ARC Centre for Engineered Quantum Systems, School of Physics, The University of Sydney, Sydney, New South Wales 2006, Australia.
National Measurement Institute, West Lindfield, New South Wales 2070, Australia.
Nat Commun. 2017 Jan 16;8:14106. doi: 10.1038/ncomms14106.
The wide-ranging adoption of quantum technologies requires practical, high-performance advances in our ability to maintain quantum coherence while facing the challenge of state collapse under measurement. Here we use techniques from control theory and machine learning to predict the future evolution of a qubit's state; we deploy this information to suppress stochastic, semiclassical decoherence, even when access to measurements is limited. First, we implement a time-division multiplexed approach, interleaving measurement periods with periods of unsupervised but stabilised operation during which qubits are available, for example, in quantum information experiments. Second, we employ predictive feedback during sequential but time delayed measurements to reduce the Dick effect as encountered in passive frequency standards. Both experiments demonstrate significant improvements in qubit-phase stability over 'traditional' measurement-based feedback approaches by exploiting time domain correlations in the noise processes. This technique requires no additional hardware and is applicable to all two-level quantum systems where projective measurements are possible.
量子技术的广泛采用需要在面对测量下的状态崩溃挑战时，实际提高我们保持量子相干的能力，取得高性能的进展。在这里，我们使用控制理论和机器学习技术来预测量子位状态的未来演化；我们利用这些信息来抑制随机的、半经典的退相干，即使在测量访问受限的情况下也是如此。首先，我们实现了时分复用方法，在测量期间插入无监督但稳定的操作期，在此期间可以使用量子位，例如在量子信息实验中。其次，我们在顺序但有时间延迟的测量中采用预测性反馈，以减少被动频率标准中遇到的迪克效应。这两个实验通过利用噪声过程中的时域相关性，在量子位相稳定性方面显著优于“传统”基于测量的反馈方法。该技术不需要额外的硬件，并且适用于所有可以进行投影测量的两能级量子系统。
