Quantum Data Harmonization: Resolving Disparate Information Realms with AI-driven Data Science
Keywords:
data transformation, harmonization, quantum techniques, Hybrid quantum-classical algorithms, ETL techniques, encryption, multidimensionalAbstract
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.
References
Thompson NC, Ge S, Sherry YM. Building the algorithm commons: Who discovered the algorithms that underpin computing in the modern enterprise? Glob Strategy J. 2021 Feb; 11(1): 17–33.
Li Y, Hu X, Daggubati P, Langlois P. Understanding machine learning based clinical decision support systems. In AMIA Joint Summits on Translational Science Proceedings, American Medical Informatics Association. 2018 May; 2018: 433.
Harrow AW, Montanaro A. Quantum computational supremacy. Nature. 2017; 549(7671): 203–209.
Giovannetti V, Lloyd S, Maccone L. Quantum random access memory. Phys Rev Lett. 2008; 100(16): 160501.
Cong I. Quantum computing promises new insights, not just faster answers. Quanta Magazine. 2019; 8.
Benedetti M, Lloyd E, Sack S, Fiorentini M. Parameterized quantum circuits as machine learning models. Quantum Sci Technol. 2019; 4(4): 043001.
Havlicek V, Córcoles AD, Temme K, Harrow AW, Kandala A, Chow JM, Gambetta JM. Supervised learning with quantum-enhanced feature spaces. Nature. 2019 Mar 14; 567(7747): 209–12.
Kak S. Quantum neural computing. Adv Imaging Electron Phys. 1995; 94: 259–313. Elsevier.
Ryu M, Yaoyuneyong G, Foster C, Frénot S, Radzik T. Benchmarking data harmonisation techniques for environmental sustainability measures. Environ Model Softw. 2021; 140: 105040.
Paparo GD, Dunjko V, Metzler R, Martin-Delgado MA. Quantum speedup for active learning agents. Phys Rev X. 2012; 2(3): 031002.
van der Sar T, Blok MS, Szalay A, Tiwari V. Geometry of complex-valued parametrized quantum circuits. Machine Learning: Science and Technology. 2022.
Y-Liang H, Qiang L, Jun L, Jian W, Qi J. Quantum internet: Networking challenges in distributed quantum computing. IEEE J Sel Areas Commun. 2020; 38(3): 500–510.
