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For two unknown mixed quantum states $\rho$ and $\sigma$ in an $N$-dimensional Hilbert space, computing their fidelity $F(\rho,\sigma)$ is a basic problem with many important applications in quantum computing and quantum information, for example verification and characterization of the outputs of a quantum computer, and design and analysis of quantum algorithms. In this paper, we propose a quantum algorithm that solves this problem in $\operatorname{poly}(\log (N), r, 1/\varepsilon)$ time, where $r$ is the lower rank of $\rho$ and $\sigma$, and $\varepsilon$ is the desired precision, provided that the purifications of $\rho$ and $\sigma$ are prepared by quantum oracles. This algorithm exhibits an exponential speedup over the best known algorithm (based on quantum state tomography) which has time complexity polynomial in $N$., Comment: Final version with an improvement over the previous version. 19 pages, 2 tables, 1 algorithm

Deep learning methods such as convolutional neural networks (CNN) have been shown to be highly effective in complex nonlinear modeling or classification in multimode fiber transmission systems with intensity-only detection. However, such powers are often realized along with time-consuming training processes requiring large number of data samples, which may not be achievable in some practical implementations where only limited numbers of samples are available. In this paper, aiming at high-accuracy perturbation location in a multimode fiber specklegram sensing (FSS) system with small size training data set, we compare and analyze the performance of the CNN based FSS system using fibers under different conditions of mode coupling. We demonstrate that, by utilizing a ring core fiber (RCF) supporting a few weakly-coupled mode groups(MGs), high accuracy of around 100% in the classification of perturbation locations can be more easily achieved with faster convergence speed and fewer training samples, compared with FSS systems using conventional OM3 multimode fibers (MMFs) with hundreds of modes. Furthermore, owing to similar low-level characteristics extracted from the speckle-pattern images, the CNN exhibits good performance of transfer learning in a more practical RCF based FSS system with foot-stepping perturbations, confirming their good potential of extension into practical systems.

In this article, based on a specially designed ring-core fiber (RCF) with low inter-mode group (MG) crosstalk, we experimentally demonstrate orbital-angular-momentum (OAM) MG-multiplexing (MGM) transmission with intensity modulation and direct detection (IM/DD) utilizing discrete multi-tone (DMT) modulation. Two adjacent high-order OAM MGs | l | = 2 and | l | = 3 with low inter-MG crosstalk of −24.3 dB over 1-km RCF transmission system are employed for data transmission simultaneously. In addition, a low-complexity frequency-domain polynomial nonlinear equalizer (FD-PNLE) combining with adaptive bit and power loading are implemented to mitigate the modulation/detection related nonlinearities as well as modal dispersion induced high-frequency power fading. Experimental results show that compared with linear equalization, the capacities of MGs | l | = 2 and | l | = 3 using FD-PNLE can be significantly improved by 13% and 11.1% with adaptive bit and power loading DMT modulation over 1-km low-crosstalk RCF at a received optical power (ROP) of −13 dBm, respectively. Based on the nonlinearity-aware adaptive bit and power loading DMT signal, successful 186.4-Gbit/s MGM transmission over 1-km low inter-crosstalk RCF is realized only with 20-GHz electrical signal bandwidth. Therefore, the low-crosstalk RCF-based MGM scheme combined with advanced modulation format shows great potential in large-capacity low-cost short-reach optical interconnects.

Loop pipelining is one of the most important optimization methods in high-level synthesis (HLS) for increasing loop parallelism. There has been considerable work on improving loop pipelining, which mainly focuses on optimizing static operation scheduling and parallel memory accesses. Nonetheless, when loops contain complex memory dependencies, current techniques cannot generate high performance pipelines. In this paper, we extend the capability of loop pipelining in HLS to handle loops with uncertain dependencies (i.e., parameterized by an undetermined variable) and/or nonuniform dependencies (i.e., varying between loop iterations). Our optimization allows a pipeline to be statically scheduled without the aforementioned memory dependencies, but an associated controller will change the execution speed of loop iterations at runtime. This allows the augmented pipeline to process each loop iteration as fast as possible without violating memory dependencies. We use a parametric polyhedral analysis to generate the control logic for when to safely run all loop iterations in the pipeline and when to break the pipeline execution to resolve memory conflicts. Our techniques have been prototyped in an automated source-to-source code transformation framework, with Xilinx Vivado HLS, a leading HLS tool, as the RTL generation backend. Over a suite of benchmarks, experiments show that our optimization can implement optimized pipelines at almost the same clock speed as without our transformations, running approximately 3.7– $10{\times }$ faster, with a reasonable resource overhead.

This letter presents a novel flood detection approach using full polarimetric synthetic aperture radar (PolSAR) images based on a level set method considering prior geoinformation. The prior geoinformation includes information derived from vector data and topography data. The main approach accomplishes flood detection by the improved level set method, an active contour segmentation model, based on the classical Wishart distribution. Vector data are used to generate the zero initial level set curves. To investigate the separability between water and nonwater low–backscattering objects in PolSAR images, topography information is incorporated into the level set function as a constraint. Moreover, we introduce a piecewise statistical method to refine the result with the Kullback–Leibler divergence of circular polarization coherence. In addition, we design a new quantitative evaluation index to assess flood detection results. For validation, three real PolSAR images of flooded area are tested. The experimental results confirm the effectiveness of the proposed method.

Polarimetric features are essential to polarimetric synthetic aperture radar (PolSAR) image classification for their better physical understanding of terrain targets. The designed classifiers often achieve better performance via feature combination. However, the simply combination of polarimetric features cannot fully represent the information in PolSAR data, and the statistics of polarimetric features are not extensively studied. In this paper, we propose a joint statistical model for polarimetric features derived from the covariance matrix. The model is based on copula for multivariate distribution modeling and alpha-stable distribution for marginal probability density function estimations. We denote such model by CoAS. The proposed model has several advantages. First, the model is designed for real-valued polarimetric features, which avoids the complex matrix operations associated with the covariance and coherency matrices. Second, these features consist of amplitudes, correlation magnitudes, and phase differences between polarization channels. They efficiently encode information in PolSAR data, which lends itself to interpretability of results in the PolSAR context. Third, the CoAS model takes advantage of both copula and the alpha-stable distribution, which makes it general and flexible to construct the joint statistical model accounting for dependence between features. Finally, a supervised Markovian classification scheme based on the proposed CoAS model is presented. The classification results on several PolSAR data sets validate the efficacy of CoAS in PolSAR image modeling and classification. The proposed CoAS-based classifiers yield superior performance, especially in building areas. The overall accuracies are higher by 5%–10%, compared with other benchmark statistical model-based classification techniques.

Automatic optical-to-SAR image registration is considered as a challenging problem because of the inconsistency of radiometric and geometric properties. Feature-based methods have proven to be effective; however, common features are difficult to extract and match, and the robustness of those methods strongly depends on feature extraction results. In this paper, a new method based on iterative line extraction and Voronoi integrated spectral point matching is developed. The core idea consists of three aspects: 1) An iterative procedure that combines line segment extraction and line intersections matching is proposed to avoid registration failure caused by poor feature extraction. 2) A multilevel strategy of coarse-to-fine registration is presented. The coarse registration aims to preserve main linear structures while reducing data redundancy, thus providing robust feature matching results for fine registration. 3) Voronoi diagram is introduced into spectral point matching to further enhance the matching accuracy between two sets of line intersection. Experimental results show that the proposed method improves the matching performance. Compared with previous methods, the proposed algorithm can effectively and robustly generate sufficient reliable point pairs and provide accurate registration.

This paper presents the design and implementation of a Shunt Active Filter (SAF) for aircraft power networks using an accurate wide-band current control method based on Iterative Learning Control (ILC). The SAF control system is designed to compensate harmonic currents, with a 400 Hz supply voltage. This work introduces useful design strategies to increase the error-decay speed and improve the robustness of the SAF control system by using a hybrid P-type ILC controller. Detailed design of the hybrid P-type ILC controller and simulation results are presented. The overall system implementation is demonstrated through experimental results on a laboratory prototype.