Adaptive experimental design for one-qubit state estimation with finite data based on a statistical update criterion
Quantum Physics
2012-05-21 v2 Statistics Theory
Machine Learning
Statistics Theory
Abstract
We consider 1-qubit mixed quantum state estimation by adaptively updating measurements according to previously obtained outcomes and measurement settings. Updates are determined by the average-variance-optimality (A-optimality) criterion, known in the classical theory of experimental design and applied here to quantum state estimation. In general, A-optimization is a nonlinear minimization problem; however, we find an analytic solution for 1-qubit state estimation using projective measurements, reducing computational effort. We compare numerically two adaptive and two nonadaptive schemes for finite data sets and show that the A-optimality criterion gives more precise estimates than standard quantum tomography.
Cite
@article{arxiv.1203.3391,
title = {Adaptive experimental design for one-qubit state estimation with finite data based on a statistical update criterion},
author = {Takanori Sugiyama and Peter S. Turner and Mio Murao},
journal= {arXiv preprint arXiv:1203.3391},
year = {2012}
}
Comments
15 pages, 7 figures