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Development of Evolutionary Systems Based on Quantum Petri Nets

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Listed:
  • Tiberiu Stefan Letia

    (Department of Automation, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania)

  • Elenita Maria Durla-Pasca

    (Department of Automation, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania)

  • Dahlia Al-Janabi

    (Department of Automation, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania)

  • Octavian Petru Cuibus

    (Department of Automation, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania)

Abstract

Evolutionary systems (ES) include software applications that solve problems using heuristic methods instead of the deterministic ones. The classical computing used for ES development involves random methods to improve different kinds of genomes. The mappings of these genomes lead to individuals that correspond to the searched solutions. The individual evaluations by simulations serve for the improvement of their genotypes. Quantum computations, unlike the classical computations, can describe and simulate a large set of individuals simultaneously. This feature is used to diminish the time for finding the solutions. Quantum Petri Nets (QPNs) can model dynamical systems with probabilistic features that make them appropriate for the development of ES. Some examples of ES applications using the QPNs are given to show the benefits of the current approach. The current research solves quantum evolutionary problems using quantum genetic algorithms conceived and improved based on QPN. They were tested on a dynamic system using a Quantum Discrete Controlled Walker (QDCW).

Suggested Citation

  • Tiberiu Stefan Letia & Elenita Maria Durla-Pasca & Dahlia Al-Janabi & Octavian Petru Cuibus, 2022. "Development of Evolutionary Systems Based on Quantum Petri Nets," Mathematics, MDPI, vol. 10(23), pages 1-34, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:23:p:4404-:d:980848
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    References listed on IDEAS

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    1. Ajagekar, Akshay & You, Fengqi, 2019. "Quantum computing for energy systems optimization: Challenges and opportunities," Energy, Elsevier, vol. 179(C), pages 76-89.
    2. Rui Zhang & Zhiteng Wang & Hongjun Zhang, 2014. "Quantum-Inspired Evolutionary Algorithm for Continuous Space Optimization Based on Multiple Chains Encoding Method of Quantum Bits," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-16, July.
    3. Huaixiao Wang & Jianyong Liu & Jun Zhi & Chengqun Fu, 2013. "The Improvement of Quantum Genetic Algorithm and Its Application on Function Optimization," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-10, May.
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    1. Tiberiu Stefan Letia & Camelia Avram & Dahlia Al-Janabi & Ionel Miu & Octavian Cuibus, 2025. "Development of Dynamic System Applications Using Distributed Quantum-Centric Computing," Mathematics, MDPI, vol. 13(19), pages 1-38, October.

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