Traffic congestion remains a significant challenge in urban environments, necessitating innovative solutions for efficient traffic management. This paper explores the use of quantum annealing for traffic optimization, building on the pioneering work of Volkswagen and D-Wave. In 2017, a collaborative team demonstrated the potential of quantum annealing to optimize traffic flow by addressing a complex 418-car Quadratic Unconstrained Binary Optimization problem. Using a hybrid classical and quantum computing approach, they achieved notable improvements in traffic distribution. In this paper, we introduce a novel approach: Quantum Congestion-Focused Traffic Optimization (Q-CFTO). Our approach is designed to enhance traffic management optimization by breaking down the problem of optimizing traffic flow into smaller and more manageable subproblems rather than dealing with a single large optimization challenge. This decomposition facilitates the handling of the problem's complexity as embedding a large problem into a Quantum Processing Unit can be difficult. We successfully embedded the problem directly into the D-Wave Advantage Quantum Processing Unit with the "Pegasus" topology, significantly accelerating the solution process. The results demonstrate that our approach is both faster and more effective in real-world scenarios.
