Application of Quantum Machine Learning Algorithms in Financial Sciences

Document Type : Promotional article

Authors

1 Amirkabir University of Technology

2 Imam Reza International University

3 Department of Management, Faculty of Humanities and Social Sciences, Golestan University, Gorgan, Iran

Abstract

Machine learning is a set of algorithms that make it possible for a computer to learn statistical patterns in existing data without explicit programming. Machine learning has many applications in financial services such as fraud detection, automated portfolio management, estimating transaction risk levels, and more. But since most of these methods require large amounts of data, performing these calculations on classical computers requires a large amount of computational time and resources. Because of their parallel processing power, quantum computers can solve certain computational problems much faster than their classical counterpart algorithms. Therefore, it is possible to increase the quantum speed in machine learning algorithms. This article examines the impact of quantum machine learning on financial science. Specifically, the present study has tried to identify those computational problems in the field of financial sciences that the use of quantum machine learning method is superior to the best classical algorithms. The feasibility of physical realization of these methods in the short term has also been investigated.

Keywords

References

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History

  • Receive Date: 07 November 2021
  • Revise Date: 07 January 2022
  • Accept Date: 10 January 2022

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