A Study on Quantum Computer Technology in the Era of Digital Transformation

Authors

  • Tiara Putri Author

Abstract

The era of digital transformation has significantly increased the demand for advanced computing technologies capable of processing large volumes of data and solving complex problems efficiently. Quantum computer technology has emerged as a promising innovation that extends beyond the limitations of classical computing by leveraging the principles of quantum mechanics. This study aims to examine quantum computer technology and analyze its role and relevance in the era of digital transformation. The research method employed is a literature review, drawing on scientific journals, reference books, and recent research publications related to quantum computing and digital transformation. The findings indicate that quantum computer technology has the potential to support digital transformation initiatives by enhancing data processing performance, optimization capabilities, cybersecurity, and intelligent decision-making systems. However, the widespread adoption of quantum computing remains constrained by technological maturity, system scalability, infrastructure readiness, and human resource challenges. Therefore, continuous research and strategic development are required to ensure that quantum computer technology can effectively contribute to digital transformation in future information technology ecosystems.

References

Lee, J., Park, S., & Kim, H. (2022). U-Net based segmentation and deep feature extraction for fracture classification on arm X-ray. IEEE Transactions on Medical Imaging, 41(5), 1200–1210. https://doi.org/10.1109/TMI.2022.314159

Patel, D., & Kar, S. (2021). Optimized watershed segmentation and PCA-based feature reduction for vertebra bone images. Computers in Biology and Medicine, 130, Article 104217.

Rahman, M. S., & Chowdhury, M. S. (2023). Hybrid region-growing and edge refinement with LBP features for micro-fracture detection in femur X-rays. Medical Image Analysis, 85, 102–113. https://doi.org/10.1016/j.media.2023.102113

Smith, L., & Lee, D. (2021). Morphology-enhanced Canny edge detection and texture analysis of wrist radiographs. Journal of Digital Imaging, 34(2), 450–460. https://doi.org/10.1007/s10278-020-00415-6

Veza, O., Kom, S., Kom, M., Agustini, S., Kom, S., & Kom, M. (2025). PENGENALAN DASAR PENGOLAHAN CITRA. Cendikia Mulia Mandiri.

Arifin, N. Y., Kom, S., Kom, M., Tyas, S. S., Kom, S., Sulistiani, H., ... & Kom, M. (2022). Analisa Perancangan Sistem Informasi. Cendikia Mulia Mandiri.

Setyabudhi, C. A. L., Marwan, S., Yuli Setiawannie, S. T., Surya Indrawan, S. T., Nita Marikena, S. T., Roudlotul, B. A., ... & ST, M. L. (2025). SUSTAINABLE SUPPLY CHAIN. Cendikia Mulia Mandiri.

Published

2026-02-05

How to Cite

A Study on Quantum Computer Technology in the Era of Digital Transformation. (2026). Root Indexing, 2(01). https://worldscientificindex.com/index.php/winx/article/view/60