论文标题
通过深度学习增强的前馈控制的噪声预测和减少单电子自旋
Noise prediction and reduction of single electron spin by deep-learning-enhanced feedforward control
论文作者
论文摘要
噪声引起的控制缺陷是基于钻石的纳米级传感应用的重要问题，在这种应用中，通常利用基于测量的策略实时纠正低频噪声。但是，由于光子检测效率低，自旋状态读数需要很长时间。这不可避免地引入了降噪过程的延迟，并限制了其性能。在这里，我们介绍了通过预测噪声趋势并补偿延迟的深度学习方法来放松这一限制。我们通过实验性地实施了钻石中氮 - 视态中心的前馈量子控制，以保护其自旋相干性并改善对噪声的传感性能。新方法有效地增强了电子自旋的变质时间，从而使更多物理学从其谐振光谱中探索。提供了一个理论模型来解释改进。该方案可以应用于一般传感方案，并扩展到其他量子系统。
Noise-induced control imperfection is an important problem in applications of diamond-based nano-scale sensing, where measurement-based strategies are generally utilized to correct low-frequency noises in realtime. However, the spin-state readout requires a long time due to the low photon-detection efficiency. This inevitably introduces a delay in noise-reduction process and limits its performance. Here we introduce the deep learning approach to relax this restriction by predicting the trend of noise and compensating the delay. We experimentally implement feedforward quantum control of nitrogen-vacancy center in diamond to protect its spin coherence and improve the sensing performance against noise. The new approach effectively enhances the decoherence time of the electron spin, which enables exploring more physics from its resonant spectroscopy. A theoretical model is provided to explain the improvement. This scheme could be applied in general sensing schemes and extended to other quantum systems.
