Adaptive Bayesian and frequentist data processing for quantum tomography
G. M. D'Ariano, D. F. Magnani, and P. Perinotti
TL;DR
This paper introduces two novel non-linear data-processing strategies for quantum tomography that improve convergence speed in estimating ensemble expectations from measurement outcomes.
Contribution
The paper presents new non-linear data-processing methods for quantum tomography that enhance convergence speed compared to existing techniques.
Findings
Faster convergence to theoretical expectations in quantum state estimation
Introduction of two non-linear data-processing strategies
Applicable to informationally complete quantum measurements
Abstract
The outcome statistics of an informationally complete quantum measurement for a system in a given state can be used to evaluate the ensemble expectation of any linear operator in the same state, by averaging a function of the outcomes that depends on the specific operator. Here we introduce two novel data-processing strategies, non-linear in the frequencies, which lead to faster convergence to theoretical expectations.
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