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

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.