Quantum computing applications, challenges, and prospects in financial portfolio optimization

Omoshola S. Owolabi 1, *, Prince C. Uche 1, Nathaniel T. Adeniken 1, Victoria Tanoh 2 and Oluwabukola G. Emi-Johnson 3

1 Department of Data Science, Carolina University, Winston Salem - North Carolina, USA.
2 Applied Science and Technology Program, NC A&T State University, Greensboro, North Carolina, USA.
3 Department of Statistical Sciences, Wake Forest University, North Carolina, USA.
Review Article
World Journal of Advanced Research and Reviews, 2024, 22(03), 014–022
Article DOI: 10.30574/wjarr.2024.22.3.1648
Publication history: 
Received on 14 April 2024 revised on 26 May 2024; accepted on 29 May 2024
Quantum computing has the potential to improve financial portfolio optimization by addressing scalability and computational complexity issues. This article explores the application of quantum algorithms to portfolio optimization. It begins by discussing the limitations of classical optimization methods and introduces the basics of quantum computing. Two key quantum algorithms, quantum annealing and the Quantum Approximate Optimization Algorithm (QAOA), are presented in detail. These algorithms are applied to solve the Quadratic Unconstrained Binary Optimization (QUBO) formulation of the portfolio optimization problem. The article provides a high-level quantum algorithm, along with its pseudo-code Python implementation. The potential computational speedup of quantum algorithms is analyzed, highlighting the theoretical quadratic speedup over classical methods. However, the article also acknowledges the challenges and limitations currently facing quantum computing. It also concludes by emphasizing the promising future of quantum computing in finance and encourages further research to unlock the full potential of quantum technologies in portfolio optimization and other complex financial problems.
Quantum; Portfolio; Optimization; Annealing; Binary; Computational Complexity; Algorithms
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