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.
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.
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.
Quantum-enhanced sensing has emerged as a transformative technology with the potential to surpass classical sensing modalities in precision and sensitivity.This study explores the advancements and applications of quantum-enhanced sensing, emphasizing its capacity to bridge fundamental physics and practical implementations.The current progress in experimental demonstrations of quantum-enhanced sensing systems was reviewed, focusing on breakthroughs in metrology and the development of physically realizable sensor architectures.Two practical implementations of quantum-enhanced sensors based on trapped ions were proposed.The first design utilizes Ramsey interferometry with spin-squeezed atomic ensembles, employing laser-induced spin-exchange interactions to reconstruct the sensing Hamiltonian.This approach enables measurement rates to scale with the number of sensing atoms, achieving sensitivity enhancements beyond the standard quantum limit (SQL).The second implementation introduces mean-field interactions mediated by coupled optical cavities that share coherent atomic probes, enabling the realization of high-performance sensing systems.Both sensor systems were demonstrated to be feasible on state-ofthe-art ion-trap platforms, offering promising benchmarks for future applications in metrology and imaging.Particular attention was given to the integration of quantum-enhanced sensing with complementary imaging technologies, which continues to gain traction in medical imaging and other fields.The mutual reinforcement of quantum and complementary technologies is increasingly supported by significant investments from governmental, academic, and commercial entities.The ongoing pursuit of improved measurement resolution and imaging fidelity underscores the interdependence of these innovations, advancing the transition of quantum-enhanced sensing from fundamental research to widespread practical use.
Distributed quantum sensing (DQS) leverages quantum resources to estimate an unknown global property of a networked quantum sensor beyond the classical limit. We propose and analyze an all-optical resource-efficient scheme for the next-generation DQS systems. Our method utilizes phase-sensitive optical parametric amplifiers and linear interferometers and achieves the sensitivity close to the optimal limit, as determined by the quantum Fisher information of the entangled resource state. Furthermore, it utilizes high-gain OPA-assisted detection, offering critical advantages of increased bandwidth and loss tolerance, in contrast to conventional methods employing balanced homodyne detection (BHD). We show the efficacy of our proposal for displacement sensing and show its loss tolerance against high levels of photon loss, thus circumventing the major obstacle in current BHD-based approaches. Our architectural analysis shows that our scheme can be realized with current quantum photonic technology
Distributed quantum sensing uses quantum correlations between multiple sensors to enhance the measurement of unknown parameters beyond the limits of unentangled systems. We describe a sensing scheme that uses continuous-variable multipartite entanglement to enhance distributed sensing of field-quadrature displacement. By dividing a squeezed-vacuum state between multiple homodyne-sensor nodes using a lossless beam-splitter array, we obtain a root-mean-square (rms) estimation error that scales inversely with the number of nodes (Heisenberg scaling), whereas the rms error of a distributed sensor that does not exploit entanglement is inversely proportional to the square root of the number of nodes (standard quantum limit scaling). Our sensor's scaling advantage is destroyed by loss, but it nevertheless retains an rms-error advantage in settings in which there is moderate loss. Our distributed sensing scheme can be used to calibrate continuous-variable quantum key distribution networks, to perform multiple-sensor cold-atom temperature measurements, and to do distributed interferometric phase sensing.
Abstract Distributed quantum sensing (DQS) leverages quantum resources to estimate an unknown global property of a networked quantum sensor beyond the classical limit. We propose and analyze an all-optical resource-efficient scheme for the next-generation DQS systems. Our method utilizes phase-sensitive optical parametric amplifiers (OPAs) and linear interferometers and achieves the sensitivity close to the optimal limit, as determined by the quantum Fisher information of the entangled resource state. Furthermore, it utilizes high-gain OPA-assisted detection, offering critical advantages of increased bandwidth and loss tolerance, in contrast to conventional methods employing balanced homodyne detection (BHD). We show the efficacy of our proposal for displacement sensing and show its loss tolerance against high levels of photon loss, thus circumventing the major obstacle in current BHD-based approaches. Our architectural analysis shows that our scheme can be realized with current quantum photonic technology.
This paper explores the potential of utilizing quantum entanglement to construct distributed sensor networks. The core claim is that leveraging entanglement enables precise measurement and transmission of environmental information. The proposed mechanism involves entangling multiple sensor nodes, exploiting the unique properties of entanglement for information fusion and data transfer, ultimately enhancing sensor accuracy and reliability. This research addresses a novel application of quantum entanglement in distributed sensing, offering the prospect of significantly improved sensor performance compared to classical approaches. The focus is on theoretical development and conceptual design, without experimental validation. We present a framework for designing such a network and discuss the key challenges and potential benefits.