Quantum machine learning: Classifications, challenges, and solutions

Abstract

Recently, research at the intersection of quantum mechanics and machine learning has gained attention. This interdisciplinary field aims to tackle the computational efficiency of machine learning by leveraging quantum computing and to derive novel machine learning algorithms inspired by quantum principles. Despite substantial progress in quantum science research, several challenges persist, including the preservation of quantum coherence, mitigation of environmental constraints, advancing quantum computer development, and formulating comprehensive quantum machine learning algorithms. To date, a comprehensive theoretical framework for quantum machine learning is lacking, with much of the research still in the exploratory and experimental stages. This study conducts a thorough survey on quantum machine learning, with the aim of classifying quantum machine learning algorithms while addressing the existing challenges and potential solutions in this emerging field.