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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·April 13, 2026
Quantum Physics
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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.