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Quantum AI Urbanism: Redefining the Future of Artificial Intelligence in Cities

Author

Listed:
  • Tan Yigitcanlar
  • Sk Tahsin Hossain
  • Abdulrazzaq Shaamala
  • Xinyue Ye

Abstract

The increasing complexity of urban environments necessitates computational advancements beyond the capabilities of classical systems. Traditional computing frameworks, grounded in a Newtonian worldview, face significant limitations in addressing critical urban challenges such as traffic optimization, energy distribution, and governance. Emerging technological paradigms seek to overcome these constraints by integrating quantum computing, quantum theory, and artificial intelligence (AI) to enhance urban intelligence and efficiency. One such paradigm is “Quantum AI Urbanism,” an interdisciplinary approach that leverages quantum principles such as superposition and entanglement to enable faster data processing, enhanced machine learning capabilities, and quantum-secure cryptography. By unlocking new computational potentials, this approach offers transformative possibilities for reimagining city infrastructure, governance, and citizen engagement. This article introduces the layered framework of quantum AI urbanism, outlining its core components, applications, and implications for city management. It highlights the transformative potential of quantum AI in reshaping urban infrastructure, governance, and citizen engagement while acknowledging the barriers that must be addressed for large-scale implementation. By establishing a foundational understanding of quantum AI urbanism, this study contributes to the ongoing discourse on the future of smart cities and provides insights into the pathways for integrating quantum computing and AI into urban systems.

Suggested Citation

  • Tan Yigitcanlar & Sk Tahsin Hossain & Abdulrazzaq Shaamala & Xinyue Ye, 2025. "Quantum AI Urbanism: Redefining the Future of Artificial Intelligence in Cities," Journal of Urban Technology, Taylor & Francis Journals, vol. 32(3), pages 213-226, May.
  • Handle: RePEc:taf:cjutxx:v:32:y:2025:i:3:p:213-226
    DOI: 10.1080/10630732.2025.2500826
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