A Gate-Based Quantum Genetic Algorithm for Real-Valued Global Optimization
This work addresses global optimization problems, potentially benefiting fields like engineering or finance, but it is incremental as it builds on existing quantum and genetic algorithm concepts.
The authors tackled real-valued global optimization by proposing a gate-based Quantum Genetic Algorithm (QGA) that uses quantum circuits to represent individuals, with evolutionary operators acting on circuit structures. They demonstrated that superposition (e.g., using Hadamard gates) consistently improves convergence and robustness, and introducing pairwise inter-individual entanglement accelerates early convergence, showing quantum resources enhance search dynamics.
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.