TL;DR
This paper reviews adaptive quantum tomography techniques, emphasizing Bayesian methods for improved experimental design and state reconstruction, supported by recent experimental and numerical developments.
Contribution
It provides a comprehensive review of adaptive quantum tomography, advocating Bayesian approaches and analyzing recent experimental implementations.
Findings
Bayesian methods enhance quantum state reconstruction.
Adaptive protocols improve efficiency of quantum tomography.
Recent experiments validate the effectiveness of adaptive strategies.
Abstract
We provide a review of the experimental and theoretical research in the field of quantum tomography with an emphasis on recently developed adaptive protocols. Several statistical frameworks for adaptive experimental design are discussed. We argue in favor of the Bayesian approach, highlighting both its advantages for a statistical reconstruction of unknown quantum states and processes, and utility for adaptive experimental design. The discussion is supported by an analysis of several recent experimental implementations and numerical recipes.
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