Quantum data visualization: A quantum computing framework for enhancing visual analysis of data

Nianqiao Li, Fei Yan and Kaoru Hirota

Physica A: Statistical Mechanics and its Applications, 2022, vol. 599, issue C

Abstract: Data visualization assists in the evaluation and analysis of large graphical data sets. In this study, quantum data visualization (QDV) is proposed as the first attempt to aid users in more effectively comprehending data via quantum mechanical effects. The QDV framework is introduced to fully illustrate the steps necessary for implementing this novel concept. To provide a more intuitive visual representation for data analysis, the quantum rendering module is established to associate quantum data with color gradient information based on continuous geometric primitives in QDV tools. As an application, 2D and 3D QDV tools are designed and applied, including quantum circuit diagrams for preparing pie charts, scatter plots, bar graphs, and function curves. Moreover, the interaction mechanisms used to perform scaling, numerical calculations, and position swapping operations on geometric primitives are discussed and demonstrated. In analyzing QDV efficiency, evaluation metrics, such as cost, delay, width, and auxiliary qubit quantities, were calculated for key quantum processes, to assess framework performance and illustrate corresponding advantages over conventional data visualization models.

Keywords: Quantum computing; Data visualization; Interactive techniques; Quantum data; Visual analysis (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:599:y:2022:i:c:s0378437122003466

DOI: 10.1016/j.physa.2022.127476

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