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

TL;DR

This paper compares a hybrid quantum-classical genetic algorithm to a classical one for portfolio optimization, showing faster convergence and fewer evaluations needed by the quantum approach.

Contribution

It introduces and evaluates a Hybrid Quantum Genetic Algorithm, demonstrating its advantages over classical algorithms in portfolio optimization.

Findings

HQGA converges faster to the optimal solution.

HQGA maintains higher population diversity.

HQGA requires fewer evaluations than brute-force methods.

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.