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Engineering Applications of Artificial Intelligence

Genetic algorithm-enhanced neural network for parameter optimization of quantum key distribution

Haonan Ying, Jihao Fan

Published Oct 1, 2026• 0 citations
Publisher
DOI
10.1016/j.engappai.2026.116395

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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.
A quantum network connecting quantum devices is expected to enhance quantum computing and information processing by increasing the number of qubits, and distributing computation and processing. However, noise-induced decoherence remains a big challenge as a quantum network can experience multiple sources of noises. Optimizing a quantum network for a given noise regime thus can improve its performance and reliability. This research uses multi-objective optimization with a genetic algorithm to find optimal conditions for constructing a quantum network in specific configurations. We employed the qwanta quantum network simulator to investigate two scenarios of a three-node quantum network with a quantum repeater in the middle. Pareto frontier analysis enables us to visualize the trade-offs between a quantum state fidelity and throughput in a quantum network. The results provide parameters that achieve the fidelity higher than the targeted value of 0.828 for quantum key distribution in a linear network with equal distances between nodes. However, for a three-nodes network with unequal distances based on Thailand's geographical locations, the highest fidelity achieved is 0.706. Expanding a genetic algorithm's search space to include a broader parameter range can potentially improve the results. This research demonstrates potential deployment of a genetic algorithm with multiple-objective optimization in a quantum network.