AccScience Publishing / GHES / Online First / DOI: 10.36922/ghes.2148
REVIEW

From digital to quantum epidemiology: The Quantum Data Lake concept for big data related to viral infectious diseases

Olga Kolesnichenko1* Igor Nakonechniy2 Yuriy Kolesnichenko3
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1 Project Quantum Balchug IT (QBIT), Moscow, Russia
2 Department of Ecology and Environmental Technologies, Shipbuilding Educational and Scientific Institute, Admiral Makarov National University of Shipbuilding, Mykolaiv, Ukraine
3 Data Science Project Ultrasound Technologies, AI and Neural Networks (Uzgraph), Moscow, Russia
Submitted: 31 October 2023 | Accepted: 27 December 2023 | Published: 20 March 2024
© 2024 by the Author (s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Abstract

The development of quantum epidemiology represents the next anticipated phase in epidemiology transformation, driven by the emergence of new quantum technologies. Epidemiology is currently transitioning into the digital era and undergoing a paradigm shift from a data-driven to a value-driven strategy. Epidemiology data are characterized by uncertainty, multidimensionality, and disconnection, thereby correlating with the preferential quantum approach for data exposition, value creation, and modeling. Examples of such complex epidemiology data include the data on DNA viruses with associated symptoms and diseases. The Quantum Data Lake concept is proposed and consists of several layers and quantum tools, including Robson semantic triples, Quantum Universal Exchange Language, Hyperbolic Dirac Net, “quantum ribosome” structure, quantum random access memory, teleportation, Quantum Query Language, non-Hermitian gates, and tensor networks (e.g., matrix product state, projected entangled pair state, and multiscale entanglement renormalization ansatz [MERA]), alongside PT-symmetry properties. PT-symmetry can serve as an intuitive modeling tool, and PT-symmetry breaking can detect the hidden shift in the information that is permanently updated in the Data Lake. The computational output is presented as PT-symmetry gain/loss equilibrium breaking in the form of a complex number, i.e., two possible variants of epidemic modeling. For MERA, non-Hermiticity with spontaneous PT-symmetry breaking can theoretically appear as a violation of the entanglement monotonicity and local entanglement gain, leading to a non-reversible character of the coarse-graining transformation. The duality of PT-symmetry equilibrium breaking can be compared to, for example, the estimation of the best and worst scenarios simultaneously, or the gain of entanglement can display a significant correlation between some studied parameters embedded into the data. The fundamental difference between digital and quantum epidemiology is the implementation of quantum logic and reliance on a quantum theory.

Keywords
Big data
Quantum epidemiology
Quantum Data Lake
DNA viruses
Infectious diseases
PT-symmetry
Funding
None.
Conflict of interest
The authors declare they have no competing interests.
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