Quantum Optimization for Efficient Traffic Flow Management

Quantum computing holds promise for tackling complex optimization problems, yet its practical applications remain a subject of active research. In this thesis, we investigate the feasibility and effectiveness of the Quantum Approximate Optimization Algorithm (QAOA) in real-life applications, focusing on traffic flow optimization. Our objective is to evaluate how well QAOA performs with regard to both classical optimization techniques and alternative quantum techniques, particularly quantum annealers.

We first formulate the traffic flow optimization problem as a binary quadratic program and implement QAOA to find optimal solutions. Through simulations, we demonstrate the capability of QAOA to allocate vehicles to routes, reducing traffic congestion efficiently. Furthermore, we compare the performance of QAOA with Quantum Annealers and classical tabu search algorithm, revealing insights into their computational efficiency and scalability.

Our findings indicate that while QAOA exhibits slower execution times compared to classical methods, its performance holds promise for handling larger datasets. Additionally, QAOA and Quantum Annealers are almost comparable in terms of speed. However, challenges such as hardware limitations and accessibility to quantum resources hinder its widespread adoption and further development.

Looking ahead, we propose future avenues of research, including the integration of machine learning techniques to address scalability issues and overcome accessibility constraints. By exploring these directions, we aim to advance the practical applicability of quantum algorithms for addressing real-world optimization challenges.