Adaptive Rank Quantum State Estimation

Loading...
Thumbnail Image

Date

2014-06-24

Authors

Pasieka, Aron

Journal Title

Journal ISSN

Volume Title

Publisher

University of Guelph

Abstract

Quantum state estimation is an important task in the realm of experimental quantum information science. While the common approaches to this task are generally adequate, the maximum-likelihood method is frequently used with parameterizations that do not strictly satisfy the statistical requirements of maximum-likelihood estimation. We show that the source of this issue is in the structure of the parameter space and we introduce a density matrix parameterization that provides direct control over this structure. The parameterization leads to a natural quantum state estimation algorithm that does satisfy the statistical requirements of maximum-likelihood estimation. Finally, we examine the algorithm through the analysis of several data sets and experimental results.

Description

Keywords

quantum state estimation, adaptive rank, maximum likelihood estimation, quantum information, quantum computing

Citation