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Asymptotically Optimal Adversarial Strategies for the Probability Estimation Framework

The probability estimation framework involves direct estimation of the probability of occurrences of outcomes conditioned on measurement settings and side information. It is a powerful tool for certifying randomness in quantum nonlocality experiments. In this paper, we present a self-contained proof...

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Autores principales: Patra, Soumyadip, Bierhorst, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10667995/
https://www.ncbi.nlm.nih.gov/pubmed/37761589
http://dx.doi.org/10.3390/e25091291
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author Patra, Soumyadip
Bierhorst, Peter
author_facet Patra, Soumyadip
Bierhorst, Peter
author_sort Patra, Soumyadip
collection PubMed
description The probability estimation framework involves direct estimation of the probability of occurrences of outcomes conditioned on measurement settings and side information. It is a powerful tool for certifying randomness in quantum nonlocality experiments. In this paper, we present a self-contained proof of the asymptotic optimality of the method. Our approach refines earlier results to allow a better characterisation of optimal adversarial attacks on the protocol. We apply these results to the (2,2,2) Bell scenario, obtaining an analytic characterisation of the optimal adversarial attacks bound by no-signalling principles, while also demonstrating the asymptotic robustness of the PEF method to deviations from expected experimental behaviour. We also study extensions of the analysis to quantum-limited adversaries in the (2,2,2) Bell scenario and no-signalling adversaries in higher  [Formula: see text]  Bell scenarios.
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spelling pubmed-106679952023-09-02 Asymptotically Optimal Adversarial Strategies for the Probability Estimation Framework Patra, Soumyadip Bierhorst, Peter Entropy (Basel) Article The probability estimation framework involves direct estimation of the probability of occurrences of outcomes conditioned on measurement settings and side information. It is a powerful tool for certifying randomness in quantum nonlocality experiments. In this paper, we present a self-contained proof of the asymptotic optimality of the method. Our approach refines earlier results to allow a better characterisation of optimal adversarial attacks on the protocol. We apply these results to the (2,2,2) Bell scenario, obtaining an analytic characterisation of the optimal adversarial attacks bound by no-signalling principles, while also demonstrating the asymptotic robustness of the PEF method to deviations from expected experimental behaviour. We also study extensions of the analysis to quantum-limited adversaries in the (2,2,2) Bell scenario and no-signalling adversaries in higher  [Formula: see text]  Bell scenarios. MDPI 2023-09-02 /pmc/articles/PMC10667995/ /pubmed/37761589 http://dx.doi.org/10.3390/e25091291 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Patra, Soumyadip
Bierhorst, Peter
Asymptotically Optimal Adversarial Strategies for the Probability Estimation Framework
title Asymptotically Optimal Adversarial Strategies for the Probability Estimation Framework
title_full Asymptotically Optimal Adversarial Strategies for the Probability Estimation Framework
title_fullStr Asymptotically Optimal Adversarial Strategies for the Probability Estimation Framework
title_full_unstemmed Asymptotically Optimal Adversarial Strategies for the Probability Estimation Framework
title_short Asymptotically Optimal Adversarial Strategies for the Probability Estimation Framework
title_sort asymptotically optimal adversarial strategies for the probability estimation framework
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10667995/
https://www.ncbi.nlm.nih.gov/pubmed/37761589
http://dx.doi.org/10.3390/e25091291
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