Impact-Driven Quantum Decomposition for Traffic Zone Partitioning: A Hybrid Gate-Model Framework
For researchers in intelligent transportation systems, this work provides a practical pathway to apply current quantum hardware to large-scale optimization problems, albeit with incremental improvements over classical methods.
The paper tackles the computationally challenging problem of partitioning transportation networks into balanced and spatially coherent traffic zones. It proposes a hybrid quantum-classical optimization framework that uses impact-driven decomposition to assign quantum computation to influential subproblems, achieving improved convergence and spatial coherence compared to classical refinement, though not outperforming direct quantum optimization.
Partitioning transportation networks into balanced and spatially coherent traffic zones is a fundamental yet computationally challenging task in intelligent transportation systems. The resulting optimization problem exhibits dense interactions among decision variables and can be formulated as a Quadratic Unconstrained Binary Optimization (QUBO) model. While quantum optimization naturally aligns with such quadratic energy representations, current noisy intermediate-scale quantum hardware imposes limitations on problem size, connectivity, and circuit reliability. This paper proposes an impact-driven hybrid quantum--classical optimization framework for traffic zone partitioning that bridges transportation-scale optimization models and practical gate-based quantum processors. Instead of static geographic decomposition, the method estimates the energy impact of decision variables and selectively assigns quantum computation to influential subproblems while a classical coordination loop maintains global feasibility. The framework is implemented using the Iskay optimizer and evaluated on the IBM Quantum System One backend. Experiments compare direct quantum optimization, classical iterative SubQUBO refinement, and the proposed hybrid approach. Results show that impact-guided decomposition improves convergence behavior and produces more coherent spatial partitions relative to classical refinement, while remaining consistent with hardware constraints. Although the hybrid method does not outperform the best direct quantum solution, it demonstrates a practical pathway toward scalable hybrid optimization for transportation applications under current quantum hardware conditions.