We study the dephasing-assisted parameter estimation precision (PEP) enhancement in an atom interferometer with dynamical decoupling (DD) pulses. Through calculating the spin squeezing (SS) and the quantum Fisher information, we find that dephasing noise can improve PEP by inducing SS, and the DD pulses can maximize the improvement. It is indicated that when using the DD pulse the dephasing-induced SS can reach the limit of ``one-axis twisting'' model ${\ensuremath{\xi}}^{2}\ensuremath{\simeq}{N}^{\ensuremath{-}2/3}$ with ${\ensuremath{\xi}}^{2}$ being the SS parameter and N the number of atoms. In particular, we find that the DD pulses can amplify the dephasing-induced quantum Fisher information by a factor of $\ensuremath{\simeq}N/2$ compared with the noise-free case, which means that under the control of DD pulses, the dephasing noise can enhance the precision of parameter estimation to the scale of 2/N, the same scale of the Heisenberg limit (1/N).
Dynamical decoupling (DD) and bath engineering are two parallel techniques employed to mitigate qubit decoherence resulting from their unavoidable coupling to the environment. Here, we present a hybrid DD approach that integrates pulsed DD with bath spin polarization to enhance qubit coherence within the central spin model. This model, which can be realized using GaAs semiconductor quantum dots or analogous quantum simulators, demonstrates a significant extension of the central spin's coherence time by approximately 2 to 3 orders of magnitude that compared with the free-induced decay time, where the dominant contribution from DD and a moderate improvement from spin-bath polarization. This study, which integrates uniaxial dynamical decoupling and auxiliary bath-spin engineering, paves the way for prolonging coherence times in various practical quantum systems, including GaAs/AlGaAs, silicon and Si/SiGe. And this advancement holds substantial promise for applications in quantum information processing.
Protecting quantum coherence against realistic environmental noise remains one of the fundamental obstacles to scalable quantum technologies. We develop a noise-adaptive dynamical decoupling framework that combines analytical open-quantum-system modeling with machine-learning-based forecasting for a qubit interacting with random telegraph noise. Unlike conventional dynamical decoupling protocols based on fixed pulse schedules, the proposed approach continuously forecasts short-time coherence evolution and adaptively applies control pulses according to the instantaneous noise dynamics. We investigate stationary and non-stationary environments spanning both Markovian and non-Markovian regimes. Numerical simulations demonstrate that the machine-learning-assisted adaptive control strategy substantially outperforms conventional periodic dynamical decoupling while using a comparable number of control pulses. The improvement becomes particularly pronounced in non-Markovian and non-stationary regimes, where memory effects, coherence revivals, and temporally evolving noise strongly limit the effectiveness of static pulse protocols. These results establish predictive machine-learning-assisted dynamical decoupling as a promising and scalable framework for adaptive quantum control in realistic noisy quantum devices.