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Enhancing Image Classification in Quantum Computing: A Study on Preprocessing Techniques and Qubit Limitations

Year: 2025

Type: conference-paper

Abstract: Quantum algorithms present unique advantages over classical methods but remain constrained by the limited number of qubits in current quantum computers.This limitation hinders their effectiveness in machine learning tasks, such as image classification.Despite its relevance, the impact of these constraints on quantum machine learning remains underexplored.This study addresses this gap by analyzing preprocessing techniques for preparing images on quantum processors.We evaluated 10 dimensionality reduction methods across four standard datasets using three distinct quantum neural network architectures.The results provide valuable insights into optimizing classification efficiency under qubit constraints, paving the way for broader applications of quantum machine learning.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2025.es2025-195

Authors: Henrique Alves Barbosa, Gustavo Augusto Pires, Juliana Assis Alves, Luiz Torres, Janier Arias García, Frederico Gualberto Ferreira Coelho

Institutions: Universidade Federal de Minas Gerais, Universidade Federal de Ouro Preto

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

OpenAlex ID: W4409480871

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