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Robust Quantum Control via a Model Predictive Control Strategy
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This article presents a robust control strategy using Time-Optimal Model Predictive Control (TOMPC) for a two-level quantum system subject to bounded uncertainties. In this method, the control field is optimized over a finite horizon using a nominal quantum system as the reference and then the optimal control for the first time interval is applied and a projective measurement is implemented on the uncertain system. The new control field for the next time interval will be iteratively optimized based on the measurement result. We present theoretical results to guarantee the stability of the TOMPC algorithm. We also characterize the robustness and the convergence rate of the TOMPC strategy for the control of two-level systems. Numerical simulations further demonstrate that, in the presence of uncertainties, our quantum TOMPC algorithm enhances robustness and steers the state to the desired state with high fidelity. This work contributes to the progress of Model Predictive Control in quantum control and explores its potential in practical applications of quantum technology.
Forward citations
Cited by 2 Pith papers
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A Model Predictive Control-Inspired Quantum Algorithm
A model-predictive-control-inspired hybrid algorithm optimizes quantum circuit layers over a receding horizon and is proven to at least match FALQON while sometimes outperforming it in practice.
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Model predictive quantum control: A modular approach for efficient and robust quantum optimal control
Splitting quantum optimal control into repeated short-horizon MPC problems, with terminal constraints or optimized setpoints, gives faster and more robust qubit state preparation in simulations.
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