On the Classical Shadow Nonparametric Bootstrap

Fuente: arXiv
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Main Authors: Ghysels, Eric, Morgan, Jack
Format: Preprint
Published: 2025
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author Ghysels, Eric
Morgan, Jack
author_facet Ghysels, Eric
Morgan, Jack
contents Classical shadows are an efficient method for constructing an approximate classical description of a quantum state using very few measurements. In the paper we propose to enhance classical shadow methods using bootstrap resampling methods. We apply nonparametric bootstrapping to assess the variability and accuracy of estimators by repeatedly sampling with replacement from the observed data, i.e. in our case the classical shadow measurements. We show that the bootstrap distributions are very different from the Gaussian approximations. Likewise, the theoretical error bounds are not tight compared to the bootstrap percentiles. Finally, we suggest using resampling tools to make risk assessments.
format Preprint
id arxiv_https___arxiv_org_abs_2511_09793
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle On the Classical Shadow Nonparametric Bootstrap
Ghysels, Eric
Morgan, Jack
Quantum Physics
Classical shadows are an efficient method for constructing an approximate classical description of a quantum state using very few measurements. In the paper we propose to enhance classical shadow methods using bootstrap resampling methods. We apply nonparametric bootstrapping to assess the variability and accuracy of estimators by repeatedly sampling with replacement from the observed data, i.e. in our case the classical shadow measurements. We show that the bootstrap distributions are very different from the Gaussian approximations. Likewise, the theoretical error bounds are not tight compared to the bootstrap percentiles. Finally, we suggest using resampling tools to make risk assessments.
title On the Classical Shadow Nonparametric Bootstrap
topic Quantum Physics
url https://arxiv.org/abs/2511.09793