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arXiv · 2604.11667

A Comparative Study of Hybrid Quantum and Classical Genetic Algorithms in Portfolio Optimization

Romeu Rossi Junior·José Augusto Miranda Nacif·Leonardo Antônio Mendes Souza·Marcus Henrique Soares Mendes
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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.

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Romeu Rossi Junior, José Augusto Miranda Nacif, Leonardo Antônio Mendes Souza, Marcus Henrique Soares Mendes. 2026-04-13. A Comparative Study of Hybrid Quantum and Classical Genetic Algorithms in Portfolio Optimization. https://arxiv.org/abs/2604.11667

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