Quantum‐Enhanced Genetic Algorithm for Quantum Neural Network Optimization

ABSTRACT

This work proposes a Quantum Enhanced Genetic Algorithm (QEGA) that synergistically integrates three quantum‐inspired operators: a Grover adaptive search‐based selection mechanism, a swap test‐guided crossover scheme, and a quantum walk‐driven mutation process. By harnessing quantum amplitude amplification, entanglement recombination, and probabilistic exploration, QEGA pioneers the integration of quantum computational advantages into evolutionary optimization for quantum neural network parameters. The algorithm enhances global search effectiveness while accelerating convergence and exhibiting robust resistance to local optima. Experimental results on three benchmark datasets, i.e., Iris, the plane point set, and MNIST binary classification, demonstrate that QEGA outperforms classical genetic algorithms and quantum optimization algorithms from recent studies by a substantial margin on classification tasks. Specifically, it attains a peak accuracy of 98.67%, 99.16%, and 98.00% on the three datasets, respectively, accompanied by consistently smooth and monotonic convergence behaviors across the evaluated trials. These findings validate QEGA's potential as a versatile optimization framework for quantum neural network training and highlight the broader promise of quantum‐enhanced evolutionary computation in practical machine learning applications.