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Laser & Photonics Review

Quantum‐Enhanced Optomechanical Sensor Network

Qinghui Li, Wei Li, Yufan Wang, Yajun Wang, Long Tian, Shaoping Shi, Yaohui Zheng

Published Nov 11, 2025• 3 citations
Publisher

Abstract

ABSTRACT Distributed quantum sensing (DQS), an essential branch of quantum metrology, can substantially boost the sensitivity and reliability in multi‐parameter estimation, achieving the Heisenberg scaling. Here, we report the first experimental demonstration of a quantum‐enhanced optomechanical sensor network, achieving the DQS advantage of a maximum ‐fold increase trend with sensor number. The challenge of poor uniformity is addressed by decoupling and independently manipulating each optomechanical parameter, and the acquired inconsistencies are less than 2.4%. By tailoring a four‐partite entangled state, the acquired quantum advantage is 57.3% compared to that with coherent probes, and 23.7% compared to that with the separable scheme. Moreover, the quantum enhanced optomechanical sensor network is applied to estimate incoherent forces, enhancing the estimation resolution by 33.7% in contrast with the separable scheme. The entanglement‐enhanced optomechanical sensors with high consistency pave the way for future large‐scale quantum sensor network, facilitating the micro‐seismic epicenter locating, dark matter searching, and so on.

DOI
10.1002/lpor.202501636

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ABSTRACT Distributed quantum sensing (DQS), an essential branch of quantum metrology, can substantially boost the sensitivity and reliability in multi‐parameter estimation, achieving the Heisenberg scaling. Here, we report the first experimental demonstration of a quantum‐enhanced optomechanical sensor network, achieving the DQS advantage of a maximum ‐fold increase trend with sensor number. The challenge of poor uniformity is addressed by decoupling and independently manipulating each optomechanical parameter, and the acquired inconsistencies are less than 2.4%. By tailoring a four‐partite entangled state, the acquired quantum advantage is 57.3% compared to that with coherent probes, and 23.7% compared to that with the separable scheme. Moreover, the quantum enhanced optomechanical sensor network is applied to estimate incoherent forces, enhancing the estimation resolution by 33.7% in contrast with the separable scheme. The entanglement‐enhanced optomechanical sensors with high consistency pave the way for future large‐scale quantum sensor network, facilitating the micro‐seismic epicenter locating, dark matter searching, and so on.
Abstract Squeezed light has long been used to enhance the precision of a single optomechanical sensor. An emerging set of proposals seeks to use arrays of optomechanical sensors to detect weak distributed forces, for applications ranging from gravity-based subterranean imaging to dark matter searches; however, a detailed investigation into the quantum-enhancement of this approach remains outstanding. Here, we propose an array of entanglement-enhanced optomechanical sensors to improve the broadband sensitivity of distributed force sensing. By coherently operating the optomechanical sensor array and distributing squeezing to entangle the optical fields, the array of sensors has a scaling advantage over independent sensors (i.e., M→M\sqrt{M}\to MM​→M M → M , where M is the number of sensors) due to coherence as well as joint noise suppression due to multi-partite entanglement. As an illustration, we consider entanglement-enhancement of an optomechanical accelerometer array to search for dark matter, and elucidate the challenge of realizing a quantum advantage in this context.
Distributed quantum metrology has drawn intense interest as it outperforms the optimal classical counterparts in estimating multiple distributed parameters. However, most schemes so far have required entangled resources consisting of photon numbers equal to or more than the parameter numbers, which is a fairly demanding requirement as the number of nodes increases. Here, we present a distributed quantum sensing scenario in which quantum-enhanced sensitivity can be achieved with fewer photons than the number of parameters. As an experimental demonstration, using a two-photon entangled state, we estimate four phases distributed 3 km away from the central node, resulting in a 2.2 dB sensitivity enhancement from the standard quantum limit. Our results show that the Heisenberg scaling can be achieved even when using fewer photons than the number of parameters. We believe our scheme will open a pathway to perform large-scale distributed quantum sensing with currently available entangled sources.
Acceleration sensing, an essential branch of quantum sensing, faces a fundamental trade-off between resolution and bandwidth. Here, we present a quantum-enhanced optomechanical accelerometer (QEOMA), simultaneously achieving the improvement of the sensing resolution and bandwidth in contrast with a classical counterpart. By tailoring quantum squeezed light, the optomechanical cooperativity is significantly raised, extending the sensing bandwidth. Quantum squeezed light increases the equivalent Q value of the optomechanical accelerometer owing to the reduction of the mechanical damping rate, driving the resolution improvement at the resonance frequency. At off-resonance frequencies, the resolution improvement is attributed to the imprecision noise reduction. We obtain the measured noise power spectrum and inferred acceleration resolution for the (3,3), (4,4), (5,5), and (6,6) mechanical modes, respectively. The maximum quantum enhancement is measured for the (6,6) mechanical mode with a 38.4% resolution enhancement and 1.55-fold bandwidth broadening in contrast with a coherent probe. The proposed QEOMA shows significant potential for applications ranging from ultralight dark matter searches to inertial navigation of fast-moving objects.
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.
Sensing networks underpin applications ranging from fundamental physics to real-world engineering. Distributed quantum sensing (DQS) can improve measurement performance, but existing protocols typically require multipartite entanglement, which poses substantial challenges for scalable implementation. Here, we introduce a DQS protocol based on bidirectional causal routing in a cyclic network, where a single probe sequentially interrogates M independent sensors along two opposite causal routes. By exploiting the noncommutativity between inter-sensor propagation and local sensing operations, the protocol turns propagation from a passive transport process into a source of sensing information, yielding an asymptotic 1/M^2 scaling of the estimation precision without multipartite entanglement. We experimentally demonstrate the protocol for distributed beam-tilt sensing in a free-space quantum optical network comprising up to 9 sensors, achieving picoradian-level precision in estimating the average tilt angle. These results identify propagation dynamics and routing geometry as active metrological resources for scalable distributed quantum sensing.
Abstract A plethora of applications hinge on a network or an array of sensors to undertake measurement tasks. A rule of thumb for sensing is that a collective measurement taken by M independent sensors can improve the sensitivity by 1 / M , known as the standard quantum limit (SQL). Quantum resources such as entanglement and squeezed light can be harnessed to surpass the SQL. Distributed quantum sensing is an emerging subject dedicated to investigating the performance gain enabled by entangled states shared by multiple sensors in tackling different measurement problems. This review formulates distributed quantum sensing (DQS) from a quantum-information perspective and describes DQS protocols and their experimental demonstrations. The applications enabled by DQS and an outlook for future opportunities will also be discussed.