Version 1
: Received: 7 May 2024 / Approved: 8 May 2024 / Online: 8 May 2024 (12:01:03 CEST)
How to cite:
Khan, K. Quantum Data Science: Leveraging Data Analytics for Advancing Quantum Computing. Preprints2024, 2024050495. https://doi.org/10.20944/preprints202405.0495.v1
Khan, K. Quantum Data Science: Leveraging Data Analytics for Advancing Quantum Computing. Preprints 2024, 2024050495. https://doi.org/10.20944/preprints202405.0495.v1
Khan, K. Quantum Data Science: Leveraging Data Analytics for Advancing Quantum Computing. Preprints2024, 2024050495. https://doi.org/10.20944/preprints202405.0495.v1
APA Style
Khan, K. (2024). Quantum Data Science: Leveraging Data Analytics for Advancing Quantum Computing. Preprints. https://doi.org/10.20944/preprints202405.0495.v1
Chicago/Turabian Style
Khan, K. 2024 "Quantum Data Science: Leveraging Data Analytics for Advancing Quantum Computing" Preprints. https://doi.org/10.20944/preprints202405.0495.v1
Abstract
Quantum computing represents a paradigm shift in computational power, promising exponential speedups for solving certain classes of problems. However, harnessing the full potential of quantum computers requires effective utilization of data science techniques. In this review paper, we explore the intersection of data science and quantum computing, focusing on the role of data analytics in advancing quantum computing applications. We begin with an overview of quantum computing fundamentals, including quantum mechanics principles and quantum algorithms. We then delve into topics such as quantum data representation, manipulation, and machine learning algorithms tailored for quantum computing environments. Additionally, we discuss quantum error correction and noise mitigation strategies essential for reliable quantum computation. Furthermore, we survey the landscape of quantum software development tools and frameworks, highlighting their importance in facilitating quantum algorithm design and optimization. Through case studies and examples, we demonstrate the practical applications of data science techniques in quantum computing, including quantum cryptography and quantum-enhanced data analysis. Finally, we identify future research directions and challenges in the field, emphasizing the need for interdisciplinary collaboration between the data science and quantum computing communities to unlock the full potential of quantum data science.
Keywords
Quantum Computing; Data Science; Quantum Algorithms; Quantum Data Analysis; Quantum Error Correction
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.