Authors:
Varun Kumar Nomula

Addresses:
Department of AI/ML Engineer Analytics, Georgia Institute of Technology, Atlanta, Georgia, United States of America.                      

Abstract:

Quantum computing is an emerging paradigm that will soon transform the face of several industries, such as health care. This paper focuses on applying quantum computing in optimizing analytics on healthcare data, especially amidst big data challenges in the digital age. A health system generates tremendous amounts of complex datasets requiring advanced tools to draw meaningful insights from such data. Traditionally, most of these methods fail to meet the demands for real-time processing, predictiveness, and data security. Taking leaps with extraordinary computing power from quantum computing, health analytics can be taken towards leaps of revolutionary precision in medicine, resource allocation, and early detection of diseases. This paper explains how quantum algorithms synergistically find applications in health data. Areas of applicability, technological constraints, and issues related to ethics form other emphases. This, in turn, provides a comprehensive literature review, a method for quantum-enhanced analytics, and an analysis of the results of simulated quantum experiments, which points toward this integration as transformative. Furthermore, an architectural framework for quantum-driven healthcare systems, as depicted in the study, is postulated. The results are illustrated, including figures and corresponding tables for descriptive performance benefits and challenges. Implications to clinical workflows, patient outcomes, and the larger health ecosystem are demonstrated in the discussion. The conclusion presents the limitations and avenues for further research focusing on interdisciplinarity with quantum computing for the best healthcare.

Keywords: Quantum Computing; Healthcare Analytics; Big Data; Predictive Modeling; Precision Medicine; Tremendous Amounts; Synergistic Ways; Technological Constraints; Architectural Framework.

Received on: 23/04/2024, Revised on: 12/06/2024, Accepted on: 21/08/2024, Published on: 03/12/2024

DOI: 10.69888/FTSCL.2024.000278

FMDB Transactions on Sustainable Computer Letters, 2024 Vol. 2 No. 4, Pages: 186-194

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