Title	Reinforcement Learning for Robust Adaptive Quantum-Enhanced Metrology
Creator	Palittapongarnpim, Pantita
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2017-08-17T17:00
Description	Quantum feedback control is challenging to implement as a measurement on a quantum state only reveals partial information of the state. A feedback procedure can be developed based on a trusted model of the system dynamic, which is typically not available in practical applications. We aim to devise tractable methods to generate effective feedback procedures that do not depend on trusted models. As an application, we construct a reinforcement-learning algorithm to generate adaptive for quantum-enhanced phase estimation in the presence of arbitrary phase noise. Our algorithm exploits noise-resistant differential evolution and introducesan accept-reject criterion. Our robust method shows a path forward to realizing adaptive quantum metrology with unknown noise properties.
Extent	29 minutes
Subject	Mathematics; Quantum theory; Optics, electromagnetic theory; Mathematical physics
Type	Moving Image
File Format	video/mp4
Language	eng
Notes	Author affiliation: U. of Calgary
Series	BIRS Workshop Lecture Videos (Oaxaca de Juárez (Mexico))
Date Available	2018-02-14
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivatives 4.0 International
DOI	10.14288/1.0363503
URI	http://hdl.handle.net/2429/64627
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Graduate
Rights URI	http://creativecommons.org/licenses/by-nc-nd/4.0/
Aggregated Source Repository	DSpace

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Reinforcement Learning for Robust Adaptive Quantum-Enhanced Metrology Palittapongarnpim, Pantita

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