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Evolutionary Optimization-Based Design of LQG Controllers in Quantum Coherent Feedback.

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    A new differential evolution (DE) algorithm optimizes linear-quadratic-Gaussian (LQG) controllers for quantum systems. This method enhances performance and ensures physical realizability for quantum applications.

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    Area of Science:

    • Quantum Control Engineering
    • Quantum Systems Optimization
    • Advanced Algorithm Design

    Background:

    • Designing controllers for quantum systems presents unique challenges due to physical realizability constraints.
    • Existing control design methods may not adequately address the specific requirements of quantum systems, impacting performance and practicality.
    • Linear-Quadratic-Gaussian (LQG) control is a powerful framework, but its application in quantum domains requires specialized adaptation.

    Purpose of the Study:

    • To develop a novel differential evolution (DE) algorithm tailored for designing LQG controllers in quantum systems.
    • To enhance the exploration and exploitation capabilities of DE while ensuring physical realizability for quantum applications.
    • To demonstrate the effectiveness of the proposed algorithm in optimizing controllers for a quantum optical system.

    Main Methods:

    • A modified differential evolution (DE) algorithm incorporating relaxed feasibility rules, a scheduled penalty function, adaptive search range adjustment, and a 'bet-and-run' initialization strategy.
    • Application of the DE algorithm to design three distinct LQG controllers for a quantum optical system with varying configurations.
    • Performance evaluation based on LQG performance indices and verification of physical realizability constraints.

    Main Results:

    • The proposed DE algorithm successfully designed LQG controllers for a quantum optical system.
    • The designed controllers achieved superior performance, indicated by lower LQG performance indices compared to existing methods.
    • All designed controllers satisfied the physical realizability constraints, ensuring compatibility with practical quantum platforms.

    Conclusions:

    • The specialized DE algorithm is effective for designing high-performance, physically realizable LQG controllers for quantum systems.
    • The enhanced DE framework offers a promising approach for performance optimization in linear quantum systems with feasibility constraints.
    • This methodology has broad potential for application across various quantum control and optimization tasks.