MKS Publication

Chapter 1

Exploring the Relationship between Quantum Computing and Machine Learning. A Literature Review

Abstract/Preface

This study examines the relationship between machine learning and quantum computing, emphasizing the potential benefits of quantum algorithms for classification, optimization and clustering problems. Through a comprehensive literature review of peer reviewed journal articles and preprints from 2014 to 2024, Quantum K-Means Clustering, Quantum Support Vector Machines (QSVMs), and Quantum Approximate Optimization Algorithms (QAOA) and Quantum Annealing are among the important quantum algorithms identified in the study. Although the theoretical potential of these algorithms is substantial, present hardware constraints, such as noise, de-coherence, and qubit count limitations, make practical implementation difficult. The review also highlights the ongoing challenges in quantum error correction and the nascent stage of quantum hardware development, which prevent large-scale machine learning tasks from being fully realized. Even so, hybrid quantum-classical models are a plausible route forward for near-term utility. These results suggest that to leverage quantum machine learning to its full potential, further progress in quantum hardware, error correction codes, and hybrid algorithms is required. Future studies should focus on designing more robust quantum error correction methods, further developing hybrid systems and exploring new areas of machine learning, such as reinforcement learning and generative models.