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Quantum Optimization Quantum Machine Learning

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

arXiv
Authors: Romeu Rossi Junior, José Augusto Miranda Nacif, Leonardo Antônio Mendes Souza, Marcus Henrique Soares Mendes

Year

2026

Paper ID

48913

Status

Preprint

Abstract Read

~2 min

Abstract Words

71

Citations

N/A

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.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2026 reference point for readers tracking recent quantum research.
  • 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.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2604.11667 [2] arXiv https://arxiv.org/abs/2604.11667 [3] Publisher https://arxiv.org/abs/2604.11667

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Current Paper #48913 #75729 How Quantum Is the Advantage? A...

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