STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Advances in Shell Programming

Quantum Data Harmonization: Resolving Disparate Information Realms with AI-driven Data Science

  1. Ushaa Eswaran
  2. Vivek Eswaran
  3. Keerthna Murali
  4. Vishal Eswaran

Abstract

The proliferation of heterogeneous data structures across fragmented information systems hampers insights derivation necessitating harmonization methodologies reconciling multidimensional datasets for collective analytics. This article reviews quantum techniques leveraging qubit superposition for probabilistic data transformations as a novel data harmonization paradigm tailored for varied schemas commonly confronting interdisciplinary analyses spanning healthcare, finance and environmental domains. Hybrid quantum-classical algorithms and AI-based automation workflows hold promise coordinating dispersed data pools through learned mappings at scale. Multi-dimensional test cases benchmark quantum harmonization protocols against traditional ETL techniques revealing enhanced context preservation and lowered distortion losses even for nuanced domains like genomic encodings or molecular dynamics. Information-theoretic metrics quantify residual entanglement between datasets post synthesis. However, quantum entropy measures pose interpretability challenges requiring dimensionality reductions visualizing the fusion fidelity. Analysis also reveals hybrid encryption protocols for securing quantum data transfers safeguarding sensitivity. Overall, this research charts an expansive future landscape where ubiquitous quantum data harmonization resolves information fragmentation challenges advancing organization-wide analytics cohesion. Key quantum techniques explored include qubit encoding schemes leveraging quantum RAM, tunable parameterized circuit designs, and automation through quantum machine learning.

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