Speaker
Description
Under increasing environmental pressures, ecological corridor planning is vital for maintaining habitat connectivity. However, traditional approaches such as Minimum Cumulative Resistance (MCR) often generate singular, line-like paths that are highly vulnerable to local disruptions. In this study, we reformulate corridor design as a Quadratic Unconstrained Binary Optimization (QUBO) problem, explicitly incorporating volumetric awareness to enhance network resilience and obstacle avoidance. For large-scale scenarios, the proposed formulation is evaluated using the Compal GPU Annealer (CGA), Simulated Annealing (SA), and the D-Wave Quantum Annealer, while the simulator-based QAOA implementations using IBM Qiskit and NVIDIA CUDA-Q are employed for small-scale microscopic validation. Experimental results demonstrate that the proposed QUBO-based framework generates substantially wider and more spatially distributed ecological corridors than traditional methods while maintaining competitive ecological resistance. Furthermore, the proposed Corridor Efficiency (CE) and Robustness Gain Ratio (RGR) complement conventional resistance-based metrics by providing a more comprehensive evaluation of corridor quality. Ultimately, evaluations across these diverse solvers demonstrate that quantum and quantum-inspired annealing platforms successfully leverage the proposed QUBO model to achieve a highly favorable trade-off between ecological resistance and spatial redundancy.