IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i23p4404-d980848.html
   My bibliography  Save this article

Development of Evolutionary Systems Based on Quantum Petri Nets

Author

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/23/4404/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/23/4404/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ajagekar, Akshay & You, Fengqi, 2019. "Quantum computing for energy systems optimization: Challenges and opportunities," Energy, Elsevier, vol. 179(C), pages 76-89.
    2. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rafał Różycki & Joanna Józefowska & Krzysztof Kurowski & Tomasz Lemański & Tomasz Pecyna & Marek Subocz & Grzegorz Waligóra, 2022. "A Quantum Approach to the Problem of Charging Electric Cars on a Motorway," Energies, MDPI, vol. 16(1), pages 1-20, December.
    2. Klemeš, Jiří Jaromír & Wang, Qiu-Wang & Varbanov, Petar Sabev & Zeng, Min & Chin, Hon Huin & Lal, Nathan Sanjay & Li, Nian-Qi & Wang, Bohong & Wang, Xue-Chao & Walmsley, Timothy Gordon, 2020. "Heat transfer enhancement, intensification and optimisation in heat exchanger network retrofit and operation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
    3. Ajagekar, Akshay & You, Fengqi, 2024. "Variational quantum circuit based demand response in buildings leveraging a hybrid quantum-classical strategy," Applied Energy, Elsevier, vol. 364(C).
    4. Ahmed Al-Shafei & Hamidreza Zareipour & Yankai Cao, 2022. "High-Performance and Parallel Computing Techniques Review: Applications, Challenges and Potentials to Support Net-Zero Transition of Future Grids," Energies, MDPI, vol. 15(22), pages 1-58, November.
    5. Lyu, Wenjing & Liu, Jin, 2021. "Artificial Intelligence and emerging digital technologies in the energy sector," Applied Energy, Elsevier, vol. 303(C).
    6. Ajagekar, Akshay & You, Fengqi, 2022. "Quantum computing and quantum artificial intelligence for renewable and sustainable energy: A emerging prospect towards climate neutrality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    7. Olatunji, Kehinde O. & Ahmed, Noor A. & Madyira, Daniel M. & Adebayo, Ademola O. & Ogunkunle, Oyetola & Adeleke, Oluwatobi, 2022. "Performance evaluation of ANFIS and RSM modeling in predicting biogas and methane yields from Arachis hypogea shells pretreated with size reduction," Renewable Energy, Elsevier, vol. 189(C), pages 288-303.
    8. Deng, Zhipeng & Wang, Xuezheng & Dong, Bing, 2023. "Quantum computing for future real-time building HVAC controls," Applied Energy, Elsevier, vol. 334(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:23:p:4404-:d:980848. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.