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

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  • Li, Nianqiao
  • Yan, Fei
  • Hirota, Kaoru

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.

Suggested Citation

  • Li, Nianqiao & Yan, Fei & Hirota, Kaoru, 2022. "Quantum data visualization: A quantum computing framework for enhancing visual analysis of data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    • Handle: RePEc:eee:phsmap:v:599:y:2022:i:c:s0378437122003466
    DOI: 10.1016/j.physa.2022.127476
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    References listed on IDEAS

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    1. Jacob Biamonte & Peter Wittek & Nicola Pancotti & Patrick Rebentrost & Nathan Wiebe & Seth Lloyd, 2017. "Quantum machine learning," Nature, Nature, vol. 549(7671), pages 195-202, September.
    2. E. Knill & R. Laflamme & R. Martinez & C.-H. Tseng, 2000. "An algorithmic benchmark for quantum information processing," Nature, Nature, vol. 404(6776), pages 368-370, March.
    3. Meen Chul Kim & Yongjun Zhu & Chaomei Chen, 2016. "How are they different? A quantitative domain comparison of information visualization and data visualization (2000–2014)," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(1), pages 123-165, April.
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