ideas from
compressed sensing, non-convex optimization, and acceleration methods. The algorithm …

When performing maximum-likelihood quantum-state tomography, one must find the
quantum state that maximizes the likelihood of the state given observed measurements on …

In this paper we present an inexact stepsize selection for the Diluted R\rho R algorithm, used
to obtain the maximum likelihood estimate to the density matrix in quantum state …

We present a Bayesian approach to the problem of estimating density matrices in quantum
state tomography. A general framework is presented based on a suitable mathematical …