Quantum Machine Learning Algorithms for Traffic Management in Big Cities

This research paper proposes a novel approach to traffic management optimization using a hybrid quantum-inspired machine learning method. We introduce a Hybrid Quantum and Bayesian Algorithm to compute Eigen values, facilitating the extraction of crucial traffic network characteristics. Leveraging AWS Hyperparameter Tuning SageMaker models, our methodology encompasses data preparation, feature engineering, model training, evaluation, deployment, and monitoring. By integrating SigOpt with AWS, we enhance model hyper parameter optimization, significantly improving efficiency and accuracy. We discuss the relevance of our work to current traffic management challenges, including scalability, expertise, and integration complexities. Our research demonstrates the potential of machine learning that is inspired by quantum mechanics to revolutionize traffic control systems for huge cities. The integration of quantum computing in intelligent traffic management systems aims to address congestion and pollution issues. Quantum algorithms offer efficient solutions for traffic optimization, signal timing, and flow prediction. Challenges include specialized hardware and expertise requirements. Future research focuses on developing more effective algorithms and addressing quantum error correction. The potential benefits include reduced congestion, fuel consumption, and air pollution. The field shows promise but requires further advancements to fully leverage quantum computing capabilities in traffic management.