TO CENTER OR NOT TO CENTER? HYPERSPECTRAL DATA VS. QUANTUM COVARIANCE MATRICES

Artur Miroszewski; Jakub Nalepa; Agata M. Wijata

doi:10.1109/IGARSS55030.2025.11243597

TO CENTER OR NOT TO CENTER? HYPERSPECTRAL DATA VS. QUANTUM COVARIANCE MATRICES

Artur Miroszewski
, Jakub Nalepa
, Agata M. Wijata

Jagiellonian University in Kraków

Research output: Contribution to journal › Conference article › peer-review

Abstract

We highlight how the L² normalization required for embedding data in quantum states affects data centering, which can significantly influence quantum amplitude-encoded covariance matrices in quantum data analysis algorithms. We examine the spectra and eigenvectors of quantum covariance matrices derived from hyperspectral data under various centering scenarios. Surprisingly, our findings reveal that classification performance in problems reduced by principal component analysis remains unaffected, no matter if the data is centered or uncentered, provided that eigenvector filtering is handled appropriately.

Original language	English
Pages (from-to)	1332-1336
Number of pages	5
Journal	International Geoscience and Remote Sensing Symposium (IGARSS)
DOIs	https://doi.org/10.1109/IGARSS55030.2025.11243597
Publication status	Published - 2025
Event	2025 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2025 - Brisbane, Australia Duration: 3 Aug 2025 → 8 Aug 2025

Keywords

Covariance matrices
Data Analysis
Hyperspectral Imaging
Quantum Machine Learning

ASJC Scopus subject areas

Computer Science Applications
General Earth and Planetary Sciences

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10.1109/IGARSS55030.2025.11243597

Cite this

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title = "TO CENTER OR NOT TO CENTER? HYPERSPECTRAL DATA VS. QUANTUM COVARIANCE MATRICES",

abstract = "We highlight how the L2 normalization required for embedding data in quantum states affects data centering, which can significantly influence quantum amplitude-encoded covariance matrices in quantum data analysis algorithms. We examine the spectra and eigenvectors of quantum covariance matrices derived from hyperspectral data under various centering scenarios. Surprisingly, our findings reveal that classification performance in problems reduced by principal component analysis remains unaffected, no matter if the data is centered or uncentered, provided that eigenvector filtering is handled appropriately.",

keywords = "Covariance matrices, Data Analysis, Hyperspectral Imaging, Quantum Machine Learning",

author = "Artur Miroszewski and Jakub Nalepa and Wijata, \{Agata M.\}",

note = "Publisher Copyright: {\textcopyright}2025 IEEE.; 2025 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2025 ; Conference date: 03-08-2025 Through 08-08-2025",

year = "2025",

doi = "10.1109/IGARSS55030.2025.11243597",

language = "English",

pages = "1332--1336",

journal = "International Geoscience and Remote Sensing Symposium (IGARSS)",

issn = "2153-6996",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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T1 - TO CENTER OR NOT TO CENTER? HYPERSPECTRAL DATA VS. QUANTUM COVARIANCE MATRICES

AU - Miroszewski, Artur

AU - Nalepa, Jakub

AU - Wijata, Agata M.

PY - 2025

Y1 - 2025

N2 - We highlight how the L2 normalization required for embedding data in quantum states affects data centering, which can significantly influence quantum amplitude-encoded covariance matrices in quantum data analysis algorithms. We examine the spectra and eigenvectors of quantum covariance matrices derived from hyperspectral data under various centering scenarios. Surprisingly, our findings reveal that classification performance in problems reduced by principal component analysis remains unaffected, no matter if the data is centered or uncentered, provided that eigenvector filtering is handled appropriately.

AB - We highlight how the L2 normalization required for embedding data in quantum states affects data centering, which can significantly influence quantum amplitude-encoded covariance matrices in quantum data analysis algorithms. We examine the spectra and eigenvectors of quantum covariance matrices derived from hyperspectral data under various centering scenarios. Surprisingly, our findings reveal that classification performance in problems reduced by principal component analysis remains unaffected, no matter if the data is centered or uncentered, provided that eigenvector filtering is handled appropriately.

KW - Covariance matrices

KW - Data Analysis

KW - Hyperspectral Imaging

KW - Quantum Machine Learning

UR - https://www.scopus.com/pages/publications/105033603265

U2 - 10.1109/IGARSS55030.2025.11243597

DO - 10.1109/IGARSS55030.2025.11243597

M3 - Conference article

AN - SCOPUS:105033603265

SN - 2153-6996

SP - 1332

EP - 1336

JO - International Geoscience and Remote Sensing Symposium (IGARSS)

JF - International Geoscience and Remote Sensing Symposium (IGARSS)

T2 - 2025 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2025

Y2 - 3 August 2025 through 8 August 2025

ER -

TO CENTER OR NOT TO CENTER? HYPERSPECTRAL DATA VS. QUANTUM COVARIANCE MATRICES

Abstract

Keywords

ASJC Scopus subject areas

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