We analyze the average fidelity (say, F) and the fidelity deviation (say, D) in noisy-channel quantum teleportation. Here, F represents how well teleportation is performed on average and D quantifies whether the teleportation is performed impartially on the given inputs, that is, the condition of universality. Our analysis results prove that the achievable maximum average fidelity ensures zero fidelity deviation, that is, perfect universality. This structural trait of teleportation is distinct from those of other limited-fidelity probabilistic quantum operations, for instance, universal-NOT or quantum cloning. This feature is confirmed again based on a tighter relationship between F and D in the qubit case. We then consider another realistic noise model where F decreases and D increases due to imperfect control. To alleviate such deterioration, we propose a machine-learning-based algorithm. We demonstrate by means of numerical simulations that the proposed algorithm can stabilize the system. Notably, the recovery process consists solely of the maximization of F, which reduces the control time, thus leading to a faster cure cycle.

我们分析了噪声信道量子隐形传态中的平均保真度（例如F）和保真度偏差（例如D）。在此，F表示平均进行隐形传态的程度，D表示是否对给定的输入公正地进行隐形传态，即通用性的条件。我们的分析结果证明，可实现的最大平均保真度确保零保真度偏差，即完美的通用性。隐形传态的这种结构特征与其他有限保真概率量子运算的特征不同，例如通用NOT或量子克隆。根据F和D之间更紧密的关系再次确认此功能在量子位的情况下。然后，我们考虑另一个不理想的噪声模型，其中由于控制不完善，F减小而D增大。为了减轻这种恶化，我们提出了一种基于机器学习的算法。我们通过数值模拟证明了所提出的算法可以使系统稳定。值得注意的是，恢复过程仅包括F的最大化，这会缩短控制时间，从而缩短固化周期。