Quantum Machine Learning Algorithms for Unsupervised Learning

EasyChair Preprint 14858

Abstract

Quantum machine learning (QML) is an emerging field that integrates quantum computing with machine learning, promising to revolutionize traditional computational tasks by leveraging quantum parallelism and entanglement. In unsupervised learning, the goal is to uncover hidden patterns or structures in unlabeled data. This paper explores the application of quantum algorithms to enhance unsupervised learning tasks, such as clustering, dimensionality reduction, and anomaly detection. We discuss key quantum algorithms, including the Quantum Principal Component Analysis (QPCA) and Quantum k-Means, and their potential to offer exponential speedup over classical counterparts. The study highlights the current challenges in implementing QML, including noise in quantum hardware and scalability issues, while presenting promising research avenues for optimizing unsupervised learning models through quantum computing advancements.

Keyphrases: Algorithms, machine learning, quantum, unsupervised learning

@booklet{EasyChair:14858,
  author    = {Favour Olaoye and Kaledio Potter},
  title     = {Quantum Machine Learning Algorithms for Unsupervised Learning},
  howpublished = {EasyChair Preprint 14858},
  year      = {EasyChair, 2024}}