Efficient online quantum state estimation using a matrix-exponentiated gradient method

In this paper, we explore an efficient online algorithm for quantum state estimation based on a matrix-exponentiated gradient method previously used in the context of machine learning. The state update is governed by a learning rate that determines how much weight is given to the new measurement res...

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Vydané v:	arXiv.org
Hlavní autori:	Youssry, Akram, Ferrie, Christopher, Tomamichel, Marco
Médium:	Paper
Jazyk:	English
Vydavateľské údaje:	Ithaca Cornell University Library, arXiv.org 07.03.2019
Predmet:	Algorithms Convergence Learning curves Machine learning Maximum likelihood estimators Noise threshold Quantum theory State estimation Weight
ISSN:	2331-8422
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