As quantum computers become available to the general public, the need has arisen to train a cohort of quantum programmers, many of whom have been developing classical computer programs for most of their careers. While currently available quantum computers have less than 100 qubits, quantum computing hardware is widely expected to grow in terms of qubit count, quality, and connectivity. This review aims at explaining the principles of quantum programming, which are quite different from classical programming, with straightforward algebra that makes understanding of the underlying fascinating quantum mechanical principles optional. We give an introduction to quantum computing algorithms and their implementation on real quantum hardware. We survey 20 different quantum algorithms, attempting to describe each in a succinct and self-contained fashion. We show how these algorithms can be implemented on IBM’s quantum computer, and in each case, we discuss the results of the implementation with respect to differences between the simulator and the actual hardware runs. This article introduces computer scientists, physicists, and engineers to quantum algorithms and provides a blueprint for their implementations.

Global fisheries and the future of sustainable seafood are predicated on healthy populations of various species of fish and shellfish. Recent developments in the collection of large-volume optical data by autonomous underwater vehicles (AUVs), stationary camera arrays, and towed vehicles has made it possible for fishery scientists to generate species-specific, size-structured abundance estimates for different species of marine organisms via imagery. The immense volume of data collected by such devices quickly exceeds manual processing capacity and creates a strong need for automatic image analysis. This paper presents an open-source computer vision software platform designed to integrate common image and video analytics, such as stereo calibration, object detection and object classification, into a sequential data processing pipeline that is easy to program, multi-threaded, and generic. The system provides a cross-language common interface for each of these components, multiple implementations of each, as well as unified methods for evaluating and visualizing the results of different methods for accomplishing the same task.

In many applications, we are given a finite set of data points sampled from a data manifold and represented as a graph with edge weights determined by pairwise similarities of the samples. Often the pairwise similarities (which are also called affinities) are unreliable due to noise or due to intrinsic difficulties in estimating similarity values of the samples. As observed in several recent approaches, more reliable similarities can be obtained if the original similarities are diffused in the context of other data points, where the context of each point is a set of points most similar to it. Compared to the existing methods, our approach differs in two main aspects. First, instead of diffusing the similarity information on the original graph, we propose to utilize the tensor product graph (TPG) obtained by the tensor product of the original graph with itself. Since TPG takes into account higher order information, it is not a surprise that we obtain more reliable similarities. However, it comes at the price of higher order computational complexity and storage requirement. The key contribution of the proposed approach is that the information propagation on TPG can be computed with the same computational complexity and the same amount of storage as the propagation on the original graph. We prove that a graph diffusion process on TPG is equivalent to a novel iterative algorithm on the original graph, which is guaranteed to converge. After its convergence we obtain new edge weights that can be interpreted as new, learned affinities. We stress that the affinities are learned in an unsupervised setting. We illustrate the benefits of the proposed approach for data manifolds composed of shapes, images, and image patches on two very different tasks of image retrieval and image segmentation. With learned affinities, we achieve the bull's eye retrieval score of 99.99 percent on the MPEG-7 shape dataset, which is much higher than the state-of-the-art algorithms. When the data- points are image patches, the NCut with the learned affinities not only significantly outperforms the NCut with the original affinities, but it also outperforms state-of-the-art image segmentation methods.

Many features of interest in remote sensing imagery, such as roads, rivers, clouds, trees, and buildings can have high spectral, structural, and textural variability due to variations in reflectance, resolution, intrinsic shape, etc. Nevertheless they have distinctive qualitative properties of their own from the point of human perception. For instance, clouds are typically fluffy or wispy, roads have uniform widths, rivers are rarely straight, and buildings are rectilinear. The efficient quantification of such qualitative structural signatures is important for automatically recognizing and labeling features in imagery. In this paper we demonstrate the value of constrained Delaunay triangulations (CDT) of discretely sampled shape contours for obtaining quantifiers of qualitative characteristics of certain features. These quantifiers are efficient to compute, and fairly robust to partial occlusions, resolution limitations, and noise. This has applications to the automated analysis and understanding of airborne and terrestrial imagery in classifying structures such as, clouds, forests, rivers, cities, etc.

Template matching is a fundamental method of detecting the presence or the absence of objects and identifying them in an image. A template is itself an image that contains a feature or an object or a part of a bigger image, and is used to search a given image for the presence or the absence of the contents of the template. This search is carried out by translating the template systematically pixel-by-pixel all over the image, and at each position of the template the closeness of the template to the area covered by it is measured. The location at which the maximum degree of closeness is achieved is declared to be the location of the object detected. The problem of object/shape recognition is addressed in this paper in a multiresolutional setting using pyramidal decomposition of images with respect to an orthonormal wavelet basis. A new approach to efficient template matching to detect objects using computational geometric methods is put forward. An efficient paradigm for object recognition is described in detail with a complexity analysis.

Proposes an abstract framework to address the problem of fault-tolerant integration of information provided by multiple sensors. This paper presents a formal description of spatially distributed sensor networks, where i) clusters of sensors monitor (possible overlapping) regions of the environment; ii) sensors return measured values of a multidimensional parameter of interest; and iii) uncertainties associated with a sensor output are represented by a connected subset in the parameter space. A method to obtain interval estimates of components of the actual parameter vector is developed, wherein information from faulty sensors are filtered out. The problem addressed involves combining interval estimates of sensor outputs into a best intersection estimate of outputs. The sensor fault model used assumes most faults cluster in the neighborhood of the correct values. The procedure of this paper is superior to earlier work. To test the theoretical analysis of the framework proposed, we have developed a modular parameter-driven simulator SIMDSN for the fault-tolerant integration of abstract sensor interval estimates. The simulator uses the well-known Monte-Carlo technique to generate random correct and tamely faulty intervals.>

Fault-tolerance is an important issue in network design because sensor networks must function in a dynamic, uncertain world. A functional characterization of the fault-tolerant integration of abstract interval estimates is proposed. This model provides a preliminary version for a general framework that is hoped to develop to address the general problem of fault-tolerant integration of abstract sensor estimates. A scheme for narrowing the width of the sensor output in a specific failure model is proposed and given a functional representation. The main distinguishing feature of the model over the original model of K. Marzullo (1989) is in reducing the width of the output interval estimate significantly in most cases where the number of sensors involved is large.>