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ZENODO
Article . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Article . 2026
License: CC BY
Data sources: Datacite
ZENODO
Article . 2026
License: CC BY
Data sources: Datacite
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QUANTUM COMPUTING FOR TRAVEL OPTIMIZATION IN THE TOURISM SECTOR: A BIBLIOMETRIC ANALYSIS

Authors: Idris Oyewale Oyelakin, Sandeep Paatlan, Ajit Kumar Singh, Anil Kumar, Jayati Ranga, Asnidar Hanim Yusuf;

QUANTUM COMPUTING FOR TRAVEL OPTIMIZATION IN THE TOURISM SECTOR: A BIBLIOMETRIC ANALYSIS

Abstract

The study examines the usage of quantum computing in the travel industry: Looking at different ways of using quantum computing to improve crucial optimisation challenges, including route planning, pricing and resource allocation. The research methodology involves an in-depth review of the literature in the field of quantum computing for optimisation tasks. The study will highlight the opinions and advantages of quantum computing in the travel industry. Key aspects to be explored include the potential acceleration of route planning, enhanced pricing models with quantum machine learning, and resource optimisation for tourism-related services. Bibliometric analysis is conducted to understand the trends and work done in Quantum computing. In the last 5 years of the Scopus-indexed database, 14457 articles have been used, and bibliometric analysis has been conducted to understand the country's, universities', institutions', and authors' contributions in the field.

Keywords

Quantum, Computing, Travel, Tourism, Machine Learning, Industrial Growth, Product Innovation.

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green