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Register a new userMultidimensional quantum-enhanced target detection via spectro-temporal correlation measurements
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In this work we investigate quantum-enhanced target detection in the presence of large background noise using multidimensional quantum correlations between photon pairs generated through spontaneous parametric down-conversion. Until now similar experiments have only utilized one of the photon pairs many degrees of freedom such as temporal correlations and photon number correlations. Here, we utilized both temporal and spectral correlations of the photon pairs and achieved over an order of magnitude reduction to the background noise and in turn significant reduction to data acquisition time when compared to utilizing only temporal modes. We believe this work represents an important step in realizing a practical, real-time quantum-enhanced target detection system. The demonstrated technique will also be of importance in many other quantum sensing applications and quantum communications.
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Quantum computational approaches to some classic target identification and localization algorithms, especially for radar images, are investigated, and are found to raise a number of quantum statistics and quantum measurement issues with much broader applicability. Such algorithms are computationally intensive, involving coherent processing of large sensor data sets in order to extract a small number of low profile targets from a cluttered background. Target enhancement is accomplished through accurate statistical characterization of the environment, followed by optimal identification of statistical outliers. The key result of the work is that the environmental covariance matrix estimation and manipulation at the heart of the statistical analysis actually enables a highly efficient quantum implementation. The algorithm is inspired by recent approaches to quantum machine learning, but requires significant extensions, including previously overlooked `quantum analog--digital conversion steps (which are found to substantially increase the required number of qubits), `quantum statistical generalization of the classic phase estimation and Grover search algorithms, and careful consideration of projected measurement operations. Application regimes where quantum efficiencies could enable significant overall algorithm speedup are identified. Key possible bottlenecks, such as data loading and conversion, are identified as well.
Quantum illumination is a quantum sensing technique where entanglement is exploited to improve the detection of low-reflectivity targets in a strong thermal background. In this paper, we study the quantum illumination of suspected targets under the curved spacetime background of the Earth. It is counterintuitive to find that to achieve the same error-probability both target detection scenarios, the illumination strategy curved spacetime consumes less resources than the flat spacetime strategy. That is to say, the gravitational effect of the Earth can promote the efficiency of quantum spatial target detection. This is because the average particle number of the thermal signal reflected in the curved spacetime is always less than the number in flat spacetime. We also find that the spatial quantum target detection with bipartite entangled state is more efficient than the coherent state strategy in the curved spacetime.
Given a quantum system on many qubits split into a few different parties, how much total correlations are there between these parties? Such a quantity -- aimed to measure the deviation of the global quantum state from an uncorrelated state with the same local statistics -- plays an important role in understanding multi-partite correlations within complex networks of quantum states. Yet, the experimental access of this quantity remains challenging as it tends to be non-linear, and hence often requires tomography which becomes quickly intractable as dimensions of relevant quantum systems scale. Here, we introduce a much more experimentally accessible quantifier of total correlations, which can be estimated using only single-qubit measurements. It requires far fewer measurements than state tomography, and obviates the need to coherently interfere multiple copies of a given state. Thus we provide a tool for proving multi-partite correlations that can be applied to near-term quantum devices.
We describe a simple multivariate technique of likelihood ratios for improved discrimination of signal and background in multi-dimensional quantum target detection. The technique combines two independent variables, time difference and summed energy, of a photon pair from the spontaneous parametric down-conversion source into an optimal discriminant. The discriminant performance was studied in experimental data and in Monte-Carlo modelling with clear improvement shown compared to previous techniques. As novel detectors become available, we expect this type of multivariate analysis to become increasingly important in multi-dimensional quantum optics.
It is believed that the optimal performance of a quantum lidar or radar in the absence of an idler and only using Gaussian resources cannot exceed the performance of a semiclassical setup based on coherent states and homodyne detection. Here we disprove this conjecture by showing that an idler-free squeezed-based setup can beat this benchmark. More generally, we show that probes whose displacement and squeezing are jointly optimized can strictly outperform coherent states with the same mean number of input photons for both the problems of quantum illumination and reading.
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