A Gate-Based Quantum Genetic Algorithm for Real-Valued Global Optimization

TL;DR

This paper introduces a novel gate-based quantum genetic algorithm that leverages quantum circuits, superposition, and entanglement to improve global optimization performance over classical methods.

Contribution

It presents a new quantum genetic algorithm framework using gate-based circuits with adaptive structures and quantum resources, demonstrating enhanced optimization capabilities.

Findings

Superposition improves convergence and robustness.

Entanglement accelerates early convergence.

Quantum resources outperform classical counterparts.

Abstract

We propose a gate-based Quantum Genetic Algorithm (QGA) for real-valued global optimization. In this model, individuals are represented by quantum circuits whose measurement outcomes are decoded into real-valued vectors through binary discretization. Evolutionary operators act directly on circuit structures, allowing mutation and crossover to explore the space of gate-based encodings. Both fixed-depth and variable-depth variants are introduced, enabling either uniform circuit complexity or adaptive structural evolution. Fitness is evaluated through quantum sampling, using the mean decoded output of measurement outcomes as the argument of the objective function. To isolate the impact of quantum resources, we compare gate sets with and without the Hadamard gate, showing that superposition consistently improves convergence and robustness across benchmark functions such as the Rastrigin function. Furthermore, we demonstrate that introducing pairwise inter-individual entanglement in the population accelerates early convergence, revealing that quantum correlations among individuals provide an additional optimization advantage. Together, these results show that both superposition and entanglement enhance the search dynamics of evolutionary quantum algorithms, establishing gate-based QGAs as a promising framework for quantum-enhanced global optimization.