Quantum-aided feature selection model – A quantum machine learning approach

Abstract

The accuracy of information retrieval systems is measured by the relevancy of retrieved results as per the user’s query. Relevant results are presented by performing various methods viz. indexing and crawling and the output of these processes is the retrieved results that have to pass through the ranking process which is the central goal of information retrieval systems. The ranking is carried out through the classification or clustering of processed results which can include redundant and noisy features. The accuracy of classification or clusters for the ranking process can be maximized by removing noisy and duplicate features through the feature selection method. Although feature selection is an expensive computational process, after many decades, quantum computation tools are in use for many algorithms to implement realistic problems, particularly in the standard of Quantum Annealing. This paper focuses to prospect the standard of quantum computing in order to increase the quality of information classes through the feature selection method. The persuasiveness of the quantum approach is comparable to the classical process that focused on the reliability of quantum methodology from different perspectives.