Exploring the Quantum Frontier: a Thorough Examination of Quantum Machine Learning Techniques and Applications

EasyChair Preprint 11961

Abstract

In this comprehensive review, we delve into the burgeoning realm of Quantum Machine Learning (QML), examining a spectrum of techniques and applications at the intersection of quantum computing and classical machine learning. Our exploration navigates through the foundational principles of quantum mechanics, elucidating how they intertwine with machine learning algorithms to usher in a new era of computational capabilities. We scrutinize prominent QML models, addressing their advantages, challenges, and potential breakthroughs. Furthermore, we survey diverse applications of QML across domains such as optimization, pattern recognition, and artificial intelligence, providing insights into the transformative potential of quantum computing in shaping the future of machine learning.

Keyphrases: Artificial Intelligence, Computational Capabilities, Learning Algorithms, Optimization, Quantum Machine Learning, machine, pattern, quantum computing, quantum mechanics, quantum models, recognition

@booklet{EasyChair:11961,
  author    = {Asad Ali and Wydah Theorbo},
  title     = {Exploring the Quantum Frontier: a Thorough Examination of Quantum Machine Learning Techniques and Applications},
  howpublished = {EasyChair Preprint 11961},
  year      = {EasyChair, 2024}}