Adaptive quantum tomography
arXiv:1610.02840 · doi:10.1134/S0021364016190024
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.
A short review for JETP Letters. 12 pages, 6 figures
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Cited by in corpus (15)
- A Data-Driven Gradient Algorithm for High-Precision Quantum Control
- Machine Learning for Achieving Bose-Einstein Condensation of Thulium Atoms
- Quantum tomography for collider physics: Illustrations with lepton pair production
- Adaptive Quantum State Tomography with Active Learning
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