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The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Optimal resource allocation for flexible-grid entanglement distribution networks.

Jude Alnas, Muneer Alshowkan, Nageswara S V Rao

    Optics Express
    |October 14, 2022
    PubMed
    Summary

    We developed a genetic algorithm (GA) to optimize entangled photon spectrum provisioning in quantum networks. This approach aids in designing efficient large-scale entanglement distribution systems.

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    Area of Science:

    • Quantum Information Science
    • Network Engineering
    • Computational Optimization

    Background:

    • Quantum networks require efficient distribution of entanglement for advanced applications.
    • Designing optimal resource allocation in flex-grid quantum networks is complex.
    • Hyperentangled biphotons offer a robust method for entanglement distribution.

    Purpose of the Study:

    • To develop and apply a genetic algorithm (GA) for optimizing entangled photon spectrum provisioning in flex-grid quantum networks.
    • To establish a general model for entanglement distribution and derive performance bounds.
    • To experimentally validate the proposed model in a deployed quantum local area network.

    Main Methods:

    • Modeling entanglement distribution using frequency-polarization hyperentangled biphotons.
    • Deriving upper bounds on quantum network fidelity and entangled bit rate.
    • Applying a genetic algorithm (GA) for optimal resource allocation in various network scenarios.
    • Experimental validation in a deployed fiber quantum local area network.

    Main Results:

    • The genetic algorithm successfully identified optimal resource allocations for different network configurations.
    • Derived conditions based on detector quality and link efficiency determine entanglement feasibility.
    • Experimental validation confirmed the model's predictions in a real-world quantum local area network.
    • The study demonstrates the GA's effectiveness in designing large-scale entanglement distribution networks.

    Conclusions:

    • A genetic algorithm (GA) is a powerful tool for optimizing entangled photon spectrum provisioning in quantum networks.
    • The developed model provides a framework for assessing entanglement feasibility and performance.
    • The findings support the rapid design and deployment of scalable quantum networks for future applications.