Integration of Quantum Computing with Machine Learning Algorithms
Author(s): K.Mahesh Kumar Roy1 , H.Biswajith Singh2
Affiliation: 1,2Department Electronic and Telecommunications 1,2,Jorhat Institute of Science and Technology, Jorhat, India
Page No: 7-9-
Volume issue & Publishing Year: Volume 1 Issue 3,Aug-2024
Journal: International Journal of Modern Engineering and Management | IJMEM
ISSN NO: 3048-8230
DOI:
Abstract:
Quantum computing promises significant advancements in computational capabilities, with the potential to revolutionize various domains, including machine learning (ML). This paper explores the integration of quantum computing with machine learning algorithms, focusing on how quantum technologies can enhance ML tasks such as optimization, pattern recognition, and data classification. We examine current research, theoretical frameworks, and practical implementations of quantum-enhanced ML techniques. Through a detailed analysis of quantum algorithms and their application to ML problems, we assess the potential benefits, challenges, and future directions for this interdisciplinary field. Our findings indicate that while quantum computing offers promising improvements in computational efficiency, several technical and theoretical challenges must be addressed to fully realize its potential in ML.
Keywords:
Quantum Computing, Machine Learning, Quantum Algorithms, Optimization, Data Classification, Quantum-Enhanced ML
Reference:
- Nielsen, M. A., & Chuang, I. L. (2010). Quantum Computation and Quantum Information. Cambridge University Press.
- Lloyd, S. (2013). Quantum algorithms for fixed-point arithmetic and machine learning. arXiv
/1306.1810.
- Biamonte, J., & Wittek, P. (2017). Quantum machine learning. Nature, 549(7671), 195-202.
- Farhi, E., & Gutmann, S. (2014). An overview of quantum computing. arXiv
/0012141.
- Google AI Quantum and collaborators. (2020). Quantum supremacy using a programmable superconducting processor. Nature, 574(7779), 505-510.