Quantum computing-artificial intelligence synergy for adaptive urban morphogenesis: Modeling China’s hyper-growth cities under uncertainty

China’s hyper-growth cities face unprecedented uncertainty arising from intertwined economic, social, and environmental stresses that challenge traditional static approaches to urban planning. This article introduces the novel concept of adaptive urban morphogenesis—an evolving and dynamic configuration of urban structure and function—enabled by the synergistic integration of quantum computing (QC) and artificial intelligence (AI). We propose employing QC to manage inherent uncertainties and to deliver computationally feasible multi-objective combinatorial optimization solutions, such as dynamic resource allocation and resilient infrastructure design. In parallel, AI processes extensive urban datasets, extracts complex patterns, and generates real-time predictive insights. Together, these technologies establish a closed-loop feedback system: AI feeds QC simulations with predictions, while QC delivers the best adaptive solutions under uncertainty that subsequently inform AI models. This framework is designed to capture the rapid evolution of China’s urban economies and offers a paradigm shift toward forward-thinking, simulation-driven urban planning.
Agbonghae, A. (2024). Role of supply chain in maximizing revenue and profits. International Journal of Small Business and Entrepreneurship Research, 12(3), 79-100. https://doi.org/10.37745/ijsber.2013/vol12n379100
Ajagekar, A., Humble, T., & You, F. (2020). Quantum computing based hybrid solution strategies for large-scale discrete-continuous optimization problems. Computers and Chemical Engineering, 132, 106630. https://doi.org/10.1016/j.compchemeng.2019.106630
Bouhaddou, I. (2018). Plm for Supply Chain Optimization. London: Intechopen. https://doi.org/10.5772/intechopen.81272
Giraldo-Quintero, A., Pulido, J., Duque, J., & Sierra-Sosa, D. (2022). Using quantum computing to solve the maximal covering location problem. Computational Urban Science, 2(1), 43. https://doi.org/10.1007/s43762-022-00070-x
Hancke, G., Silva, B., & Hancke, G. (2012). The role of advanced sensing in smart cities. Sensors, 13(1), 393-425. https://doi.org/10.3390/s130100393
Havlíček, V., Córcoles, A., Temme, K., Harrow, A., Kandala, A., Chow, J., et al. (2019). Supervised learning with quantum-enhanced feature spaces. Nature, 567(7747), 209-212. https://doi.org/10.1038/s41586-019-0980-2
Hayashi, M. (2017). A Group Theoretic Approach to Quantum Information. https://doi.org/10.1007/978-3-319-45241-8
Huang, Y., Zuo, K., Yang, R., & Jin, C. (2023). Road traffic status discrimination method based on data fusion of multiple floating vehicles. In: Conference: 2023 3rd International Conference on Digital Signal and Computer Communications (DSCC 2023). p. 93. https://doi.org/10.1117/12.2685697
Hung, S. (2023). The fundamental theory of quantum computing, applications in practical fields, and future challenges. Applied and Computational Engineering, 18(1), 117-123.
Kitchin, R. (2014). The real-time city? big data and smart urbanism. GeoJournal, 79(1):1-14. https://doi.org/10.2139/ssrn.2289141
Kong, W., Wang, J., Han, Y., Wu, Y., Zhang, Y., Dou, M., et al. (2021). Origin pilot: A quantum operating system for effecient usage of quantum resources. Arxiv Preprint Arxiv: 2105.10730. https://doi.org/10.48550/arxiv.2105.10730
Li, W., Wu, M., & Mei, Q. (2012). The research of supply chain based on fourth party logistics optimization. Advanced Materials Research, 461, 393-397. https://doi.org/10.4028/www.scientific.net/amr.461.393
Li, Z., Shu, Z., & Wang, X. (2024). Exploring competitive advantages in enterprise supply chains: A case study of jd’s predictive and logistics links. SHS Web of Conferences, 181, 03014. https://doi.org/10.1051/shsconf/202418103014
Manika, S., Karalidis, K., & Gospodini, A. (2021). Mechanism for the optimal location of a business as a lever for the development of the economic strength and resilience of a city. Urban Science, 5(4), 70. https://doi.org/10.3390/urbansci5040070
Mathews, R., Kundu, A., Chawla, P., Sebastian, T., Palanichamy, R., & Pai, M. (2023). Morphology of Delhi National Capital Region’s Economic Geography and its Implications for Planning. Available from: https://www.wri.org/research/morphology-delhi-nation-capital-regions -Conomic- Geography -nd-Ots-impleics-Planening
Narraway, C., Davis, O., Lowell, S., Lythgoe, K., Turner, J., & Marshall, S. (2019). Biotic analogies for self-organising cities. Environment and Planning B Urban Analytics and City Science, 47(2), 268-286. https://doi.org/10.1177/2399808319882730
Resta, B., Gaiardelli, P., Cavalieri, S., & Dotti, S. (2017). Enhancing the design and management of the product-service system supply chain: An application to the automotive sector. Service Science, 9(4), 302-314. https://doi.org/10.1287/serv.2017.0193
Wang, Y., & Liang, X. (2025). Application of reinforcement learning methods combining graph neural networks and self-attention mechanisms in supply chain route optimization. Sensors, 25(3), 955. https://doi.org/10.3390/s25030955
Wohl, S. (2018). Complex adaptive systems and urban morphogenesis. Analyzing and designing urban fabric informed by CAS dynamics. A+BE Architecture and the Built Environment, 8(10), 1-238. https://doi.org/10.7480/abe.2018.10.2397