Maximum a posteriori estimation of quantum states
arXiv:1805.12235 · doi:10.1103/PhysRevA.99.012342
Abstract
Using a Bayesian methodology, we introduce the maximum a posteriori~(MAP) estimator for quantum state and process tomography. The maximum likelihood, hedged maximum likelihood, maximum likelihood-maximum entropy estimator, and estimators of this general type, can be viewed as special cases of the MAP estimator. The MAP, like the Bayes mean estimator includes prior knowledge, however for cases of interest to tomography can take advantage of convex optimization tools making it numerically easier to compute. We show how the MAP and other Bayesian quantum state estimators can be corrected for noise produced if the quantum state passes through a noisy quantum channel prior to measurement.
arXiv comment10 pages. Version 2 has an additional Sec. 4.1 with application to qubit tomography under noise, and an Appendix on convex optimization over qubit density operators
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