The Use of Quantum Algorithms vs. Classical Algorithms in Machine Learning

• Dr. Elina Saarikoski

  Faculty of Information Systems, Aurora Nordic University, Finland

  Author

Through the utilization of quantum phenomena to carry out computations that are beyond the capabilities of classical computers, quantum computing has the potential to revolutionize a variety of industries, including machine learning. In this research, we give a comparative study of conventional and quantum algorithms in the context of machine learning, and we compare and contrast the two types of algorithms. The advantages of quantum algorithms, such as quantum parallelism and entanglement, as well as their potential to outperform classical algorithms in tasks such as classification, clustering, and optimization, are investigated in this article. We explain the merits and limitations of quantum machine learning algorithms by means of theoretical analysis and practical tests. We also emphasize the possibility of these algorithms to achieve quantum advantage in certain scenarios. In addition, we examine the difficulties and opportunities associated with incorporating quantum computing into machine learning pipelines, and we describe potential future research objectives in this rapidly developing subject.

• PDF

Copyright (c) 2026 Package Printing

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

All articles published are made available under the Creative Commons Attribution–NonCommercial 4.0 International (CC BY-NC 4.0) licence.