Analysis of Quantum Image Representations for Supervised Classification

Fuente: arXiv

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Autori principali:	Parigi, Marco, Khosrojerdi, Mehran, Caruso, Filippo, Banchi, Leonardo
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Quantum Physics Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning 81P68, 81P70, 81P40, 68Q12, 68T01 I.2; I.4; J.2
Accesso online:	Acceder al recurso
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author	Parigi, Marco Khosrojerdi, Mehran Caruso, Filippo Banchi, Leonardo
author_facet	Parigi, Marco Khosrojerdi, Mehran Caruso, Filippo Banchi, Leonardo
contents	In the era of big data and artificial intelligence, the increasing volume of data and the demand to solve more and more complex computational challenges are two driving forces for improving the efficiency of data storage, processing and analysis. Quantum image processing (QIP) is an interdisciplinary field between quantum information science and image processing, which has the potential to alleviate some of these challenges by leveraging the power of quantum computing. In this work, we compare and examine the compression properties of four different Quantum Image Representations (QImRs): namely, Tensor Network Representation (TNR), Flexible Representation of Quantum Image (FRQI), Novel Enhanced Quantum Representation NEQR, and Quantum Probability Image Encoding (QPIE). Our simulations show that FRQI and QPIE perform a higher compression of image information than TNR and NEQR. Furthermore, we investigate the trade-off between accuracy and memory in binary classification problems, evaluating the performance of quantum kernels based on QImRs compared to the classical linear kernel. Our results indicate that quantum kernels provide comparable classification average accuracy but require exponentially fewer resources for image storage.
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spellingShingle	Analysis of Quantum Image Representations for Supervised Classification Parigi, Marco Khosrojerdi, Mehran Caruso, Filippo Banchi, Leonardo Quantum Physics Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning 81P68, 81P70, 81P40, 68Q12, 68T01 I.2; I.4; J.2 In the era of big data and artificial intelligence, the increasing volume of data and the demand to solve more and more complex computational challenges are two driving forces for improving the efficiency of data storage, processing and analysis. Quantum image processing (QIP) is an interdisciplinary field between quantum information science and image processing, which has the potential to alleviate some of these challenges by leveraging the power of quantum computing. In this work, we compare and examine the compression properties of four different Quantum Image Representations (QImRs): namely, Tensor Network Representation (TNR), Flexible Representation of Quantum Image (FRQI), Novel Enhanced Quantum Representation NEQR, and Quantum Probability Image Encoding (QPIE). Our simulations show that FRQI and QPIE perform a higher compression of image information than TNR and NEQR. Furthermore, we investigate the trade-off between accuracy and memory in binary classification problems, evaluating the performance of quantum kernels based on QImRs compared to the classical linear kernel. Our results indicate that quantum kernels provide comparable classification average accuracy but require exponentially fewer resources for image storage.
title	Analysis of Quantum Image Representations for Supervised Classification
topic	Quantum Physics Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning 81P68, 81P70, 81P40, 68Q12, 68T01 I.2; I.4; J.2
url	https://arxiv.org/abs/2507.22039

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