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<p>Quantum robotic systems hold promise for applications in molecular manipulation and high-precision sensing, but their operation is highly vulnerable to environmental noise. This work introduces a large deviation principle (LDP)-based control framework for mitigating stochastic perturbations in coupled master–slave quantum robots. The system Hamiltonian, expressed in terms of position and momentum operators, incorporates control terms alongside Gaussian and Poisson noise, capturing both gradual fluctuations and sudden jumps. By computing the large deviation rate function, we quantify the probability of rare noise-induced deviations and derive an optimal control strategy that minimizes such events in key observables. Simulations across distinct dynamical regimes demonstrate that the controlled trajectories remain close to the desired wave function, with deviations consistent with the theoretical bounds. These results validate the robustness and generality of the approach, providing a practical framework for stabilizing quantum robotic systems in noisy environments with potential applications in precision sensing, molecular chemistry, and quantum computing.</p>

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Large deviation-based noise mitigation in coupled quantum robotic systems

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月3日

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  • Rohit Singla,
  • Rodrigo Verschae,
  • Harish Parthasarathy

摘要

Quantum robotic systems hold promise for applications in molecular manipulation and high-precision sensing, but their operation is highly vulnerable to environmental noise. This work introduces a large deviation principle (LDP)-based control framework for mitigating stochastic perturbations in coupled master–slave quantum robots. The system Hamiltonian, expressed in terms of position and momentum operators, incorporates control terms alongside Gaussian and Poisson noise, capturing both gradual fluctuations and sudden jumps. By computing the large deviation rate function, we quantify the probability of rare noise-induced deviations and derive an optimal control strategy that minimizes such events in key observables. Simulations across distinct dynamical regimes demonstrate that the controlled trajectories remain close to the desired wave function, with deviations consistent with the theoretical bounds. These results validate the robustness and generality of the approach, providing a practical framework for stabilizing quantum robotic systems in noisy environments with potential applications in precision sensing, molecular chemistry, and quantum computing.