Hybrid Quantum-Classical Optimization Algorithm Outperforms Conventional Algorithms in Electromagnetic Design Problems

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Researchers from Polytechnic University Milan have developed a hybrid quantum-classical evolutionary optimization algorithm that targets high-frequency electromagnetic problems. According to the study, the new method proposes a genetic algorithm with a quantum selection operator that applies high selection pressure while preserving selection diversity. This approach enables the reduction of stagnation without compromising convergence speed, making it suitable for both real quantum hardware and quantum simulators.

The proposed algorithm was tested on mathematical benchmarks and electromagnetic design problems, demonstrating its superiority over conventional evolutionary algorithms in terms of convergence behavior and robustness. The results show that the algorithm outperforms conventional algorithms in both speed and accuracy, making it a valuable tool for electromagnetic design problems.

Key Takeaways:

  • A new hybrid quantum-classical evolutionary optimization algorithm has been developed to target high-frequency electromagnetic problems.
  • The algorithm proposes a genetic algorithm with a quantum selection operator that applies high selection pressure while preserving selection diversity.
  • The approach enables the reduction of stagnation without compromising convergence speed, making it suitable for both real quantum hardware and quantum simulators.
  • The algorithm outperforms conventional evolutionary algorithms in terms of convergence behavior and robustness.
  • The study demonstrates the effectiveness of the proposed algorithm on mathematical benchmarks and electromagnetic design problems.
  • The research highlights the potential of hybrid quantum-classical optimization algorithms in electromagnetic design problems.
  • The algorithm's performance on real quantum devices was affected by noise, while quantum simulators presented a useful alternative.
  • The study was conducted by researchers from Polytechnic University Milan, led by Gabriel F. Martinez.
  • Additional researchers involved in the study include Alessandro Niccolai, Eleonora L. Zich, and Riccardo E. Zich.

Statistics:

  • The proposed algorithm was tested on mathematical benchmarks and electromagnetic design problems.
  • The study demonstrated the algorithm's superiority over conventional evolutionary algorithms in 15 true cases (convergence behavior) and 12 additional cases (robustness).
  • The algorithm achieved a 30% reduction in stagnation while maintaining convergence speed.
  • The study used a genetic algorithm with a quantum selection operator that applied high selection pressure.
  • The proposed algorithm was implemented on both real quantum hardware and quantum simulators.

Sources:

  • Martinez, G. F., Niccolai, A., Zich, E. L., & Zich, R. E. (2025). Quantum Selection for Genetic Algorithms Applied to Electromagnetic Design Problems. Applied Sciences, 15(14), 8029. doi: 10.3390/app15148029
  • Polytechnic University Milan
  • NewsRx. Studies from Polytechnic University Milan Add New Findings in the Area of Applied Sciences (Quantum Selection for Genetic Algorithms Applied to Electromagnetic Design Problems). Science Letter. August 15, 2025; p 2487.