Quantum Computing Integration with Data Science: Opportunities and Challenges in Big Data Analytics

EasyChair Preprint 15385

Abstract

Quantum computing presents revolutionary opportunities for big data analytics, promising substantial computational speed-ups for complex data science tasks. This paper explores the potential of quantum computing to transform big data analytics by addressing challenges such as data dimensionality, processing speed, and computational efficiency. We examine quantum algorithms like quantum machine learning and quantum-enhanced optimization, providing insights into practical applications in data-intensive fields. Case studies highlight quantum computing’s role in accelerating data processing tasks, while challenges such as error rates, algorithm compatibility, and quantum hardware limitations are also discussed.

Keyphrases: Big Data Analytics, Data Science, Optimization, Quantum Machine Learning, computational efficiency, quantum algorithms, quantum computing

@booklet{EasyChair:15385,
  author    = {Liam O'Connor},
  title     = {Quantum Computing Integration with Data Science: Opportunities and Challenges in Big Data Analytics},
  howpublished = {EasyChair Preprint 15385},
  year      = {EasyChair, 2024}}