Approximate decoherence free subspaces for distributed sensing
Approximate decoherence free subspaces for distributed sensing
复制标题
DOI：
10.1088/2058-9565/ac44de
复制
发表时间：
2022-04-01
影响因子：
6.7
通讯作者：
Duer, Wolfgang
中科院分区：
文献类型：
--
作者：
Hamann, Arne;Sekatski, Pavel;Duer, Wolfgang
我们考虑使用传感器网络来感知具有特定空间依赖性的标量值场，例如多个原子位于陷阱内的不同位置。我们展示了如何利用空间相关性来仅感测特定信号，并且通过为固定已知位置处的噪声源构建无退相干子空间，对不同位置处的其他信号或具有不等空间依赖性的其他信号不敏感。这可以扩展到位于某些表面上的噪声源，在这些表面上我们会遇到与经典静电学中的镜像电荷和等势面的连接。对于一般情况，我们引入了近似无退相干子空间的概念，其中某个体积内所有源的噪声都被显着抑制，但代价是以受控方式降低信号强度。我们证明，尽管存在大量的多个噪声源，但可以使用这种方法在大量传感器上长时间保持海森堡标度。我们引入了一种有效的形式主义来构造内部状态和传感器配置，并将其应用于几个示例以证明我们的方法的有用性和广泛适用性。
We consider the sensing of scalar valued fields with specific spatial dependence using a network of sensors, e.g. multiple atoms located at different positions within a trap. We show how to harness the spatial correlations to sense only a specific signal, and be insensitive to others at different positions or with unequal spatial dependence by constructing a decoherence-free subspace for noise sources at fixed, known positions. This can be extended to noise sources lying on certain surfaces, where we encounter a connection to mirror charges and equipotential surfaces in classical electrostatics. For general situations, we introduce the notion of an approximate decoherence-free subspace, where noise for all sources within some volume is significantly suppressed, at the cost of reducing the signal strength in a controlled way. We show that one can use this approach to maintain Heisenberg-scaling over long times and for a large number of sensors, despite the presence of multiple noise sources in large volumes. We introduce an efficient formalism to construct internal states and sensor configurations, and apply it to several examples to demonstrate the usefulness and wide applicability of our approach.