Quantum-enhanced sensing promises to improve the performance of sensing tasks using nonclassical probes and measurements that require far fewer scene-modulated photons than the best classical schemes, thereby granting previously inaccessible information about a wide range of physical systems. We propose a generalized distributed sensing framework that uses an entangled quantum probe to estimate a scene-parameter encoded within an array of phases, with a functional dependence on that parameter determined by the physics of the actual system. The receiver uses a laser light source enhanced by quantum-entangled multipartite squeezed-vacuum light to probe the phases and thereby estimate the desired scene-parameter. The entanglement suppresses the collective quantum vacuum noise across the phase array. We report simple analytical expressions for the Cramér-Rao bound that depend only on the optical probes and the physical model of the measured system, and we show that our structured receiver asymptotically saturates the quantum Cramér-Rao bound in the lossless case. Our approach enables Heisenberg limited precision in estimating a scene-parameter with respect to total probe energy, as well as with respect to the number of modulated phases. Furthermore, we study the impact of uniform loss in our system and examine the behavior of both the quantum and the classical Cramér-Rao bounds. We apply our framework to examples as diverse as radio-frequency phased-array directional radar, beam-displacement tracking for atomic-force microscopy, and fiber-based temperature gradiometry.

中文翻译：

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嵌入在多相中的参数的纠缠增强估计

量子增强传感有望使用非经典探针和测量提高传感任务的性能，与最佳经典方案相比，这些测量需要更少的场景调制光子，从而提供以前无法获得的有关各种物理系统的信息。我们提出了一个广义的分布式传感框架，它使用纠缠量子探针来估计在相位阵列中编码的场景参数，并对该参数的功能依赖由实际系统的物理特性决定。接收器使用由量子纠缠多分压缩真空光增强的激光光源来探测相位，从而估计所需的场景参数。纠缠抑制了相位阵列上的集体量子真空噪声。我们报告了 Cramér-Rao 界的简单解析表达式，该表达式仅依赖于光学探针和被测系统的物理模型，并且我们表明，我们的结构化接收器在无损情况下渐近饱和了量子 Cramér-Rao 界。我们的方法使海森堡在估计与总探头能量以及调制相位数量相关的场景参数方面的精度有限。此外，我们研究了系统中均匀损失的影响，并检查了量子边界和经典克拉梅-拉奥边界的行为。我们将我们的框架应用于各种示例，例如射频相控阵定向雷达、原子力显微镜的光束位移跟踪和基于光纤的温度梯度测量。