1,720,963 research outputs found
Quantum matching pursuit: A quantum algorithm for sparse representations
Full text linkRepresenting signals with sparse vectors has a wide spectrum of applications that ranges from image and video coding to shape representation and health monitoring. In many applications with real-time requirements or that deal with high-dimensional signals, the computational complexity of the encoder that finds the sparse representation plays an important role. Quantum computing has recently shown promising speedups in many representation learning tasks. In this work, we propose a quantum version of the well-known matching-pursuit algorithm. Assuming the availability of a fault-tolerant quantum random access memory, our quantum matching pursuit lowers the complexity of its classical counterpart by a polynomial factor, at the cost of some error in the computation of the inner products, enabling the computation of sparse representations of high-dimensional signals. Besides proving the computational complexity of our algorithm, we provide numerical experiments that show that its error is negligible in practice. This work opens the path to further research on quantum algorithms for finding sparse representations, showing suitable quantum computing applications in signal processing
Quantum Eigenfaces: Linear Feature Mapping and Nearest Neighbor Classification with Outlier Detection
Full text linkWe propose a quantum machine learning algorithm for data classification, inspired by the seminal computer vision approach of eigenfaces for face recognition. The algorithm enhances nearest neighbor/centroid classifiers with concepts from principal component analysis, enabling the automatic detection of outliers and finding use in anomaly detection domains beyond face recognition. Assuming classical input data, we formalize how to implement the algorithm using a quantum random access memory and state-of-the-art quantum linear algebra, discussing the complexity of performing the classification algorithm on a fault-tolerant quantum device. The asymptotic time complexity analysis shows that the quantum classification algorithm can be more efficient than its classical counterpart. We showcase an application of this algorithm for face recognition and image classification datasets with anomalies, obtaining promising results for the running time parameters. This work contributes to the growing field of quantum machine learning applications, and the algorithm's simplicity makes it easily adoptable by future quantum machine learning practitioners
Evaluating the potential of quantum machine learning in cybersecurity: A case-study on PCA-based intrusion detection systems
Full text linkQuantum computing promises to revolutionize our understanding of the limits of computation, and its implications in cryptography have long been evident. Today, cryptographers are actively devising post-quantum solutions to counter the threats posed by quantum-enabled adversaries. Meanwhile, quantum scientists are innovating quantum protocols to empower defenders. However, the broader impact of quantum computing and quantum machine learning (QML) on other cybersecurity domains still needs to be explored. In this work, we investigate the potential impact of QML on cybersecurity applications of traditional ML. First, we explore the potential advantages of quantum computing in machine learning problems specifically related to cybersecurity. Then, we describe a methodology to quantify the future impact of fault-tolerant QML algorithms on real-world problems. As a case study, we apply our approach to standard methods and datasets in network intrusion detection, one of the most studied applications of machine learning in cybersecurity. Our results provide insight into the conditions for obtaining a quantum advantage and the need for future quantum hardware and software advancements
Quantum algorithms for SVD-based data representation and analysis
Full text linkThis paper narrows the gap between previous literature on quantum linear algebra and practical data analysis on a quantum computer, formalizing quantum procedures that speed-up the solution of eigenproblems for data representations in machine learning. The power and practical use of these subroutines is shown through new quantum algorithms, sublinear in the input matrix’s size, for principal component analysis, correspondence analysis, and latent semantic analysis. We provide a theoreti- cal analysis of the run-time and prove tight bounds on the randomized algorithms’ error. We run experiments on multiple datasets, simulating PCA’s dimensionality reduction for image classification with the novel routines. The results show that the run-time parameters that do not depend on the input’s size are reasonable and that the error on the computed model is small, allowing for competitive classification performances
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Quantum matching pursuit algorithms
Full text linkLAUREA MAGISTRALEIl machine learning quantistico è una disciplina emergente che combina i benefici del calcolo quantistico con tecniche di machine learning. Infatti, il calcolo quantistico accellera la computazione di diverse operazioni rispetto alla complessità delle attuali tecniche classiche. In questo lavoro, presentiamo tre nuovi algoritmi quantistici. I primi due algoritmi sono relativi alla teoria delle rappresentazioni sparse e sono basati su Matching Pursuit e Orthogonal Matching Pursuit. Questi algoritmi risolvono un sistema lineare minimizzando in maniera avida la norma della soluzione. Il terzo algoritmo, invece, è un algoritmo di classificazione nearest neighbour che sfrutta la riduzione della dimensionalità per ottenere prestazioni migliori. L'uso di procedure quantistiche introduce errore di approssimazione nel calcolo di alcuni step intermedi. Per ognuno di questi algoritmi, forniamo una spiegazione dettagliata del loro funzionamento, del loro tempo di esecuzione, della loro probabilità di successo e quali requisiti devono essere soddisfatti per ottenere uno specifico errore di approssimazione nei risultati finali. Il tempo di esecuzione di questi algoritmi è inferiore rispetto ai loro equivalenti classici. In aggiunta a ciò, analizziamo, tramite l'uso di esperimenti numerici, come i risultati degli algoritmi sono influenzati dall'errore nei risultati intermedi. Per questa analisi usiamo sia dati generati artificialmente che dati reali relativi alla sicurezza informatica.Quantum machine learning is an emerging discipline that combines the benefits of quantum computing with machine learning techniques. Indeed, quantum computing speeds up the computation of several tasks with respect to what the current classical computing techniques achieve. In this work, we provide an analysis of three novel quantum algorithms. The first two algorithms are related to the sparse representation theory and are based respectively on Matching Pursuit and Orthogonal Matching Pursuit. These algorithms solve a linear system minimizing greedily the -norm of the solution. Instead, the third one is a nearest neighbor classification algorithm that exploits the reduction of dimensionality to achieve better performance. The use of quantum procedures introduces some approximation error in the computation of several intermediate steps. For each of these algorithms, we provide a detailed description of their procedure, their running time, their probability of failure, and what requirements must be met to ensure a specific error in the final results. The runtimes of these algorithms are lower than their classical equivalents.
In addition to that, we study, through the use of numerical experiments, how the outputs of the algorithms are affected by the errors in the intermediate results. In this analysis, we use both artificially generated datasets and real cybersecurity-related datasets
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