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Bibliographic Details
Main Author: Ying, Lexing
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2405.00803
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author Ying, Lexing
author_facet Ying, Lexing
contents Spectral estimation is a fundamental task in signal processing. Recent algorithms in quantum phase estimation are concerned with the large noise, large frequency regime of the spectral estimation problem. The recent work in Ding-Epperly-Lin-Zhang shows that the ESPRIT algorithm exhibits superconvergence behavior for the spike locations in terms of the maximum frequency. This note provides a perturbative analysis to explain this behavior. It also extends the discussion to the case where the noise grows with the sampling frequency.
format Preprint
id arxiv_https___arxiv_org_abs_2405_00803
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle A perturbative analysis for noisy spectral estimation
Ying, Lexing
Numerical Analysis
Spectral estimation is a fundamental task in signal processing. Recent algorithms in quantum phase estimation are concerned with the large noise, large frequency regime of the spectral estimation problem. The recent work in Ding-Epperly-Lin-Zhang shows that the ESPRIT algorithm exhibits superconvergence behavior for the spike locations in terms of the maximum frequency. This note provides a perturbative analysis to explain this behavior. It also extends the discussion to the case where the noise grows with the sampling frequency.
title A perturbative analysis for noisy spectral estimation
topic Numerical Analysis
url https://arxiv.org/abs/2405.00803