1,720,956 research outputs found
An Application of Reinforcement Learning for Minor Embedding in Quantum Annealing
Full text linkResearch in the Quantum Computing (QC) field has been soaring thanks to the latest developments and wider availability of real hardware. The strong interest in this technology has naturally spurred a contamination with the Machine Learning (ML) field. Both quantum methods to perform ML and ML methods to support quantum computation has been developed. A largely diffused QC paradigm is that of Quantum Annealers, machines that can rapidly search for solutions to optimization problems. Their sparse qubit structure, however, requires to search for a mapping between the problem’s and the hardware’s graphs before computation. This is a NP-hard combinatorial optimization task in itself, called Minor Embedding. In this work, we aim at developing and assessing the capabilities of Reinforcement Learning to perform this task
Feature Selection via Quantum Annealers for Ranking and Classification Tasks
No full textFeature selection is a common step in many ranking, classification, or prediction tasks and serves many purposes. By removing redundant or noisy features, the accuracy of ranking or classification can be improved and the computational cost of the subsequent learning steps can be reduced. However, feature selection can be itself a computationally expensive process. While for decades confined to theoretical algorithmic papers, quantum computing is now becoming a viable tool to tackle realistic problems, in particular special-purpose solvers based on the Quantum Annealing paradigm. This paper aims to explore the feasibility of using currently available quantum computing architectures to solve some quadratic feature selection algorithms for both ranking and classification. Our experimental analysis shows that the effectiveness obtained with quantum computing hardware is comparable to that of classical solvers, indicating that quantum computers are now reliable enough to tackle interesting problems
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
Reinforcement Learning for Variational Quantum Circuit Design
Full text linkVariational Quantum Algorithms (VQAs) are widely used for solving optimization problems in the Noisy Intermediate-Scale Quantum (NISQ) era. However, designing effective quantum circuits (ansatzes) that are compatible with the limitations of current quantum hardware remains a significant challenge. In this work, we introduce a Reinforcement Learning (RL) agent that autonomously generates ansatzes for VQAs. The RL agent is trained on several optimization problems, including Maximum Cut, Maximum Clique, and Minimum Vertex Cover, across different graph topologies. Our results show that the agent is able to generate effective quantum circuits, with approximation ratios that favorably compare to commonly used ansatzes. Additionally, we identify a novel family of ansatzes, termed “Ryz-connected”, particularly effective on Maximum Cut problems. These findings highlight the potential of RL techniques in designing efficient quantum circuits for a broad class of applications in quantum computing
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
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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