利用不完全误差校正增强分布式传感
Enhancing distributed sensing with imperfect error correction
摘要：
通过分布式量子传感协议，纠缠在增强传感器网络中的信息处理任务方面显示出了希望。由于噪声在传感器网络中无处不在，需要基于Gottesman，Kitaev和Preskill（GKP）状态的纠错方案来提高性能，如[New J. Phys. 22，022001（2020）]所示，假设传感器之间的均匀噪声和完美的GKP状态。在这里，我们扩展的性能增强的分析有限压缩GKP态在非均匀噪声模型。首先，我们研究了GKP双模压缩码的不同级联方案。虽然传统的顺序级联方案在以前的作品中提高了噪声的抑制，我们提出了一个平衡的级联方案，优于顺序方案存在有限的GKP压缩。然后，我们将这些结果应用于分布式量子传感中的两个特定任务-参数估计和假设检验-以了解不完美压缩和性能之间的权衡。对于前一个任务，我们考虑了一个能量受限的情况下，并提供了一个最佳的方式来分配有限压缩GKP态的能量之间的传感器。对于后者的任务，我们表明，错误概率仍然可以大大降低，通过级联现实有限压缩GKP码。
英文摘要：
Entanglement has shown promise in enhancing information processing tasks in a sensor network, via distributed quantum sensing protocols. As noise is ubiquitous in sensor networks, error correction schemes based on Gottesman, Kitaev and Preskill (GKP) states are required to enhance the performance, as shown in [New J. Phys. 22, 022001 (2020)] assuming homogeneous noise among sensors and perfect GKP states. Here, we extend the analyses of performance enhancement to finite squeezed GKP states in a heterogeneous noise model. To begin with, we study different concatenation schemes of GKP-two-mode-squeezing codes. While traditional sequential concatenation schemes in previous works do improve the suppression of noise, we propose a balanced concatenation scheme that outperforms the sequential scheme in presence of finite GKP squeezing. We then apply these results to two specific tasks in distributed quantum sensing -- parameter estimation and hypothesis testing -- to understand the trade-off between imperfect squeezing and performance. For the former task, we consider an energy-constrained scenario and provide an optimal way to distribute the energy of the finite squeezed GKP states among the sensors. For the latter task, we show that the error probability can still be drastically lowered via concatenation of realistic finite squeezed GKP codes.
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