A Comparative Study of Hybrid Quantum and Classical Genetic Algorithms in Portfolio Optimization
Quantum Physics
2026-04-14 v1
Abstract
This work investigates the performance of a Hybrid Quantum Genetic Algorithm (HQGA) compared to a classical Genetic Algorithm (GA) for solving the portfolio optimization problem. Our results indicate that the HQGA converges faster to the optimal solution than its classical counterpart, while also maintaining a higher level of population diversity throughout the optimization process. In addition, the HQGA requires significantly fewer evaluations-to-solution than a brute-force approach to reach the global optimum.
Keywords
Cite
@article{arxiv.2604.11667,
title = {A Comparative Study of Hybrid Quantum and Classical Genetic Algorithms in Portfolio Optimization},
author = {Romeu Rossi Junior and José Augusto Miranda Nacif and Leonardo Antônio Mendes Souza and Marcus Henrique Soares Mendes},
journal= {arXiv preprint arXiv:2604.11667},
year = {2026}
}