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Diluted maximum-likelihood algorithm for quantum tomography

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arxiv quant-ph/0611244 v2 pith:YLHDV3GL submitted 2006-11-23 quant-ph

Jaroslav Rehacek , Zdenek Hradil , E. Knill , A. I. Lvovsky This is my paper · ORCID
classification quant-ph
keywords algorithmconvergencequantumtomographymaximum-likelihoodstatestatesadaptive
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We propose a refined iterative likelihood-maximization algorithm for reconstructing a quantum state from a set of tomographic measurements. The algorithm is characterized by a very high convergence rate and features a simple adaptive procedure that ensures likelihood increase in every iteration and convergence to the maximum-likelihood state. We apply the algorithm to homodyne tomography of optical states and quantum tomography of entangled spin states of trapped ions and investigate its convergence properties.

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Cited by 1 Pith paper

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  1. Connecting Quantum Tomography and Quantum Retrodiction

    quant-ph 2026-06 unverdicted novelty 5.0 of 10

    The Petz recovery map equals the gradient of the log-likelihood in maximum-likelihood tomography, unifying retrodiction and state reconstruction via a shared iterative procedure.

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