Earth TwinOps: Geospatial AI and Quantum Optimization for Climate-Resilient Cities and Grids

Abstract: This study investigates an ‘Earth TwinOps’ paradigm that couples geospatial artificial intelligence with quantum optimization to support climate‑resilient cities and electricity grids. The conceptual contribution is a cross‑layer operating model in which urban digital twins, grid digital twins, and edge‑to‑cloud decision loops are governed as a single socio‑technical system, allowing hazard signals (heat, pluvial flooding, and air‑quality stress proxies) to be translated into actionable infrastructure and operations decisions. The methodological contribution is a replicable pipeline that (i) standardizes multi‑source geospatial and infrastructure indicators into a coherent feature space, (ii) learns interpretable risk and service‑criticality relationships, and (iii) formulates a multi‑objective hub‑selection and resource‑allocation problem as a Quadratic Unconstrained Binary Optimization (QUBO) instance for quantum‑inspired solvers. To satisfy end‑to‑end reproducibility in constrained data environments, the paper provides an embedded results section with a compact, clearly labeled illustrative dataset (eight Indian metropolitan nodes and their associated State/UT grid contexts) and demonstrates how the proposed metrics, models, and optimization workflow produce decision‑relevant outputs (priority ranking, hub placement, coverage trade‑offs, and sensitivity to governance weights). The findings indicate that Earth TwinOps is technically feasible using contemporary geospatial pipelines and hybrid quantum–classical optimization, but practical deployment depends on data governance, interoperability, and institutional capacity to operationalize accountability at the city–grid interface. Policy implications emphasize standards‑based data sharing, climate‑aligned public procurement, and phased capacity building that enables trustworthy automation from municipal operations to state‑level dispatch and investment planning. Keywords Digital twins; TwinOps; geospatial AI; climate resilience; electricity grids; quantum optimization; QUBO; edge computing; urban infrastructure governance.