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arXiv:2011.02807 · Physical Review X

Field Demonstration of Distributed Quantum Sensing without Post-Selection

Si-Ran Zhao, Yuzhe Zhang, Wen‐Zhao Liu, Jian-Yu Guan, Weijun Zhang, Chenglong Li, Bing Bai, Ming-Han Li, Yang Liu, Lixing You, Jun Zhang, Jingyun Fan, Feihu Xu, Qiang Zhang, Jian-Wei Pan

Published Jul 14, 2021• 61 citations
arXivHTMLPDF

Abstract

A real-world demonstration of a distributed quantum sensing network exhibits sensitivity that, for the first time, surpasses the shot-noise limit, thereby boosting the practical application of such networks.
DOI
10.1103/physrevx.11.031009

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Quantum metrology takes advantage of nonclassical resources such as squeezing and entanglement to achieve a sensitivity level below the standard quantum limit (SQL). To date, most demonstrations of quantum sensing are restricted to a single sensor, nevertheless, many sensing tasks rely on an array of sensors working collectively. The measurement sensitivity of separable sensors scales as 1/sqrt(M) known as the SQL where M is the number of sensors. Shared entanglement among all sensors can be harnessed to surpass the SQL. Recent theoretical advances in distributed quantum sensing have shown that multipartite entangled states give rise to an improvement of 1/sqrt(M) in measurement sensitivity over separable states when estimating a global parameter. We develop a reconfigurable entangled sensor network based on continuous variable multipartite entangled states. The demonstrated entangled sensor network is composed of three sensor nodes, each equipped with an electro-optic transducer for the detection of radio-frequency (RF) signals. By properly tailoring the entangled states through a variational quantum circuit (VQC), the entangled sensor network can be reconfigured to minimize the measurement noise by more than 3dB below the SQL in different distributed RF sensing tasks, e.g., measuring the angle of arrival and mean amplitude of incident RF waves. Such a capability of capturing global features of interrogating objects by a reconfigurable entangled sensor network with less quantum noise further creates opportunities to enable a quantum advantage in data-processing problems. VQCs in conjunction with classical processing constitutes a promising architecture for quantum simulations, classical optimization, and machine learning. We train the VQC by classical machine learning algorithms to optimize the entanglement shared by the sensors for solving practical data processing problems. We show an entanglement-enabled reduction in error probability for classification of multidimensional RF signals. Our work establishes the quantum advantage of distributed quantum sensing and would lead to applications in ultrasensitive positioning, navigation, and data processing.