Genetic Algorithm-based QAOA Training improved with Fuzzy Clustering

The Quantum Approximate Optimization Algorithm (QAOA) is emerging as a key method in quantum optimization. Operating within the framework of variational quantum circuits, QAOA entails training a parameterized quantum circuit through classical optimizers to effectively address combinatorial optimization problems. In this context, evolutionary algorithms such as genetic algorithms are emerging as suitable methodologies to search the optimal parameters for QAOA circuits. However, unfortunately, the training process results in a computationally intensive task, and, as a consequence, it is crucial to explore innovative strategies to address this issue. This work introduces a novel approach based on Fuzzy C-means algorithm to opportunely initialize the population of the genetic algorithms employed as optimizers for QAOA and, as a consequence, enhance the training. Experimental results demonstrate that the proposed approach significantly improves the performance of genetic algorithms-based training for QAOA.