A Conceptual Review of Quantum-Enhanced Resource Allocation Strategies for Sustainable Energy Management in Smart Cities

Abstract

Rapid urbanization and climate change demands require transformative strategies for energy management in smart cities. Traditional computational techniques encounter substantial difficulties in optimizing intricate, dynamic, and large-scale energy resource allocation issues characteristic of contemporary smart grids. This conceptual review examines the emerging paradigm of quantumenhanced resource allocation strategies for sustainable energy management in smart cities. This paper expands on the foundational framework of quantum computing applications for smart grid digital twins proposed by Lemo et al., synthesizing current research, developing an integrated conceptual model, and identifying critical research trajectories. We systematically analyze how quantum algorithms—specifically Quantum Annealing (QA), the Quantum Approximate Optimization Algorithm (QAOA), and the Variational Quantum Eigensolver (VQE)—can tackle significant optimization challenges in energy distribution, demand-response management, and renewable energy integration. Our analysis highlights the intersection of quantum computing and digital twin technology as a crucial facilitator for real-time, adaptive energy management systems. We propose a five-layer Quantum-Digital Twin (Q-DT) integration framework and examine its implications for sustainable urban development. The paper concludes by identifying critical research deficiencies, such as the necessity for standardized problem mapping, empirical benchmarking studies, and ethical governance frameworks, while delineating a prospective research agenda for achieving quantum advantage in smart city energy ecosystems.