Distributed quantum metrology (DQM) enables the estimation of global functions of d distributed parameters beyond the capability of separable sensors. Continuous-variable DQM involves using a linear network with at least one nonclassical input. Here, we fully elucidate the structure of linear networks with two nonvacuum inputs, which allows us to prove a number of fundamental properties of continuous-variable DQM. While measuring the sum of d parameters at the Heisenberg limit can be achieved with a single nonvacuum input, we show that two inputs, one of which can be classical, are required to measure an arbitrary linear combination of d parameters and an arbitrary global function of the parameters. We obtain a universal and tight upper bound on the sensitivity of DQM networks with two inputs, and completely characterize the properties of the nonclassical input required to obtain a quantum advantage. This reveals that a wide range of nonclassical states makes this possible, including a squeezed vacuum. We also show that, for a class of nonclassical inputs, local photon number detection will achieve the maximum sensitivity. Finally we show that a general DQM network has two distinct regimes. The first achieves Heisenberg scaling. In the second the nonclassical input is much weaker than the coherent input, nevertheless providing a multiplicative enhancement to the otherwise classical sensitivity.
Distributed quantum metrology (DQM) enables the estimation of global functions of d distributed parameters beyond the capability of separable sensors. Continuous-variable DQM involves using a linear network with at least one nonclassical input. Here, we fully elucidate the structure of linear networks with two nonvacuum inputs, which allows us to prove a number of fundamental properties of continuous-variable DQM. While measuring the sum of d parameters at the Heisenberg limit can be achieved with a single nonvacuum input, we show that two inputs, one of which can be classical, are required to measure an arbitrary linear combination of d parameters and an arbitrary global function of the parameters. We obtain a universal and tight upper bound on the sensitivity of DQM networks with two inputs, and completely characterize the properties of the nonclassical input required to obtain a quantum advantage. This reveals that a wide range of nonclassical states makes this possible, including a squeezed vacuum. We also show that, for a class of nonclassical inputs, local photon number detection will achieve the maximum sensitivity. Finally we show that a general DQM network has two distinct regimes. The first achieves Heisenberg scaling. In the second the nonclassical input is much weaker than the coherent input, nevertheless providing a multiplicative enhancement to the otherwise classical sensitivity.
We derive a bound on the ability of a linear-optical network to estimate a linear combination of independent phase shifts by using an arbitrary nonclassical but unentangled input state, thereby elucidating the quantum resources required to obtain the Heisenberg limit with a multiport interferometer. Our bound reveals that while linear networks can generate highly entangled states, they cannot effectively combine quantum resources that are well distributed across multiple modes for the purposes of metrology: In this sense, linear networks endowed with well-distributed quantum resources behave classically. Conversely, our bound shows that linear networks can achieve the Heisenberg limit for distributed metrology when the input photons are concentrated in a small number of input modes, and we present an explicit scheme for doing so.
Distributed quantum metrology can improve the sensitivity in estimating arbitrary positive-weight linear combinations beyond the classical limit. It is known that multimode entangled states are crucial for distributed metrology. However, strategies to efficiently engineer multimode entangled states and harness them in distributed quantum metrology remains largely unexplored. In this work, we introduce a concept for distributed quantum sensing utilizing nonlinear optical networks. Unlike linear optical networks, nonlinear optical networks utilize optical parametric amplifiers instead of beam splitters for beam splitting. Here, we employ both simultaneous estimation (global) and separate estimation (local) methods to estimate linear combinations and derive the quantum Cram\'er-Rao bound for the two schemes, respectively. Our findings indicate that the sensitivity of the global strategy surpasses that of the local strategy and reaches the Heisenberg limit. Notably, direct intensity detection saturates the quantum Cram\'er-Rao bound. Furthermore, we characterize loss effects on measurement sensitivity, revealing the reduction in sensitivity with increasing losses and establishing the conditions for surpassing the standard quantum limit. This scheme can be directly applied to applications such as biological imaging and quantum information processing.
Quantum metrology enables parameter estimation beyond classical limits by exploiting nonclassical resources such as squeezing and entanglement. In distributed quantum sensing, Heisenberg scaling has been extended from 1/N2 to 1/(NM)2 through entanglement across both particles and spatial modes, where N denotes the photon number and M the number of spatially distributed modes. However, the overall sensitivity has remained limited to linear scaling with the number of measurement repetitions R. Here, we show that exploiting entanglement across temporal modes via time-domain multiplexing enables a scaling advantage with respect to R. As a result, the sensitivity can asymptotically approach simultaneous Heisenberg scaling in photons, spatial modes, and repetitions, yielding an overall sensitivity approaching Δ2φ∝1/(NMR)2. Using the Bogoliubov transformation formalism, we prove the optimality of the protocol within the class of Gaussian states and show that the scaling is realizable via homodyne detection and maximum-likelihood estimation. We further show that the advantage persists under optical loss and propose an experimentally feasible loop-based photonic sensing scheme. Our results open a route to incorporating time-multiplexing techniques into quantum metrology.
Quantum sensing and quantum metrology propose schemes for the estimation of physical properties, such as lengths, time intervals, and temperatures, achieving enhanced levels of precision beyond the possibilities of classical strategies. However, such an enhanced sensitivity usually comes at a price: the use of probes in highly fragile states, the need to adaptively optimise the estimation schemes to the value of the unknown property we want to estimate, and the limited working range, are some examples of challenges which prevent quantum sensing protocols to be practical for applications. This work reviews two feasible estimation schemes which address these challenges, employing easily realisable resources, i.e., squeezed light, and achieve the desired quantum enhancement of the precision, namely the Heisenberg-scaling sensitivity. In more detail, it is here shown how to overcome, in the estimation of any parameter affecting in a distributed manner multiple components of an arbitrary M-channel linear optical network, the need to iteratively optimise the network. In particular, we show that this is possible with a single-step adaptation of the network based only on a prior knowledge of the parameter achievable through a “classical” shot-noise limited estimation strategy. Furthermore, homodyne measurements with only one detector allow us to achieve Heisenberg-limited estimation of the parameter. We further demonstrate that one can avoid the use of any auxiliary network at the price of simultaneously employing multiple detectors.
The simultaneous estimation of multiple parameters is a central task in quantum metrology, distributed sensing, and the calibration of large photonic interferometers. A fundamental question is how many independent parameter combinations can inherit Heisenberg scaling from a given number of squeezed probes in a multimode Gaussian network. Here, we answer this question for arbitrary passive linear optical networks. For a p-parameter, M-channel interferometer probed by k single-mode squeezed states and at least one coherent state in the remaining channels, we show that the rank of the Heisenberg-scaling coefficient of the quantum Fisher information matrix is bounded by nHS≤min{p,k(k+3)/2}, which corresponds to the maximum number of independent combinations of parameters that can be estimated with Heisenberg-scaling sensitivity. The bound separates into two geometrically distinct contributions. The covariance contribution of the quantum Fisher information, which describes squeezing-enhanced fluctuations, provides at most k(k+1)/2 parameter combinations estimable at Heisenberg-scaling sensitivity, while the first-moment contribution provides at most k additional independent parameter combinations with Heisenberg-scaling sensitivity. We identify the conditions for saturating these bounds and construct a passive family of interferometers that saturates these bounds.
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.