Quantum machine learning in spatial analysis: a paradigm shift in resource allocation and environmental modeling

Eric Vaz ()
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Eric Vaz: Toronto Metropolitan University

Letters in Spatial and Resource Sciences, 2024, vol. 17, issue 1, No 11, 13 pages

Abstract: Abstract This paper explores the ground-breaking approach that quantum computing may have in spatial and resource sciences, capitalizing on the advanced capabilities to revolutionize applications of quantum machine learning (QML). It explores the integration of quantum algorithms with traditional spatial econometric models, highlighting their potential impact on critical areas such as environmental and resource economics, urban planning, and regional development. The paper establishes a robust theoretical foundation for spatial quantum-enhanced machine learning, demonstrating how it can significantly refine the accuracy and efficiency of spatial-temporal data analysis. This enhancement is critical for effectively understanding and addressing challenges in environmental change, urbanization trends, and resource allocation. The innovation of spatial QML models may soon support managing complex, high-dimensional spatial datasets, which often pose significant challenges for classical computing methods. The paper illustrates the capacity of quantum computing to deliver more precise predictions and deeper insights into spatial dynamics, thereby acting as a transformative tool for policymakers and planners in city and regional planning. The findings highlight the practical applications of QML in spatial and resource sciences and pave the way for new research directions. This work is a seminal contribution to the emerging spatial quantum machine learning field, offering a new perspective and cost-efficient methodology for tackling complex spatial issues.

Keywords: Quantum Machine Learning; Quantum Computing; Spatial Quantum Machine Learning; Spatial analysis; Regional Science; Environmental Systems Dynamics; Spatial decision support (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s12076-024-00374-y

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