Impact-Driven Quantum Decomposition for Traffic Zone Partitioning: A Hybrid Gate-Model Framework

TL;DR

This paper introduces a hybrid quantum-classical framework for traffic zone partitioning that leverages impact-driven decomposition to improve scalability and coherence on current quantum hardware.

Contribution

It proposes an impact-driven hybrid quantum-classical optimization method that selectively applies quantum computation to influential subproblems in transportation network partitioning.

Findings

Impact-guided decomposition enhances convergence behavior.

The hybrid approach produces more coherent spatial partitions.

The method remains feasible within current quantum hardware constraints.

Abstract

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.