Genetic AI Algorithms for Multi-Objective Optimization in Quantum Systems
Keywords:
Quantum Optimization, Multi‐Objective Genetic Algorithms, NSGA‐II, Quantum Circuit Synthesis, Pareto OptimizationAbstract
Multi‐objective optimization in quantum systems poses formidable challenges stemming from the high dimensionality, complex constraints, and conflicting performance metrics inherent to quantum control tasks and circuit synthesis. This study presents a comprehensive exploration of genetic AI algorithms—evolutionary strategies inspired by natural selection—tailored specifically for multi‐objective optimization in quantum computing contexts. We propose a hybrid framework that synergistically combines the elitist, Pareto‐based selection mechanics of NSGA-II with quantum‐inspired crossover and mutation operators designed to preserve entanglement structures and exploit hardware‐specific gate sets. In this framework, candidate solutions encoding circuit topologies or control‐pulse parameters undergo iterative evolution, guided by fitness vectors that balance gate fidelity, circuit depth, and resource overhead. To benchmark performance, we apply the algorithm to canonical quantum computing problems: Fourier transform circuits, QAOA ansätze for MaxCut instances, and random Clifford+T circuits. Through extensive simulation on noise-modelled quantum devices, we evaluate Pareto front coverage, convergence speed, diversity maintenance, and robustness under decoherence. Our results demonstrate that the proposed genetic AI algorithm outperforms classical single‐objective genetic methods and established multi‐objective evolutionary algorithms (e.g., SPEA2) by achieving broader Pareto fronts and faster convergence rates, while maintaining solution diversity. Furthermore, noise-resilience experiments show graceful degradation, indicating practical applicability on near-term quantum hardware. These findings substantiate the potential of evolutionary computation paradigms—augmented with quantum‐inspired operators—to address the intricate trade-off surfaces that define NISQ-era optimization problems, paving the way toward automated quantum circuit design and control parameter tuning for real-world quantum computing applications.
Downloads
References
• Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197. Michigan State University
• Zitzler, E., Laumanns, M., & Thiele, L. (2001). SPEA2: Improving the strength Pareto evolutionary algorithm for multiobjective optimization. International Center for Numerical Methods in Engineering (CIMNE), 95–100. Michigan State University
• Zitzler, E., & Thiele, L. (1999). Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation, 3(4), 257–271. Michigan State University
• Zhang, Q., & Li, H. (2007). MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation, 11(6), 712–731. Michigan State University
• Chao, B., & Qian, C. (2022). Running time analysis of the non‐dominated sorting genetic algorithm II (NSGA-II) using binary or stochastic tournament selection. arXiv preprint arXiv:2203.11550. arXiv
• Dang, D.-C., Opris, A., Salehi, B., & Sudholt, D. (2023). Analysing the robustness of NSGA-II under noise. arXiv preprint arXiv:2306.04525. arXiv
• Doerr, B., Korkotashvili, D., & Krejca, M. S. (2024). Difficulties of the NSGA-II with the many‐objective LeadingOnes problem. arXiv preprint arXiv:2411.10017. arXiv
• King, J., Mohseni, M., Bernoudy, W., Fréchette, A., Sadeghi, H., Isakov, S. V., Neven, H., & Amin, M. H. (2019). Quantum‐assisted genetic algorithm. arXiv preprint arXiv:1907.00707. arXiv
• Sünkel, L., Maryniuk, D., Mattern, D., Jung, J., & Others. (2023). GA4QCO: Genetic algorithm for quantum circuit optimization. arXiv preprint arXiv:2302.01303. arXivarXiv
• Kölle, M., Bintener, T., Zorn, M., Stenzel, G., Sünkel, L., Gabor, T., & Linnhoff‐Popien, C. (2025). Evaluating mutation techniques in genetic algorithm‐based quantum circuit synthesis. arXiv preprint arXiv:2504.06413. arXiv
• Haghighi, M. K., Fortin-Deschênes, M., Pere, C., & Camus, M. (2025). EAQGA: A quantum‐enhanced genetic algorithm with novel entanglement‐aware crossovers. arXiv preprint arXiv:2504.17923. arXiv
• Nowotniak, R., & Kucharski, J. (2014). Higher‐order quantum‐inspired genetic algorithms. arXiv preprint arXiv:1407.0977. arXiv
• Bracken, A., & Browne, D. E. (2022). Quantum circuit compilation by genetic algorithm for quantum approximation optimization algorithms. ScienceDirect. ScienceDirect
• Lacroix, N., Hellings, C., Andersen, C. K., Di Paolo, A., & Remm, A. (2020). Improving the performance of deep quantum optimization algorithms with continuous gate sets. PRX Quantum, 1(4), 040320. Wikipedia
• Wu, L.-A., Zanardi, P., & Lidar, D. A. (2005). Holonomic quantum computation in decoherence‐free subspaces. Physical Review Letters, 95(13), 130501. Wikipedia
• Srinivas, N., & Deb, K. (1994). Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation, 2(3), 221–248. Wikipedia
• Fonseca, C. M., & Fleming, P. J. (1993). Genetic algorithms for multiobjective optimization: Formulation, discussion and generalisation. In Proceedings of the 5th International Conference on Genetic Algorithms (pp. 416–423). Morgan Kaufmann Publishers.
• Nielsen, M. A., & Chuang, I. L. (2010). Quantum computation and quantum information (10th anniversary ed.). Cambridge University Press.
• Hybrid quantum search with genetic algorithm optimization. (2007). PMC11639129. pmc.ncbi.nlm.nih.gov
Downloads
Published
Issue
Section
License
Copyright (c) 2026 World Journal of Future Technologies in Computer Science and Engineering
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
