Projected least squares is an intuitive and numerically cheap technique for quantum state
tomography: compute the least-squares estimator and project it onto the space of states. The …

ROP: Matrix recovery via rank-one projections Page 1 The Annals of Statistics 2015, Vol. 43, No.
1, 102–138 DOI: 10.1214/14-AOS1267 © Institute of Mathematical Statistics, 2015 ROP: MATRIX …

Quantum state tomography aims to determine the state of a quantum system as represented
by a density matrix. It is a fundamental task in modern scientific studies involving quantum …

Quantum state tomography, an important task in quantum information processing, aims at
reconstructing a state from prepared measurement data. Bayesian methods are recognized …

The statistical analysis of measurement data has become a key component of many
quantum engineering experiments. As standard full state tomography becomes unfeasible …

The problem of low-rank matrix estimation recently received a lot of attention due to
challenging applications. A lot of work has been done on rank-penalized methods [1] and …

In this paper, we study extended linear regression approaches for quantum state
tomography based on regularization techniques. For unknown quantum states represented …

High-dimensional matrix regression has been studied in various aspects, such as statistical
properties, computational efficiency and application to specific instances including …

In this work, we present an efficient rank-compression approach for the classical simulation
of Kraus decoherence channels in noisy quantum circuits. The approximation is achieved …

Let S_m be the set of all m*m density matrices (Hermitian positively semi-definite matrices of
unit trace). Consider a problem of estimation of an unknown density matrix ρ∈S_m based …