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ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2022
License: CC BY
Data sources: ZENODO
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Dataset for "Strong dispersion property for the quantum walk on the hypercube"

Authors: Martins Kokainis; Krišjānis Prūsis; Jevgenijs Vihrovs; Vyacheslavs Kashcheyevs; Andris Ambainis;

Dataset for "Strong dispersion property for the quantum walk on the hypercube"

Abstract

Dataset for Figure 1 and Figure 2 presented in "Strong dispersion property for the quantum walk on the hypercube" (preprint available at arxiv.org/abs/2201.11735). The rows of data.csv file contain the calculated quantities related to the quantum walk on the hypercube: the first row is the maximum probability of a vertex during a walk on the 50-dimensional hypercube; the second row contains the number of steps to minimize the aforementioned probability for various n; the third row is the maximum probability of a vertex after approximately 0.849n steps, for various n; the fourth row is the probability of the walker to be at the 0n vertex (n=50) during a walk on the 50-dimensional hypercube. The rows of aux.csv contain auxiliary data needed to plot the figures: the first row contains the integers 0 to 199 and corresponds to the variable 't' in Figure 1; the second row contains the integers from 1 to 200 and corresponds to the variable 'n' in Figure 2; the third row is the value of the linear function -0.754 + 0.849*n, depicted in the upper panel of Figure 2; the fourth row is the value of the function 5*1.93^(-n), depicted in the lower panel of Figure 2. To generate the figures, the following Matlab commands may be used (after loading the CSV files into variables aux and data): figure; scatter(aux(1,:),data(1,:),15); set(gca,'YScale','log') % F1: upper figure; scatter(aux(1,1:2:end),data(1,1:2:end),15); hold on; scatter(aux(1,:),data(4,:),15,'s'); hold off; set(gca,'YScale','log') % F1: lower figure; scatter(aux(2,:),data(2,:),15); hold on; plot(aux(2,:),aux(3,:)); xlim([0,100]);hold off; %F2: upper figure; scatter(aux(2,:),data(3,:),15);set(gca,'YScale','log'); hold on; semilogy(aux(2,:), aux(4,:));hold off; xlim([0,100]); %F2: lower

This work has been supported by Latvian Council of Science (project no. lzp-2018/1-0173) and the ERDF project 1.1.1.5/18/A/020.

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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