Hybrid Quantum-Classical Optimization Framework for Water Resource Management Using Topological Qubit Models
Efficient management of water resource systems requires solving complex optimization problems involving nonlinear dynamics, large datasets, and multiple operational constraints. Traditional optimization techniques such as linear programming, dynamic programming, and evolutionary algorithms have been widely applied in water resource management; however, these methods often face computational challenges when applied to large-scale hydrological systems. This paper proposes a hybrid quantum-classical optimization framework for water resource management using topological qubit models. The water allocation problem is formulated as a network-based optimization model and transformed into a quadratic unconstrained binary optimization (QUBO) problem suitable for quantum optimization algorithms. A hybrid computational approach combining classical hydrological simulation with quantum optimization is developed to improve computational efficiency. Simulation results demonstrate that the proposed model achieves faster convergence and lower operational cost compared with classical optimization methods. The study shows that quantum computing provides a promising computational approach for solving complex water resource optimization problems. Future work will focus on implementing the proposed model on real quantum hardware and integrating real-world hydrological datasets.
Keywords: Quantum Computing, Water Resource Management, Hydrological Modeling, Topological Qubits, Hybrid Quantum–Classical Optimization, QUBO.
