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The Journal of Chemical Physics

Quantum control design via adaptive tracking

Wusheng Zhu, HERSCHEL A. RABITZ

Published Aug 1, 2003• 71 citations
Publisher

Abstract

An adaptive tracking algorithm is developed to achieve quantum system control field designs. The adaptive algorithm has the advantage of operating noniteratively to efficiently find desirable controls, and has the feature of high stability by suppressing the influence of disturbances from tracking singularities. The core of the adaptive tracking control algorithm is a self-learning track switch technique which is triggered by monitoring of the evolving system trajectory. The adaptive tracking algorithm is successfully tested for population transfer.

DOI
10.1063/1.1582847

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An adaptive tracking algorithm is developed to achieve quantum system control field designs. The adaptive algorithm has the advantage of operating noniteratively to efficiently find desirable controls, and has the feature of high stability by suppressing the influence of disturbances from tracking singularities. The core of the adaptive tracking control algorithm is a self-learning track switch technique which is triggered by monitoring of the evolving system trajectory. The adaptive tracking algorithm is successfully tested for population transfer.
A control strategy for tracking the trajectory of a time-variant objective function in closed quantum systems is proposed in this paper. The Lyapunov stability theorem is used to design the control law. Meanwhile, the issue of singularity is discussed and the problem of large value of control field occurred during the process of tracking are solved by using an adaptive regulation algorithm of objective function. Simulation results demonstrate the effectiveness and feasibility of the method proposed. We also explore the relationship of effect between control parameters and the error of the control system.
Based on model reference adaptive control theory, we design a control law for a given closed quantum system model by means of the Lyapunov stability theorem by choosing a time-varying objective function as the reference model. Meanwhile, the problems of singularities and a very large value of the control fields during the tracking are solved by using an adaptive tracking algorithm. The simulation experiments realize the tracking task from the arbitrary initial states to the time-varying objective function. Under the effect of control law, the state of control system can track from the arbitrary initial state to the state of objective system. Simulation results demonstrate the ability of the control method proposed to adaptively adjust the control laws and real-time track dynamic target system.
This paper proposes and analyzes an adaptive quantum feedback control system designed to optimize the performance of quantum devices. The core of the system lies in a real-time monitoring and adjustment mechanism utilizing a quantum sensor to measure the state of the quantum system. This measurement data is then fed back into a control algorithm, which dynamically adjusts the system parameters to achieve precise control. Unlike traditional control systems that are sensitive to parameter uncertainties, this adaptive approach leverages the inherent properties of quantum systems to enhance stability and performance. The theoretical framework presented here establishes a foundation for future research and development in adaptive quantum control, addressing key challenges in quantum device operation and manipulation. The system aims to provide a robust and flexible solution for controlling quantum systems with varying characteristics. The primary focus is on the control loop design and the feedback algorithm implementation, with an emphasis on achieving optimal performance under conditions of parameter uncertainty. This work contributes to the growing field of quantum control by introducing a novel adaptive strategy that can significantly improve the operational capabilities of quantum devices.
Quantum system control is a rapidly evolving field with immense potential in diverse applications, including quantum computing, quantum sensing, and quantum communication. This paper explores the design and implementation of an adaptive quantum feedback mechanism leveraging dynamic system modeling and machine learning to enhance the stability and controllability of quantum systems. We present a novel approach that dynamically adjusts quantum control parameters based on real-time system state monitoring, offering a significant advancement over existing methods. The core of this mechanism focuses on creating a self-correcting feedback loop, enabling precise control and mitigating errors inherent in quantum systems. The paper details the mathematical framework underpinning the adaptive control, highlighting the key algorithm and its benefits. We conclude with a discussion of the potential impact of this approach on achieving higher fidelity and enhanced performance in quantum systems.
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Quantum topology is a rapidly evolving field with the potential to revolutionize quantum computing by offering novel platforms for quantum information processing. This research paper explores the development of an adaptive quantum topology control algorithm, aiming to enhance the precision and controllability of quantum systems. We present a novel approach leveraging self-tuning parameters to dynamically manipulate the quantum state, ultimately enabling more accurate and robust quantum computations. The core mechanism centers around an iterative process of parameter adjustment guided by a feedback loop, resulting in a system capable of achieving higher fidelity in quantum operations. This work introduces a new algorithmic framework for controlling quantum topology, demonstrating its potential to surpass existing techniques in achieving precise control and improving the performance of quantum algorithms. The research focuses on establishing a robust and efficient methodology for adapting quantum states to achieve desired outcomes.