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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-10667995 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT patrasoumyadip asymptoticallyoptimaladversarialstrategiesfortheprobabilityestimationframework AT bierhorstpeter asymptoticallyoptimaladversarialstrategiesfortheprobabilityestimationframework |