Quantum Machine Learning: Exploring the Potential of Quantum Computing for AI Applications

EasyChair Preprint 13896

Abstract

This research explores the integration of quantum computing with machine learning to develop novel AI algorithms that leverage the unique properties of quantum mechanics. Quantum computers, with their ability to perform complex computations exponentially faster than classical computers, hold the potential to revolutionize AI by solving problems that are currently intractable. The study investigates the application of quantum algorithms to enhance machine learning tasks such as data classification, optimization, and pattern recognition. By harnessing quantum superposition, entanglement, and parallelism, quantum machine learning aims to achieve breakthroughs in fields like materials science, drug discovery, and cryptography. This research not only examines the theoretical underpinnings of quantum-enhanced AI but also evaluates practical implementations and the challenges of scaling quantum algorithms for real-world applications. The findings highlight the transformative potential of quantum machine learning in accelerating scientific discovery and developing advanced AI solutions.

Keyphrases: AI algorithms, Cryptography, Data Classification, Optimization, Quantum Machine Learning, drug discovery, entanglement, materials science, parallelism, pattern recognition, quantum computing, quantum mechanics, scientific discovery, superposition

@booklet{EasyChair:13896,
  author    = {Kaledio Potter and Dylan Stilinki},
  title     = {Quantum Machine Learning: Exploring the Potential of Quantum Computing for AI Applications},
  howpublished = {EasyChair Preprint 13896},
  year      = {EasyChair, 2024}}