The field of routing and combinatorial optimization is witnessing significant advancements with the integration of machine learning and quantum computing techniques. Researchers are exploring novel approaches to tackle complex problems such as the Vehicle Routing Problem (VRP) and the Traveling Salesman Problem (TSP). The use of Graph Neural Networks (GNNs) and other learning-based methods is becoming increasingly popular for solving these problems. Additionally, the application of quantum algorithms to optimize robotic inspection trajectories and other industrial processes is showing promising results. Noteworthy papers in this area include 'Learning to Solve Multi-Objective Routing Problems on Multigraphs', which introduces two neural approaches to address multi-objective routing on multigraphs, demonstrating strong performance across various problems. Another notable paper, 'Learning to Segment for Vehicle Routing Problems', pioneers the formal study of the First-Segment-Then-Aggregate decomposition technique to accelerate iterative solvers, achieving up to 7x acceleration in state-of-the-art iterative solvers.