"Adaptive Rank Quantum State Estimation"
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 that structure. This parameterization naturally leads to a quantum state estimation algorithm that does satisfy the statistical requirements for the use of maximum-likelihood estimation. Finally, we examine the algorithm through the analysis of several data sets and experimental results.
- Dr. Xiaorong Qin, Chair
- Dr. David W. Kribs, Advisor
- Dr. Kevin J. Resch, Co-advisor
- Dr. Elisabeth J. Nicol
- Dr. Robert W. Spekkens (Perimeter Institute) External Examiner
Dr. David Kribs, Dr. Kevin J. Resch